presentation.bib

%----- 2025 -----%

@PRESENTATION{bruer2025GSCSsmc,
  author = {Grant Bruer and Abhinav Prakash Gahlot and Felix Herrmann and Edmond Chow},
  title = {Seismic monitoring of CO2 using ensemble Kalman filtering},
  booktitle = {2025 Georgia Scientific Computing Symposium},
  year = {2025},
  month = {02},
  keywords = {GSCS, SLIM, CCS, uncertainty quantification, EnKF, GCS, monitoring, fluid flow, imaging, RTM},
  note = {(GSCS, Gatech)},
  url = {https://slim.gatech.edu/Publications/Public/Posters/GSCS/2025/bruer2025GSCSsmc/2025GSCS_poster.pdf}
}

%----- 2024 -----%

@PRESENTATION{orozco2024ML4SEISMICmev,
  author = {Rafael Orozco and Huseyin Tuna Erdinc and Thales Souza and Yunlin Zeng and Ziyi Yin and Felix J. Herrmann},
  title = {Machine-learning enabled velocity-model building with uncertainty quantification},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2024},
  month = {11},
  abstract = {Accurately characterizing subsurface properties is crucial for a wide range of geophysical applications, from hydrocarbon exploration to monitoring of CO2 sequestration projects. Traditional characterization methods such as Full-Waveform Inversion (FWI) represent powerful tools but often struggle with the inherent complexities of the inverse problem, including noise, limited bandwidth and aperture of data, limited azimuth and computational constraints. To address these challenges, we propose a scalable methodology that integrates generative modeling with physics-informed summary statistics, making it suitable for complicated imaging problems potentially including field datasets. Our approach leverages the power of conditional diffusion networks, and methodologically incorporates physics in the form of summary statistics, allowing for the computationally efficient generation of Bayesian posterior samples that offer an useful assessment of uncertainty of the inferred migration-velocity models. To validate our approach, we introduce a battery of tests that measure the quality of the image estimates as well as the quality of the inferred uncertainties. With modern synthetic datasets, we maximally leverage the advantages of using subsurface-offset Common Image Gathers (CIGs) as the conditioning observable. Next, we tackle the challenging SEAM salt model that requires incorporating salt flooding into our approach based on the iterative refinements of ASPIRE — Amortized posteriors with Summaries that are Physics-based and Iteratively REfined.},
  keywords = {ML4SEISMIC, SLIM, FWI, RTM, imaging, WISE, diffusion models, Bayesian inference, amortized variational inference, uncertainty quantification, deep learning, inverse problems, summary statistics},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/orozco2024ML4SEISMICmev}
}

@PRESENTATION{rex2024ML4SEISMICfca,
  author = {Richard Rex and Yunlin Zeng and Ziyi Yin and Rafael Orozco and Felix J. Herrmann},
  title = {FNO-charged ASPIRE},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2024},
  month = {11},
  abstract = {During this talk, we will demonstrate how extended re-migrations—i.e, formation of subsurface-offset Common-Image Gathers (CIGs) for a new velocity model, can be avoided altogether by training Fourier Neural Operators during training of ASPIRE — Amortized posteriors with Summaries that are Physics-based and Iteratively REfined. In this approach, FNOs are trained as surrogates capable of mapping CIGs for one migration-velocity model to the other. The approach is computationally feasible because it uses the same training set as used during ASPIRE. As a result, additional training costs are small and the inference costs are reduced by a factor equal to the number of ASPIRE refinements.},
  keywords = {ML4SEISMIC, SLIM, two-phase flow, kronecker product, hierarchical tucker tensor, FNO, deep learning, inverse problems},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/rex2024ML4SEISMICfca}
}

@PRESENTATION{zeng2024ML4SEISMICefv,
  author = {Yunlin Zeng and Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann},
  title = {Enhancing Full-Waveform Variational Inference through Stochastic Resampling},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2024},
  month = {11},
  abstract = {Recent developments in simulation-based inference, like the full-waveform variational inference via subsurface extensions (WISE), enable rapid online estimation of subsurface velocities by leveraging pre-trained models. To achieve this, WISE employs subsurface-offset common image gathers to convert shot data into physics-informed summary statistics. While common image gathers effectively retain critical information even when initial velocity estimates are inaccurate, WISE’s performance still depends on the assumption that the initial migration-velocity model is a single 1D velocity model. In this work, we present experiments using both 1D and 2D velocity models and develop a stochastic resampling method to generate variations of initial migration-velocity models. This technique allows us to systematically infer alternative velocity models that are consistent with the observed data, while enhancing the posterior sample quality and reducing dependency on the initial velocity model compared to the standard WISE approach.},
  keywords = {FWI, RTM, imaging, WISE, CIG, conditional normalizing flows, Bayesian inference, amortized variational inference, uncertainty quantification, deep learning, inverse problems, summary statistics, MVA},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/zeng2024ML4SEISMICefv},
}

@PRESENTATION{bhar2024ML4SEISMICshs,
  author = {Ipsita Bhar and Abhinav Prakash Gahlot and Felix J. Herrmann},
  title = {Sensitivity of Horizontal shear waves on Geological Carbon Storage},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2024},
  month = {11},
  abstract = {With the growing focus on Carbon Capture and Storage (CCS) activities, the significance of shear wave velocity (Vs) in monitoring CO2 is becoming increasingly important. Shear waves are instrumental in detecting CO2 leakage, evaluating CCS-induced seismicity, and identifying caprock failure. According to Biot-Gassmann rock physics models, changes in seismic velocity are expected during CO2 injection. This work specifically focuses on the horizontal component of shear wave velocity (Vsh). Shear (Sh) waves are more sensitive to density variations than P-wave or Sv wave velocities, leading us to anticipate changes in Sh velocity as CO2 saturation increases. While P-wave velocity tends to decrease with supercritical CO2 injection, the investigation of its effects on Sh waves presents a particularly compelling area of study. Therefore, we simulated the wave equation incorporating both P-wave and Sh wave velocities to better understand their impacts on imaging and CO2 injection processes.},
  keywords = {ML4SEISMIC, SLIM, SH wave, FWI, RTM, imaging, inverse problems},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/bhar2024ML4SEISMICshs}
}

@PRESENTATION{li2024ML4SEISMICrps,
  author = {Haoyun Li and Shiqin Zeng and Abhinav Prakash Gahlot and Felix J. Herrmann},
  title = {Reconstructing Permeability and Saturation in Reservoir Simulation Using Diffusion PDE Models},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2024},
  month = {11},
  abstract = {This study explores the application of a diffusion partial differential equation (PDE) model for reservoir simulation, particularly aimed at reconstructing permeability and saturation fields within a saline aquifer. Focusing on pairs of input permeability and output saturation, the model is trained to capture the underlying dynamics governing fluid flow in porous media. Post-training, the model is capable of inferring or recovering the complete permeability and saturation distributions when provided with limited vertical pixel data of permeability and saturation. This approach offers a novel pathway for enhancing the resolution of subsurface characteristics, contributing to more accurate predictions in reservoir engineering and carbon storage simulations.},
  keywords = {ML4SEISMIC, SLIM, two-phase flow, diffusion models, Bayesian inference, uncertainty quantification, deep learning, inverse problems, summary statistics, augmentation},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/li2024ML4SEISMICrps}
}

@PRESENTATION{zeng2024ML4SEISMICiic,
  author = {Shiqin Zeng and Rafael Orozco and Huseyin Tuna Erdinc and Felix J. Herrmann},
  title = {Image Impeccable Challenge: An Effective Machine Learning Denoising Method for 3D Seismic Volumes},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2024},
  month = {11},
  abstract = {Seismic denoising is essential for enhancing the clarity, accuracy, and reliability of seismic data. Traditional seismic denoising methods, while effective for specific types of noise, often rely on well-established mathematical techniques that can be time-consuming, require manual tuning, and struggle with more complex noise patterns. Leveraging the 500 paired synthetic seismic datasets provided by the Think Onward community, we incorporate a 3D U-Net deep learning model with residual blocks and spatial attention to capture both local and global features for the seismic denoising task. During training, we apply the Laplacian operator to preserve edge details, followed by the Structural Similarity Index Measure (SSIM) loss to fine-tune the model, effectively removing concurrent noise and recovering the original seismic information. The resulting individual model achieves an SSIM of 0.99 compared to the ground truth seismic data. Additionally, we implement Langevin dynamics and Equivariant Bootstrapping techniques to estimate uncertainty during the training and inference phases, further improving the robustness of the denoising process.},
  keywords = {ML4SEISMIC, SLIM, GCS, CCS, JRM, classification, CAM, explainability, imaging, uncertainty quantification, inverse problems},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/zeng2024ML4SEISMICiic}
}

@PRESENTATION{zeng2024ML4SEISMICepu,
  author = {Shiqin Zeng and Huseyin Tuna Erdinc and Ziyi Yin and Abhinav Prakash Gahlot and Felix J. Herrmann},
  title = {Enhancing Performance with Uncertainty Estimation in Geological Carbon Storage Leakage Detection from Time-Lapse Seismic Data},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2024},
  month = {11},
  abstract = {Ensuring CO~2~ non-leakage is a critical aspect of Geological Carbon Storage (GCS). While previous approaches that develop deep neural networks demonstrate promising automatic leakage detection and potential cost reduction in dataset collection from time-lapse seismic images, they face challenges, such as a limited ability to reduce false alarms in CO~2~ leakage instances and a lack of uncertainty analysis in detection results. This paper introduced a framework aimed at enhancing the deep neural network model's ability to detect GCS leakage risk through a multi-criteria decision-making (MCDM)-based ensemble algorithm. The proposed method can improve the detection ability of leakage cases while accurately distinguishing them from non-leakage instances. Furthermore, the proposed uncertainty analysis method utilizing Monte Carlo (MC) dropout technique efficiently identifies misclassified non-leakage cases and categorizes them as undetermined for further investigation. This comprehensive approach enhances both the reliability and performance of the model in detecting GCS leakage risks.},
  keywords = {ML4SEISMIC, SLIM, GCS, CCS, JRM, classification, CAM, explainability, imaging, uncertainty quantification, inverse problems},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/zeng2024ML4SEISMICepu}
}

@PRESENTATION{bruer2024ML4SEISMICsmp,
  author = {Grant Bruer and Abhinav Prakash Gahlot and Edmond Chow and Felix J. Herrmann},
  title = {Seismic monitoring of CO2 plume dynamics using ensemble Kalman filtering},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2024},
  month = {11},
  abstract = {Monitoring CO~2~ injected and stored in subsurface reservoirs is critical for avoiding failure scenarios and enables real-time optimization of CO~2~ injection rates. Sequential Bayesian data assimilation (DA) is a statistical method for combining information over time from multiple sources to estimate a hidden state, such as the spread of the subsurface CO~2~ plume. An example of scalable and efficient sequential Bayesian DA is the ensemble Kalman filter (EnKF). We improve upon existing DA literature in the seismic-CO~2~ monitoring domain by applying this scalable DA algorithm to a high-dimensional CO~2~ reservoir using two-phase flow dynamics and time-lapse full waveform seismic data with a realistic surface-seismic survey design. We show more accurate estimates of the CO~2~ saturation field using the EnKF compared to using either the seismic data or the fluid physics alone. Furthermore, we test a range of values for the EnKF hyperparameters and give guidance on their selection for seismic CO~2~ reservoir monitoring.},
  keywords = {ML4SEISMIC, SLIM, two-phase flow,  FWI, RTM, imaging, ensemble Kalman filter, data assimilation, Bayesian inference, uncertainty quantification, inverse problems},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/bruer2024ML4SEISMICsmp}
}

@PRESENTATION{park2024ML4SEISMICpbi,
  author = {Jeongjin Park and Huseyin Tuna Erdinc and Haoyun Li and Richard Rex and Nisha Chandramoorthy and Felix J. Herrmann},
  title = {Physical Bayesian Inference for Two-Phase Flow Problems},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2024},
  month = {11},
  abstract = {Previous research on surrogate modeling of multiphase flow systems has shown that even models with low generalization error in forward predictions can generate posterior estimates that are out of distribution and physically unrealistic. To address this, we propose a regularization method that leverages the Fisher Information Matrix (FIM) to guide the training process. By integrating the FIM into a differentiable optimization framework, we aim to improve the reliability of surrogate models, such as Fourier Neural Operators (FNO), for both forward predictions and posterior inference. Our experiments on benchmark problems, including the Lorenz-63 system and Navier-Stokes equations, demonstrate that our approach significantly enhances physical consistency throughout time evolution, keeping predictions within the correct spatial distribution. Looking ahead, we plan to extend our framework to more complex applications, such as Geological Carbon Storage, with an emphasis on scaling FIM computations for high-dimensional problems.},
  keywords = {ML4SEISMIC, SLIM, two-phase flow, FNO, Fisher Information Matrix, Bayesian inference, uncertainty quantification, deep learning, inverse problems},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/park2024ML4SEISMICpbi}
}

@PRESENTATION{herrmann2024ML4SEISMICdtg,
  author = {Abhinav Prakash Gahlot and Haoyun Li and Ziyi Yin and Rafael Orozco and Felix J. Herrmann},
  title = {A Digital Twin for Geological Carbon Storage with Controlled Injectivity},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2024},
  month = {11},
  abstract = {We present an uncertainty-aware Digital Twin (DT) for geologic carbon storage (GCS), capable of handling multimodal time-lapse data and controlling CO~2~ injectivity to mitigate reservoir fracturing risks. In GCS, DT represents virtual replicas of subsurface systems that incorporate real-time data and advanced generative Artificial Intelligence (genAI) techniques, including neural posterior density estimation via simulation-based inference and sequential Bayesian inference. These methods enable the effective monitoring and control of CO~2~ storage projects, addressing challenges such as subsurface complexity, operational optimization, and risk mitigation. By integrating diverse monitoring data, e.g., geophysical well observations and imaged seismic, DT can bridge the gaps between seemingly distinct fields like geophysics and reservoir engineering. In addition, the recent advancements in genAI also facilitate DT with principled uncertainty quantification. Through recursive training and inference, DT utilizes simulated current state samples, e.g., CO~2~ saturation, paired with corresponding geophysical field observations to train its neural networks and enable posterior sampling upon receiving new field data. However, it lacks decision-making and control capabilities, which is necessary for full DT functionality. This study aims to demonstrate how DT can inform decision-making processes to prevent risks such as cap rock fracturing during CO~2~ storage operations.},
  keywords = {ML4SEISMIC, SLIM, FWI, RTM, imaging, conditional normalizing flows, rock physics, data assimilation, Bayesian inference, amortized variational inference, uncertainty quantification, deep learning, inverse problems, summary statistics, augmentation},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/herrmann2024ML4SEISMICdtg}
}

@PRESENTATION{rex2024ML4SEISMIChtc,
  author = {Richard Rex and Srikanth Avasarala and Thomas Grady and Felix J. Herrmann},
  title = {Tucker Compression for Scalable Operator Learning in Large-Scale Parametric PDE Models},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2024},
  month = {11},
  abstract = {Simulating two-phase flow via PDEs is computationally expensive due to the inversion of large, ill-conditioned matrices. To accelerate these computations, we reformulate Hierarchical Tucker Tensor (HTT) decompositions into Kronecker products, enabling scalable Fourier Neural Operators (FNOs) for CO2 saturation predictions in subsurface environments. This reformulation allows efficient scaling across multiple GPUs while maintaining a large number of modes. Building on our existing matrix-free abstraction library, we extend its capabilities to support distributed tensor operators. The extended library is auto-differentiable, with customized AD rules for training complex networks. We demonstrate the performance and scalability of our approach by evaluating FNO simulations against traditional PDE solvers for predicting time-varying CO2 saturations from permeability models in large-scale subsurface environments.},
  keywords = {ML4SEISMIC, SLIM, two-phase flow, kronecker product, hierarchical tucker tensor, FNO, deep learning, inverse problems},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/rex2024ML4SEISMIChtc}
}

@PRESENTATION{deng2024ML4SEISMICpjr,
  author = {Zijun Deng and Rafael Orozco and Abhinav Prakash Gahlot and Felix J. Herrmann},
  title = {Probabilistic Joint Recovery Method for CO2 plume monitoring},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2024},
  month = {11},
  abstract = {Accurately predicting fluid flow patterns in Carbon Capture and Storage (CCS) is a challenging task, particularly due to uncertainties in CO~2~ plume dynamics and reservoir properties. While previous deterministic methods such as the Joint Recovery Method (JRM) have provided valuable insights, their effectiveness is limited as tools for decision-making since they do not communicate uncertainty. To address this, we propose a Probabilistic Joint Recovery Method (PJRM) that computes the posterior distribution at each monitoring survey while leveraging the shared structure among surveys through a common generative model. By efficiently computing posterior distributions for each monitoring survey, this method aims to provide valuable uncertainty information to decision-makers in CCS projects, augmenting their workflow with principled risk minimization.},
  keywords = {ML4SEISMIC, SLIM, FWI, imaging, jrm, image generative model, igm, Bayesian inference, amortized variational inference, uncertainty quantification, deep learning, inverse problems, summary statistics},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/deng2024ML4SEISMICpjr}
}

@PRESENTATION{gahlot2024ML4SEISMICosm,
  author = {Abhinav Prakash Gahlot and Felix J. Herrmann},
  title = {Optimizing CO2 Storage Monitoring with Enhanced Rock Physics Modeling},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2024},
  month = {11},
  abstract = {Based on the latest data-assimilation and machine-learning techniques, Digital Twins (DTs) have shown promise for high-fidelity monitoring and control of underground CO2​ storage. While the use of these techniques have important advantages, they do rely on certain assumptions. If these assumptions are not met, the DT’s neural networks may no longer infer the state of the CO2 plume (pressure/saturation) accurately. By augmenting the forecast ensemble, we address this issue.},
  keywords = {ML4SEISMIC, SLIM, FWI, RTM, imaging, conditional normalizing flows, rock physics, data assimilation, Bayesian inference, amortized variational inference, uncertainty quantification, deep learning, inverse problems, summary statistics, augmentation},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/gahlot2024ML4SEISMICosm}
}

@PRESENTATION{erdinc2024ML4SEISMICsfm,
  author = {Huseyin Tuna Erdinc and Rafael Orozco and Felix J. Herrmann},
  title = {SAGE -- Subsurface foundational model with AI-driven Geostatical Extraction},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2024},
  month = {11},
  abstract = {In this study, we present a novel approach for synthesizing diverse subsurface velocity models using diffusion-based generative models. Traditional methods often depend on large, high-quality datasets of 2D velocity models, which can be difficult to obtain in subsurface applications. In contrast, our method leverages incomplete well and seismic data to generate high-fidelity velocity samples without requiring fully sampled training datasets.The results demonstrate that the generative model accurately captures long-range geological structures and aligns well with unseen ground-truth velocity models. Furthermore, it is shown that the diversity of generated velocity models can be increased through prior guidance in the training phase, and model uncertainties can be reduced with well conditioning during inference.Experiments conducted with multiple datasets (BG, Synthoseis, and North Sea data) and velocity models featuring various geological structures (e.g., faults, salt bodies) suggest that our approach facilitates realistic subsurface velocity synthesis, providing valuable inputs for full-waveform inversion and enhancing seismic-based subsurface modeling.},
  keywords = {ML4SEISMIC, SLIM, FWI, RTM, imaging, WISE, diffusion models, Bayesian inference, amortized variational inference, uncertainty quantification, deep learning, inverse problems, summary statistics},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/erdinc2024ML4SEISMICsfm}
}

@PRESENTATION{orozco2024ICLnwi,
  author = {Rafael Orozco and Ziyi Yin and Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann},
  title = {Neural wave-based imaging with amortized uncertainty quantification},
  booktitle = {ICL Seminar},
  year = {2024},
  month = {6},
  keywords = {FWI, RTM, imaging, WISE, CIG, conditional normalizing flows, Bayesian inference, amortized variational inference, uncertainty quantification, deep learning, inverse problems, summary statistics, MVA},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ICL/2024/orozco2024ICLnwi},
}

@PRESENTATION{gahlot2024ICLdtg,
  author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Huseyin Tuna Erdinc and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann},
  title = {Digital Twins in the era of generative AI - Application to Geological CO2 Storage},
  booktitle = {ICL Seminar},
  year = {2024},
  month = {6},
  abstract = {Our industry is experiencing significant changes due to AI and the challenges of the energy transition. While some view these changes as threats, recent advances in AI offer unique opportunities, especially in the context of Digital Twins for subsurface monitoring and control. IBM defines "A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making." During this talk, I will explore these concepts and their significance in addressing the challenges of monitoring & control of geological CO2 storage projects. This talk also aims to illustrate how Digital Twins can serve as a platform to integrate the seemingly disparate and siloed fields of geophysics and reservoir engineering.},
  keywords = {GCS, digital twin, control, WISE, FWI, RTM, imaging, CIG, conditional normalizing flows, data assimilation, Bayesian inference, amortized variational inference, uncertainty quantification, deep learning, inverse problems, summary statistics, MVA},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ICL/2024/gahlot2024ICLdtg},
}

@PRESENTATION{herrmann2024dt4gcs,
  author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Huseyin Tuna Erdinc and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann},
  title = {Digital Twins in the Era of Generative AI: Application to Geological CO2 Storage},
  booktitle = {HCMF Seminar},
  year = {2024},
  month = {2},
  abstract = {Our industry is experiencing significant changes due to AI and the challenges of the energy transition. While some view these changes as threats, recent advances in AI offer unique opportunities, especially in the context of Digital Twins for subsurface monitoring and control. IBM defines "A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making." During this talk, I will explore these concepts and their significance in addressing the challenges of monitoring & control of geological CO~2~ storage projects. This talk also aims to illustrate how Digital Twins can serve as a platform to integrate the seemingly disparate and siloed fields of geophysics and reservoir engineering.},
  keywords = {SLIM, ccs, deep learning, uncertainty quantification, bayesian inference, imaging, monitoring, digital twin},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/Halliburton/2024/herrmann2024dt4gcs},
  presentation = {https://slim.gatech.edu/Publications/Public/Conferences/Halliburton/2024/herrmann2024dt4gcs.pdf}
}

@PRESENTATION{yin2024GTwise,
  author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann},
  title = {Generative AI for full-waveform variational inference},
  booktitle = {Georgia Tech Geophysics Seminar},
  year = {2024},
  month = {1},
  abstract = {We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging.},
  keywords = {GT, SLIM, normalizing flows, variational inference, FWI, conditional normalizing flows, uncertainty quantification, machine learning, deep learning, summary statistics, inversion, amortized Bayes, generative AI},
  url = {https://slim.gatech.edu/Publications/Public/Lectures/GTseminar/2024/yin2024GTwise}
}

%----- 2023 -----%

@PRESENTATION{erdinc2023ML4SEISMICias,
  author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann},
  title = {Improved automatic seismic CO2 leakage detection via dataset augmentation},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2023},
  month = {11},
  abstract = {Previous works showed that neural classifiers can be trained to detect CO2 leakage from time-lapse seismic images. While this result is crucial to the global deployment of geological carbon storage (GCS), its success depends on relatively dense non-replicated time-lapse data acquisition. In this study, we present an approach to enhance the detection accuracy and robustness of CO2 leakage detection by augmenting the training dataset with a variety of coarsely sampled receiver data and their corresponding receiver numbers. This augmentation strategy is particularly beneficial for scenarios where low-cost coarse receiver samplings, such as with ocean bottom nodes (OBNs), are utilized. Furthermore, we explore interpretability of the classifier's decisions by generating saliency maps for further analysis.},
  keywords = {ML4SEISMIC, SLIM, classifier, ccs, leakage detection, deep learning},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/erdinc2023ML4SEISMICias}
}

@PRESENTATION{herrmann2023ML4SEISMICmsc,
  author = {Abhinav Prakash Gahlot and Ting-ying Yu and Rafael Orozco and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann},
  title = {Monitoring subsurface CO2 plumes with learned sequential Bayesian inference},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2023},
  month = {11},
  abstract = {Reservoir engineers frequently employ two-phase flow simulations and history-matching to oversee and anticipate the behavior of CO2 plumes within geological carbon storage. These simulations, while valuable for gaining insights, face limitations due to several complex factors, such as uncertainties surrounding the plume’s dynamics. To investigate this phenomenon more comprehensively, we introduce the concept of stochasticity in the dynamics, accounting for uncertainties in the underlying permeability of the reservoir. To enhance the accuracy of CO2 plume predictions and quantify the uncertainties involved, we utilize machine learning techniques to condition these predictions on time-lapse seismic and well observations. This framework works on the principle of sequential Bayesian inference that continuously assimilates information from time-lapse observations, updates the CO2 plume predictions, and characterizes uncertainties about the plumes.},
  keywords = {ML4SEISMIC, SLIM, ccs, deep learning, uncertainty quantification, bayesian inference, imaging, monitoring},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/herrmann2023ML4SEISMICmsc}
}

@PRESENTATION{bruer2023ML4SEISMICcrm,
  author = {Grant Bruer and Felix J. Herrmann and Edmond Chow},
  title = {CO2 reservoir monitoring through Bayesian data assimilation},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2023},
  month = {11},
  abstract = {Carbon capture and storage can be implemented by injecting supercritical carbon dioxide (CO2) into geological carbon reservoirs for long-term containment. Monitoring the pressure and saturation of the CO2 is necessary to optimize the injection amount without causing CO2 leakage or seismic activity. Directly measuring the CO2 at locations within the reservoir requires expensive drilling procedures that may damage the reservoir, so direct measurements are sparse and usually lie along injection and production wells. Indirect measurements such as seismic data are typically noisy, and inverting for the CO2 state is ill-posed. Bayesian data assimilation techniques allow us to integrate known physics for CO2 flow into this inversion process. The most well-established data assimilation algorithms are the family of Kalman filters. The ensemble Kalman filter is designed to efficiently work with large problem sizes and nonlinearity. In this work, we apply the ensemble Kalman filter to seismic measurements of a CO2 reservoir, yielding an estimate of the saturation and pressure fields with quantified uncertainties. This method models the CO2 plume state as a random field with a known distribution and assimilates information from seismic measurements with information from a physics model describing the CO2 flow. We show that the data assimilation strategy is a valuable contribution to advancing reservoir monitoring technology.},
  keywords = {ML4SEISMIC, SLIM, kalman, data assimilation, bayes, ccs, monitoring},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/bruer2023ML4SEISMICcrm}
}

@PRESENTATION{gahlot2023ML4SEISMICtsm,
  author = {Abhinav Prakash Gahlot and Mathias Louboutin and Ziyi Yin and Felix J. Herrmann},
  title = {Time-lapse seismic monitoring of geological carbon storage with the nonlinear joint recovery model},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2023},
  month = {11},
  abstract = {During time-lapse seismic monitoring of CO2 plumes, a weak 4D signal below the level of inversion or migration artifacts poses challenges. To address these, low-cost randomized non-replicated acquisitions and a linear joint recovery model (JRM) have been introduced. It takes advantage of the shared information between different vintages in the time-lapse seismic data and subsurface structure undergoing localized changes. Since the relationship between seismic data and subsurface properties is seldom linear, we propose a more versatile nonlinear JRM (nJRM) to invert for the squared slowness of the vintages. The nJRM takes advantage of the full nonlinear relation between these squared slownesses and time-lapse data through the wave equation. Also, careful derivation of the gradients makes the computational cost of nJRM equivalent to the independent recovery. We present a synthetic study for geological carbon storage (GCS) which shows that the non-replication can be beneficial to time-lapse imaging, making seismic monitoring of GCS less costly for the long term sustainability of the technology.},
  keywords = {ML4SEISMIC, SLIM, jrm, ccs, monitoring},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/gahlot2023ML4SEISMICtsm}
}

@PRESENTATION{rex2023ML4SEISMIClsp,
  author = {Richard Rex and Thomas J. Grady II and Rishi Khan and Ziyi Yin and Felix J. Herrmann},
  title = {Large-scale parametric PDE approximations with model-parallel Fourier neural operators},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2023},
  month = {11},
  abstract = {Solving PDEs to simulate two-phase flow is expensive since it involves the inversion of large ill-conditioned matrices. FNOs represent a special type of neural network capable of approximating solutions to two-phase flow equations. In order to speed up this computation, we develop a high-level software abstraction tool to exploit the linearly separable property of Fourier Transforms via Kronecker products. We perform a series of all-to-all operations where we partition the data to apply the operations in a distributed fashion. We apply these FNOs to predict the evolution of CO2-plumes in subsurface environments. Our model takes an input permeability model, and outputs time-varying CO2 saturations in a quick and cost-effective manner. Additionally, our research involves developing a distributed matrix-free abstraction library that can be used to represent any generic linear and nonlinear operator. This library is scalable and auto-differentiable thanks to the hand-written customized AD rules, allowing us to represent and train any network. We provide an evaluation on the accuracy of FNO simulations compared to traditional PDE simulations in solving various classes of PDEs.},
  keywords = {ML4SEISMIC, SLIM, Fourier neural operators, deep learning, CCS, HPC},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/rex2023ML4SEISMIClsp}
}

@PRESENTATION{li2023ML4SEISMICmci,
  author = {Haoyun Li and Ziyi Yin and Olav Møyner and Felix J. Herrmann},
  title = {Maximizing CO2 injectivity within fracture pressure},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2023},
  month = {11},
  abstract = {In geological carbon storage projects, optimizing CO2 injection strategies is paramount to enhance storage efficiency and prevent leakage. The objective is to maximize the CO2 injection volume without surpassing the fracture pressure. Traditional adjoint-based approaches necessitate extensive numerical simulations, leading to significant computational overhead. To circumvent this challenge, we introduce an optimization framework based on physics-informed deep convolutional neural networks. Trained on different permeability slices, our model can rapidly predict the maximal CO2 injection volume for new permeability fields in real-time.},
  keywords = {ML4SEISMIC, SLIM, ccs, optimization, reservoir simulation},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/li2023ML4SEISMICmci}
}

@PRESENTATION{orozco2023ML4SEISMICtgs,
  author = {Rafael Orozco and Mathias Louboutin and Felix J. Herrmann},
  title = {Towards generative seismic kriging with normalizing flows},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2023},
  month = {11},
  abstract = {Our goal is to build realistic parameterized (acoustic, velocity, permeability etc) earth models where the training and testing phase of our method uses only data that is available in the field. We first demonstrate the expressive power of normalizing flows to generate detailed realistic earth models by training on supervised pairs of full earth models and borehole wells. Our results are compared with traditional variogram kriging to show that our generated models can be used in parameterizations of various downstream tasks such as simulations of realistic acoustic waves and fluid flow for reservoir simulations. Then we introduce a novel unsupervised training objective that can train normalizing flows to generate full earth models without needing training pairs of the full earth models. By using a known proxy earth model as a testbed, we make preliminary prescriptions on how many wells our method needs to generate permissible earth models in a target area.},
  keywords = {ML4SEISMIC, SLIM, uncertainty quantification, kriging, normalizing flows},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/orozco2023ML4SEISMICtgs}
}

@PRESENTATION{orozco2023ML4SEISMICuqs,
  author = {Rafael Orozco and Mathias Louboutin and Peng Chen and Felix J. Herrmann},
  title = {Uncertainty quantification so what? Leveraging probabilistic seismic inversion for experimental design},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2023},
  month = {11},
  abstract = {Combining physics with recent developments of generative machine learning enables a scalable probabilistic framework for tackling seismic inversion problems including Full-Waveform Inversion. These probabilistic results can be proven to be from the Bayesian posterior but how exactly can we use them for practical downstream tasks? In this talk, we answer the question with a practical application of the probabilistic framework towards designing ocean bottom node placement of seismic imaging. With a simple adjustment to the original training objective, we show that jointly optimizing for an experimental design corresponds to maximizing the expected information gain used by the Bayesian community. After verifying this novel joint optimization with a stylized problem, we demonstrate its application for optimizing the placement of ocean bottom nodes in a synthetic seismic imaging experiment.},
  keywords = {ML4SEISMIC, SLIM, uncertainty quantification, experimental design, FWI, bayesian, normalizing flows},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/orozco2023ML4SEISMICuqs}
}

@PRESENTATION{yin2023ML4SEISMICe2e,
  author = {Ziyi Yin and Mathias Louboutin and Olav Møyner and Felix J. Herrmann},
  title = {End-to-end permeability inversion from prestack time-lapse seismic data: a case study on Compass model},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2023},
  month = {11},
  abstract = {Effective geological carbon storage hinges on a deep understanding of CO2 plume behavior. The dynamics of these plumes can be modeled using multiphase flow equations, but their accuracy is tied to a precise permeability model. A significant challenge is that we often lack detailed permeability data, limiting our predictive capabilities. To bridge this gap, we’ve developed a multiphysics inversion method. This technique inverts for the permeability from observed time-lapse seismic data. Through a case study on the Compass model, we’ve compared this approach with traditional 4D FWI in forecasting CO2 plume movements. Additionally, our research delves into how different initial permeability models, acquisition setups, and survey frequencies affect the results. Across the board, the inversion method not only enhances our current estimations but also provides valuable insights into future plume dynamics, even without continuous monitoring.},
  keywords = {ML4SEISMIC, SLIM, ccs, coupled inversion, end-to-end, fluid-flow, inversion, monitoring},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/yin2023ML4SEISMICe2e}
}

@PRESENTATION{yin2023ML4SEISMICwise,
  author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann},
  title = {WISE: Full-waveform Inference with Subsurface Extensions},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2023},
  month = {11},
  abstract = {Quantifying uncertainty in full-waveform inversion is complex given the large sizes of both the model and data. A previous approach employed a variational inference framework, leveraging reverse-time migration to summarize observed data and approximate the posterior distribution through conditional normalizing flows. While reverse-time migration effectively summarizes the data when the background model is close to the true one, its accuracy diminishes with a less accurate background model. In our study, we suggest utilizing subsurface offset gathers as the summary statistics for the variational inference of full-waveform inversion. These gathers retain all the information in seismic data, even when the background model is cycle-skipped or fails to flatten the gathers. Through a case study on Compass model, we confirm our framework's effectiveness and show that subsurface offset gathers offer a better summary statistic than just reverse-time migration.},
  keywords = {ML4SEISMIC, SLIM, normalizing flows, variational inference, FWI, conditional normalizing flows, uncertain quantification, machine learning, deep learning, summary statistics, inversion, amortized Bayes},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/yin2023ML4SEISMICwise}
}

@PRESENTATION{yin2023CSEslim,
  title = {Introduction to Seismic Laboratory for Imaging and Modeling},
  booktitle = {CSE Student Recruiting Event},  
  year = {2023},
  month = {11},
  abstract = {We introduce the latest research developments in Seismic Laboratory for Imaging and Modeling (SLIM), including geological carbon storage monitoring, seismic and medical imaging and uncertainty quantification, and advancements in scientific machine learning and high performance computing.},
  keywords = {SLIM, uncertainty quantification, GCS, CCS, medical imaging, PDE, Fourier neural operators, normalizing flows, multiphysics, deep learning, learned surrogates, learned constraints, inverse problems},
  note = {(CSE Student Recruiting Event)},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/CSE/2023/SLIM.pdf},
  author = {Ziyi Yin}
}

@PRESENTATION{yin2023HOTCSEspi,
  title = {Solving PDE-based inverse problems with learned surrogates and constraints},
  booktitle = {HotCSE Seminar},  
  year = {2023},
  month = {11},
  abstract = {In this presentation, I will introduce a learned inversion algorithm for solving inverse problems with computationally expensive forward operators. We tackle this challenge by combining learned surrogates (Fourier neural operators) with learned constraints (normalizing flows). After jointly training these networks with the same samples, the learned surrogates lead to computationally efficient surrogate-assisted inversion. Meanwhile, the learned constraints safeguard the accuracy of the surrogates by forcing the model iterates to remain in-distribution. By combining the two, we come up with a homotopy / continuation scheme where the constraints are relaxed slowly so that the data misfit objective can be minimized while the model iterates always remain in the statistical distribution on which the surrogates are trained. We demonstrate the efficacy of our learned inversion algorithm through carefully selected experiments centered around the problem of geological carbon storage monitoring.},
  keywords = {PDE, Fourier neural operators, normalizing flows, multiphysics, deep learning, learned surrogates, learned constraints, inverse problems},
  note = {(HotCSE)},
  url = {https://slim.gatech.edu/Publications/Public/Lectures/HotCSE/2023/yin2023HOTCSEspi},
  author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}

%----- 2022 -----%

@PRESENTATION{herrmann2023SCAIMact,
  author = {Felix J. Herrmann},
  title = {Act normal that's crazy enough — an overview of seismic inversion with normalizing flows and surrogate modeling},
  booktitle = {Scientific Computing, Applied and Industrial Mathematics (SCAIM) Seminar},
  year = {2023},
  month = {03},
  abstract = {During this talk, I will give an overview on how techniques from neural (conditional) density estimation and surrogate modeling can be used to solve challenging problems in seismic imaging and monitoring of geological carbon storage. I will start by outlining how (conditional) normalizing flows can be used as priors, to regularize inverse problems, and as low-fidelity amortized posteriors for wave-based inversions. To this end, I will uss techniques from simulation-based inference. When time, permits I will also talk about permeability inversion from time-lapse seismic data using neural surrogates (Fourier Neural Operators) to mimic solution operators of two-phase flow equations.},
  keywords = {SCAIM, SLIM, CCS, uncertainty quantification, sequential Bayes, normalizing flows, surrogate, fno, GCS, monitoring},
  note = {(SCAIM, Vancover)},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SCAIM/2023/herrmann2023SCAIMact/index.html}
}

@PRESENTATION{yu2022ML4SEISMICmsb,
  author = {Ting-ying Yu and Rafael Orozco and Ziyi Yin and Felix J. Herrmann},
  title = {Monitoring with sequential Bayesian inference},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2022},
  month = {11},
  abstract = {For this study, we apply sequential Bayesian inference to monitor the time evolution of subsurface flow of CO2 from indirect acoustic measurements at the surface. Upon receiving new acoustic measurements, we infer the current state of the CO2 by sampling from a learned posterior. Using the incoming data, we then perform online updates of the current posterior. This is accomplished by using the fluid flow model to advance the estimated state variable forward in time in order to update the learned posterior. With a synthetic experiment, we demonstrate this method can track the flow evolution accurately as measured by PSNR metrics. Since the posterior is a learned network, we can compute estimates faster than traditional least squares methods. This method can also quantify the uncertainty due to stochasticity in fluid flow model and the limited-azimuth imaging configuration.},
  keywords = {ML4SEISMIC, SLIM, time-lapse, uncertainty quantification, sequential Bayes, conditional normalizing flows, CCS, GCS, monitoring},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yu2022ML4SEISMICmsb/index.html}
}

@PRESENTATION{zhang2022ML4SEISMICtss,
  author = {Yijun Zhang and Mathias Louboutin and Ali Siahkoohi and Ziyi Yin and Rajiv Kumar and Oscar Lopez and Felix J. Herrmann},
  title = {Time-lapse seismic survey design by maximizing the spectral gap},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2022},
  month = {11},
  abstract = {While time-lapse seismic has been applied successfully to CO2 sequestration monitoring, it remains a challenging problem since replicated dense surveys come at a very high cost in the field. Wavefield reconstruction based on matrix completion (MC) from randomized subsampled data is an efficient way to reduce operational costs. This technique allows for accurate time-lapse reconstruction by employing the joint recovery model (JRM), which capitalizes on the fact that different vintages share a common component. However, combining JRM with optimal time-lapse acquisition survey design remains an unexplored area of research. In expander graph theory, spectral gap (SG) reveals the source-receiver layout connectivity and is related to reconstruction quality during MC. Building on these insights, we proposed a simulation free time-lapse survey design based on JRM that aims to get similar reconstructed quality without insisting on replicate surveys, which significantly reduces the cost in the field. This approach uses the simulated annealing algorithm to find subsampling masks for each vintage. Numerical experiments confirm a direct correlation between increased spectral gap and promising time-lapse reconstruction quality.},
  keywords = {ML4SEISMIC, SLIM, time-lapse, acquisition, survey design, wavefield reconstruction, spectral gap, matrix factorization, JRM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/zhang2022ML4SEISMICtss/index.html}
}

@PRESENTATION{yin2022ML4SEISMICutc,
  author = {Ziyi Yin and Rafael Orozco and Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann},
  title = {Uncertainty-aware time-lapse {CO$_2$} monitoring with learned end-to-end inversion},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2022},
  month = {11},
  abstract = {Seismic monitoring of CO2 sequestration is computationally expensive as it involves modeling of both fluid-flow physics modeling and wave physics and differentiation through the solvers with respect to the subsurface properties of interest. In this talk, we demonstrate the effectiveness of learned coupled inversion framework using a pre-trained Fourier neural operator as a learned surrogate for the fluid-flow simulator, which greatly reduces the cost associated with fluid-flow modeling and differentiation through the solver. We study the effectiveness and correctness of inversion based on Fourier neural operator surrogate and a normalizing flow prior. We also demonstrate the efficacy of this framework on monitoring the growth of CO2 plumes during sequestration, and on uncertainty quantification of the permeability and CO2 plumes with conditional normalizing flow. With this framework, we can further forecast the CO2 plume in the future without any acquired seismic data with uncertainty estimation.},
  keywords = {ML4SEISMIC, SLIM, normalizing flows, Fourier neural operators, GCS, conditional normalizing flows, uncertain quantification, machine learning, deep learning, time-lapse, inversion, amortized Bayes},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yin2022ML4SEISMICutc/index.html}
}

@PRESENTATION{yin2022ML4SEISMICsfg,
  author = {Ziyi Yin and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann},
  title = {Simulation-based framework for geological carbon storage monitoring},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2022},
  month = {11},
  abstract = {While various monitoring modalities exist to track the behavior of CO2 plumes to ensure safe operations and compliance with regulatory requirements, active 3D time-lapse seismic monitoring has proven superior but costly. At SLIM, we aim to reduce the operating costs by optimizing acquisition design, to help drive innovations in seismic monitoring acquisition design and imaging, and to test novel time-lapse acquisition and imaging technologies in silico at scale. In this talk, we will introduce our open-source software platform simulation-based monitoring design framework. We demonstrate how to make use of proxy models for seismic properties derived from real 3D imaged seismic and well data to conduct realistic synthetic geological carbon storage projects. Furthermore, we discuss our proposed sparse non-replicated seismic acquisition and cutting-edge methodology to recover the dense data or to directly image the sparse non-replicated via joint recovery model. This automatic workflow ends with deep neural classifiers to detect potential CO2 leakage over seal through pressure-induced fault openings. We envisage the development of an automatic workflow to handle the large number of continuously monitored CO2 injection sites needed to help combat climate change.},
  keywords = {ML4SEISMIC, SLIM, CCS, GCS, Imaging, JRM, time-lapse, CAM, machine learning, deep learning, classification, explainability},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yin2022ML4SEISMICsfg/index.html}
}

@PRESENTATION{yin2022ML4SEISMICavc,
  author = {Ali Siahkoohi and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann},
  title = {Amortized velocity continuation with Fourier neural operators},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2022},
  month = {11},
  abstract = {Velocity continuation aims to map the migration image using one background model to the image using another background model. It is of great importance to quantify the uncertainty in seismic imaging result from various background models. With Fourier neural operators as a learned surrogate, this continuation from a given background model to an unseen background model can be quite accurately estimated with near-zero cost. However, the limitation of the prior art is that the input background model and the survey area are assumed to be fixed. The main contribution of this work is to extend the Fourier neural operator surrogate to be amortized over different given background models and survey areas. We verify the effectiveness of our learned surrogates by a realistic example on different areas of Parihaka dataset against different background models.},
  keywords = {ML4SEISMIC, SLIM, Fourier neural operators, velocity continuation, uncertainty quantification},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yin2022ML4SEISMICavc/index.html}
}

@PRESENTATION{siahkoohi2022ML4SEISMICluq,
  author = {Ali Siahkoohi and Gabrio Rizzuti and Rafael Orozco and Felix J. Herrmann},
  title = {Low-cost uncertainty quantification for large-scale inverse problems},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2022},
  month = {11},
  abstract = {Bayesian inference for large-scale inverse problems is challenged by the computationally costly forward operator evaluations during posterior distribution sampling. Recent advances in variational inference and deep learning reduce these costs by pretraining a neural network capable of sampling the posterior distribution for previously unseen observed data. In geophysical applications, however, the accuracy of these methods depends on sufficiently capturing subsurface variability through a training dataset, which is challenging given the heterogeneity of the Earth’s subsurface and our lack of access to it. Moreover, these methods may be unreliable in the presence of data distribution shifts, e.g., a change in the number of source experiments, noise distribution, or geological features to be imaged. As such, we present a solution that increases the robustness of deep-learning-based Bayesian inference approaches when faced with changes in data distribution. Our proposed method involves a physics-based adaptation to the latent distribution of a conditional normalizing flow that is pretrained to approximate the posterior distribution for previously unseen data. Instead of feeding standard Gaussian latent samples to the conditional normalizing flow, this method parameterizes the latent distribution by a Gaussian distribution with an unknown mean and diagonal covariance, estimated by minimizing the Kullback-Leibler divergence between predicted and true posterior distributions. This method is applicable to a wide range of inverse problems and has the potential to significantly reduce the costs of Bayesian variational inference. By means of a realistic seismic imaging example we demonstrate that the proposed latent distribution adaptation method mitigates the Bayesian inference errors induced by data distribution shifts, including shifts in the forward model and prior distribution.},
  keywords = {ML4SEISMIC, SLIM, Variational Inference, Seismic Imaging, Normalizing Flows, Inverse Problems, Uncertainty Quantification},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/siahkoohi2022ML4SEISMICluq/siahkoohi2022ML4SEISMICluq_pres.pdf}
}

@PRESENTATION{orozco2022ML4SEISMICnfr,
  author = {Rafael Orozco and Mathias Louboutin and Felix J. Herrmann},
  title = {Normalizing flows for regularization of 3D seismic inverse problems},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2022},
  month = {11},
  abstract = {We present the first known exploration of a normalizing flow (NF) for generative 3D volumes. First, we tackle computational issues surrounding the high dimensionality of our desired 3D volume output. This is of particular concern in normalizing flows since their invertibility constraint implies equal dimension of output and input. Our findings show that by “freezing” expensive layers we can efficiently train a normalizing flow on 3D volumes. Using this NF architecture, we train a generative model on volume sections of the 3D BG compass model. Our method produces visually plausible generative samples which are efficient to produce. We demonstrate its practical use by using our trained generative model as an implicit prior in a Maximum A Posteriori (MAP) framework. We evaluate this MAP framework by estimating the solution of a inverse problem in seismic imaging. Our method results in higher SNR estimates than the baseline and in less iterations, importantly saving the computational cost of evaluating the expensive 3D PDE solver during optimization. Finally, through scaling analysis of training cost, we show that NF convolutional layers allow this approach to scale favorably to larger volumes.},
  keywords = {ML4SEISMIC, SLIM, Uncertainty Quantification, Bayesian Inference, Normalizing Flows, Inverse Problems, 3D, Machine Learning, Deep Learning},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/orozco2022ML4SEISMICnfr/index.html}
}

@PRESENTATION{orozco2022ML4SEISMICaos,
  author = {Rafael Orozco and Mathias Louboutin and Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann},
  title = {Adjoint operators as summary functions in amortized Bayesian inference frameworks},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2022},
  month = {11},
  abstract = {An important concern in seismic inverse problems is the large and varying size of observed data. The large size can cause computational cost concerns and its varying size (such as when changing receiver geometries) implies the need to rerun inference algorithms from scratch for each new observation. Motivated by these two problems, we take inspiration from the statistics literature which commonly relies on summary statistic of observed data. Summary statistic compress the observed data leaving only information needed for inference. In this work, we argue that the adjoint operator provides a natural candidate for a summary function in the context of physics-based inverse problems. We first mathematically show that for certain general assumptions transforming data under the adjoint operator defines a new conditional distribution which preserves the expectations of the original posterior. We validate our hypothesis by evaluating our framework in a learned amortized inference algorithm. Our seismic and medical synthetic experiments show computational gains and increased quality of point estimates using our framework. We discuss statistical metrics that show our learned posterior is well calibrated therefore justifying its use in uncertainty quantification.},
  keywords = {ML4SEISMIC, SLIM, Uncertainty Quantification, Bayesian Inference, Amortized Inference, Normalizing Flows, Inverse Problems, Medical Imaging, Machine Learning, Deep Learning},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/orozco2022ML4SEISMICaos/index.html}
}

@PRESENTATION{louboutin2022ML4SEISMICmos,
  author = {Mathias Louboutin and Ziyi Yin and Rafael Orozco and Thomas J. Grady II and Felix J. Herrmann},
  title = {ML4Seismic open-source software: updates and developments},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2022},
  month = {11},
  abstract = {Software is at the core of research and development in inverse problems. At SLIM, we have experience developing scalable and performant software, such as our legacy parallel MATLAB framework. With ML4Seismic, we are dedicated to build on this experience to develop HPC open source software (OSS) for the scientific community in collaboration with our partners. In this talk, we will describe our OSS Julia and Python environment, our high-level abstraction principles, and the range of solutions we offer for seismic processing and inversion and for machine learning. We will emphasize our aim to provide scalable software that can be easily applied to industrial problems.},
  keywords = {ML4SEISMIC, SLIM, open-source, software},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/louboutin2022ML4SEISMICmos/index.html}
}

@PRESENTATION{louboutin2022ML4SEISMIClew,
  author = {Mathias Louboutin and Yadhu Kartha and Rafael Orozco and Felix J. Herrmann},
  title = {Learned extensions for wave-based simulation and inversion},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2022},
  month = {11},
  abstract = {We introduce a new method that explores velocities as an operator (extended velocities) for wave-equation based inversion. Through this extended formulation, we obtain the known benefits of working with subsurface offset volumes. The offset-dependence of these volumes has been studied in the linear case, i.e as part of extended Born scattering and extended least-squares reverse-time migration, but has been avoided for non-linear inversion due to computational concderns and challenges. By using techniques from randomized linear algebra, we will show that we can work with extended velocities for inversion while maintaining an acceptable computational cost much lower than solving one PDE per extended velocity model.},
  keywords = {ML4SEISMIC, SLIM, deep learning, wave equation, imaging},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/louboutin2022ML4SEISMIClew/index.html}
}

@PRESENTATION{herrmann2022ML4SEISMICmod,
  author = {Felix J. Herrmann},
  title = {Meet our digital twin for geological carbon storage},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2022},
  month = {11},
  abstract = {By embracing recent developments in simulation-based Bayesian inference—i.e., the task of deriving statistical information from a system based on in silico simulations—we envisage the development of an uncertainty-aware Digital Twin for seismic monitoring of Geologic Carbon Storage (GCS). According to IBM, “A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making”. This Digital Twin will be designed to maximally benefit from vastly improved abilities to simulate complex phenomena, including the development of CO2 plumes in saline aquifers, and from the ability of neural networks to learn by example as part of inference. For GCS, this means that systematic assessment of uncertainties now becomes possible when observing CO2 plumes from time-lapse geophysical data (e.g., seismic). Because the proposed Digital Twin’s neural networks are taught to produce samples from the probability distribution for the CO2 plume conditioned by the observed time-lapse data, this approach will provide access to this information on uncertainty. As part of ML4Seismic, we are working on various aspects regarding the development of the Digital Twin including: (i) capability to generate realistic time-lapse data in response to CO2 injection in large strongly heterogeneous reservoirs. This simulation framework will facilitate the design of high-fidelity monitoring systems and is unique since it uses proxy Earth models with realistic CO2 plumes and heterogeneity; (ii) An inversion framework capable of producing high-fidelity time-lapse images of CO2 plumes and reservoir properties from time-lapse data collected in response to CO2 injection; (iii) uncertainty-aware data-assimilation framework based on techniques from sequential Bayes and capable of rapidly producing high-fidelity CO2 plume forecasts that are consistent with observed time-lapse data; (iv) A scalable uncertainty-aware early warning system designed to safeguard CO2 injection operations built on the latest insights from interpretable and trustworthy (explainable and robust) machine learning. After describing how to build a Digital Twin for GSC, early results will be presented on the use of Fourier Neural Networks as surrogates for the two-phase flow equations, seismic monitoring with our joint recovery model, and the use of spectral ratio to design low-cost acquisitions for time-lapse seismic.},
  keywords = {ML4SEISMIC, SLIM, digital twin, GCS, CCS},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/herrmann2022ML4SEISMICmod/index.html}
}

@PRESENTATION{herrmann2022ML4SEISMICintro,
  author = {Felix J. Herrmann},
  title = {Introduction to 2022 ML4SEISMIC Partners Meeting},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2022},
  month = {11},
  keywords = {ML4SEISMIC, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/herrmann2022ML4SEISMICintro/index.html}
}

@PRESENTATION{grady2022ML4SEISMICesn,
  author = {Thomas J. Grady II and Rishi Khan and Mathias Louboutin and Ziyi Yin and Philipp A. Witte and Ranveer Chandra and Russell J. Hewett and Felix J. Herrmann},
  title = {Effective scaling of numerical surrogates via domain-decomposed Fourier neural operators},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2022},
  month = {11},
  abstract = {Numerical surrogates are models which learn to mimic a complex physical process (such as the solution to a PDE produced by a solver) from a set of input/output pairs. Fourier neural operators (FNOs) are a specific type of numerical surrogate which use a learned matched filter to quickly approximate solutions to relatively smooth complex physical processes. In the case of carbon capture sequestration (CCS) technology, FNOs have been shown to well-approximate solutions to the two-phase flow equations, with speedups of 1,000 to 10,000 times at inference time versus a tradtitional solver. This speed combined with the fact that FNOs are differentiable with respect to their input parameters allows for inverse and uncertainty quantification problems to theoretically be solved on real 3D data, a previously intractible task. However, due to the size of the input data, network weights, and optimizer state, FNOs have thus far been limited to small to medium 2D and 3D problems, well below the size of an industry standard such as the Sleipner benchmark. Here we alleviate this problem by proposing a model-parallel FNO which makes use of domain decomposition of the input data and network weights, and exploits architectural features of FNOs to also include a natural form of asynchronous pipeline parallelism. Our network can scale to arbitrary problem sizes on CPU and GPU systems.},
  keywords = {ML4SEISMIC, SLIM, Fourier neural operators, deep learning, CCS, HPC},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/grady2022ML4SEISMICesn/index.html}
}

@PRESENTATION{erdinc2022ML4SEISMICdgp,
  author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann},
  title = {De-risking GCS projects with explainable {CO$_2$} leakage detection in time-lapse seismic images},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2022},
  month = {11},
  abstract = {With the global deployment of Carbon, capture and storage (CCS) technology to combat climate change, there is an associated risk of contamination with CO2 leaking back to the atmosphere. Thus, it requires continuous monitoring of CO2 after the injection stops at the storage site. In this work, we generated synthetic CO2 plume development data with both leakage and no leakage scenarios. We trained a convolutional neural network (CNN) discriminative classifier and also a generative classifier and compared their performances in CO2 leakage detection. The accuracy of our discriminative classifier on the test data is 85% and that of the generative classifier is 90%. The Class Activation Mapping (CAM) results of the discriminative classifier and the latent space representation of our dataset in the case of generative classifier strengthens our claims about trustworthy leakage classification.},
  keywords = {ML4SEISMIC, SLIM, GCS, CCS, JRM, classification, CAM, explainability, imaging},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/erdinc2022ML4SEISMICdgp/index.html}
}

@PRESENTATION{herrmann2022DIRACCudt,
  author = {Felix J. Herrmann},
  title = {Uncertainty-aware Digital Twin for Monitoring Geological Carbon Storage},
  booktitle = {Direct Air Capture Center Meeting},
  year = {2022},
  month = {11},
  note = {(Diracc inaugural meeting, Atlanta)},
  keywords = {Diracc, SLIM, CCS, classification, CAM, explainability, time-lapse, uncertain quantification, digital twin},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/Diracc/2022/herrmann2022DIRACCudt/index.html}
}

@PRESENTATION{yin2022TRANSFORMjulia,
  author = {Ziyi Yin and Mathias Louboutin and Philipp A. Witte and Ali Siahkoohi and Gabrio Rizzuti and Rafael Orozco and Henryk Modzelewski and Felix J. Herrmann},
  title = {Julia for Geoscience},
  booktitle = {Transform},
  year = {2022},
  month = {04},
  abstract = {In this tutorial, we will introduce the Julia programming language to the geoscience community, covering topics such as I/O, data processing, inversion, and machine learning. We will begin by installing Julia and relevant packages. Through a series of tutorials, we will demonstrate Julia's abstraction power and show how to load and plot data, write your own functions/operators, form and solve a geophysical inverse problem, and demonstrate how to integrate wave-equation solvers in Julia with machine learning frameworks. The intent of this presentation is to provide an introductory level tutorial that will be useful to members of the geoscience community.},
  keywords = {TRANSFORM, SLIM, julia, software, JOLI, JUDI, Machine Learning},
  url = {https://transform.softwareunderground.org/2022-julia-for-geoscience},
  url2 = {https://www.youtube.com/watch?v=HyWfp3NzIbg},
  software = {https://github.com/slimgroup/SLIMTutorials}
}

%----- 2021 -----%

@PRESENTATION{zhang2021ML4SEISMICiss,
  author = {Yijun Zhang and Felix J. Herrmann},
  title = {Improved seismic survey design by maximizing the spectral gap with global optimization},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2021},
  month = {11},
  abstract = {Random subsampling is increasingly being used in the acquisition of seismic data to shorten the acquisition time and to reduce costs. However, the design of optimal acquisition geometries is still an ongoing area of research. Matrix completion (MC) is a computationally efficient method to reconstruct fully sampled wavefields from sparsely sampled seismic data. In MC theory, the spectral gap (SG), which is a measure of the connectedness of the graph in expander graph theory, has been used to predict, and to some degree quantify, the quality of wavefield reconstruction, given a specific subsampling scheme (acquisition mask). Building on these insights, we propose an optimization scheme, based on simulated annealing, which finds subsampling masks with large SGs that improve the quality of wavefield reconstruction with MC. The experimental results show that the proposed method successfully increases the SG of the subsampling mask starting from randomly initialized masks. Increasing the SG leads to improved connectivity between the sources and receivers and therefore of the wavefield reconstruction. Numerical experiments confirm a direct relationship between increased SG and improved reconstruction quality. This confirms the value SG analysis brings to the design of seismic surveys without the need to carry out expensive wavefield reconstructions to optimize the acquisition design.},
  keywords = {ML4SEISMIC, SLIM, acquisition, compressive sensing, survey design, wavefield reconstruction},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/zhang2021ML4SEISMICiss/Tue-10-30-Zhang.pdf}
}

@PRESENTATION{yin2021ML4SEISMICism,
  author = {Ziyi Yin and Mathias Louboutin and Felix J. Herrmann},
  title = {Improved seismic monitoring of CO2 sequestration with the weighted joint recovery model},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2021},
  month = {11},
  abstract = {Time-lapse seismic monitoring of CO2 sequestration is challenging because the time-lapse signature of CO2 plumes is weak in amplitude and often contaminated by imaging artifacts due to coarsely sampled, noisy, and non-replicated surveys. In this talk, we present a sparsity-promoting least-squares imaging method where the baseline, and the current and past monitor surveys are inverted jointly. We demonstrate that the sensitivity of seismic monitoring can be improved by inverting for the common component—i.e., the component shared by all vintages, and innovations with respect to this common component. Combining this joint approach with weighted l1,2-norm minimization leads to a monitoring scheme capable of detecting irregular CO2-plume growth in a realistic geological setting.},
  keywords = {ML4SEISMIC, SLIM, CCS, Compressive Sensing, Imaging, JRM, time-lapse},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/yin2021ML4SEISMICism/Tue-11-20-Yin.html}
}

@PRESENTATION{witte2021ML4SEISMICrtc,
  author = {Philipp A. Witte},
  title = {Redwood – towards clusterless supercomputing in the cloud},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2021},
  month = {11},
  abstract = {We present Redwood, a Julia framework for clusterless supercomputing in the cloud. Redwood provides a set of distributed programming macros that enable users to remotely execute Julia functions in parallel through cloud services for batch and serverless computing. We present the architecture and design of Redwood, as well as its application to existing Julia packages for machine learning and inverse problems.},
  keywords = {ML4SEISMIC, SLIM, HPC, clusterless, julia, cloud computing},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/witte2021ML4SEISMICrtc/Mon-10-30-Witte.pdf}
}

@PRESENTATION{siahkoohi2021ML4SEISMICuqi,
  author = {Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann},
  title = {Uncertainty quantification in imaging and automatic horizon tracking—a Bayesian deep-prior based approach},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2021},
  month = {11},
  abstract = {In inverse problems, uncertainty quantification (UQ) deals with a probabilistic description of the solution nonuniqueness and data noise sensitivity. Setting seismic imaging into a Bayesian framework allows for a principled way of studying uncertainty by solving for the model posterior distribution. Imaging, however, typically constitutes only the first stage of a sequential workflow, and UQ becomes even more relevant when applied to subsequent tasks that are highly sensitive to the inversion outcome. In this paper, we focus on how UQ trickles down to horizon tracking for the determination of stratigraphic models and investigate its sensitivity with respect to the imaging result. As such, the main contribution of this work consists in a data-guided approach to horizon tracking uncertainty analysis. This work is fundamentally based on a special reparameterization of reflectivity, known as “deep prior”. Feasible models are restricted to the output of a convolutional neural network with a fixed input, while weights and biases are Gaussian random variables. Given a deep prior model, the network parameters are sampled from the posterior distribution via a Markov chain Monte Carlo method, from which the conditional mean and point-wise standard deviation of the inferred reflectivities are approximated. For each sample of the posterior distribution, a reflectivity is generated, and the horizons are tracked automatically. In this way, uncertainty on model parameters naturally translates to horizon tracking. As part of the validation for the proposed approach, we verified that the estimated confidence intervals for the horizon tracking coincide with geologically complex regions, such as faults.},
  keywords = {ML4SEISMIC, SLIM, horizon picking, Imaging, machine learning, Uncertainty quantification},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/siahkoohi2021ML4SEISMICuqi/Mon-11-20-Siahkoohi.pdf}
}

@PRESENTATION{siahkoohi2021ML4SEISMICmcn,
  author = {Ali Siahkoohi and Rafael Orozco and Gabrio Rizzuti and Philipp A. Witte and Mathias Louboutin and Felix J. Herrmann},
  title = {Multifidelity conditional normalizing flows for physics-guided Bayesian inference},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2021},
  month = {11},
  abstract = {We introduce a scalable Bayesian inference approach that combines techniques from deep learning with a physic-based variational inference formulation. Bayesian inference for ill-posed inverse problems is challenged by the high-dimensionality of the unknown, computationally expensive forward operator, and choosing a prior distribution that accurately encodes prior knowledge on the unknown. To handle this situation and to assess uncertainty, we propose to approximate the posterior distribution using a pretrained conditional normalizing flow, which is trained on existing low- and high-fidelity estimations of the unknown. To further improve the accuracy of this approximation, we use transfer learning and finetune this normalizing flow by minimizing the Kullback-Leibler divergence between the predicted and the desired high-fidelity posterior density. This amounts to minimizing a physic-based variational inference objective with respect to the network weights, which we believe might scale better than Bayesian inference with Markov Chain sampling methods. We apply the proposed Bayesian inference approach to seismic imaging where we use quasi-real data obtained from the Parihaka dataset.},
  keywords = {ML4SEISMIC, SLIM, deep learning, Normalizing flows, seismic imaging, Variational Inference},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/siahkoohi2021ML4SEISMICmcn/Mon-11-45-Siahkoohi.pdf}
}

@PRESENTATION{rizzuti2021ML4SEISMICdfw,
  author = {Gabrio Rizzuti and Mathias Louboutin and Rongrong Wang and Felix J. Herrmann},
  title = {A dual formulation of wavefield reconstruction inversion for large-scale seismic inversion},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2021},
  month = {11},
  abstract = {Many of the seismic inversion techniques currently proposed that focus on robustness with respect to the background model choice are not apt to large-scale 3D applications, and the methods that are computationally feasible for industrial problems, such as full waveform inversion, are notoriously limited by convergence stagnation and require adequate starting models. We propose a novel solution that is both scalable and less sensitive to starting models or inaccurate parameters (such as anisotropy) that are typically kept fixed during inversion. It is based on a dual reformulation of the classical wavefield reconstruction inversion, whose empirical robustness with respect to these issues is well documented in the literature. While the classical version is not suited to 3D, as it leverages expensive frequency-domain solvers for the wave equation, our proposal allows the deployment of state-of-the-art time-domain finite-difference methods, and is potentially mature for industrial-scale problems.},
  keywords = {ML4SEISMIC, SLIM, FWI, WRI, wave-equation},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/rizzuti2021ML4SEISMICdfw/Tue-10-55-Rizzuti.pdf}
}

@PRESENTATION{orozco2021ML4SEISMICvia,
  author = {Rafael Orozco and Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann},
  title = {Variational inference for artifact removal of adjoint solutions in photoacoustic problems},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2021},
  month = {11},
  abstract = {Photoacoustic is a medical imaging modality which combines light and ultrasound waves to image internal structures of biological tissue. The inverse problem reconstructs the tissue initially excited by light given propagated ultrasound data at receivers outside the tissue. Due to noisy, limited-view and sparse receiver data, traditional time-reversal adjoint solutions are highly ill-posed. This necessitates uncertainty quantification to communicate to practitioners which areas of the image can be trusted. We propose a framework which leverages a machine learning based method (Conditional Normalizing Flows) to learn the full posterior distribution of viable solutions given the time-reversal adjoint solution. We show that areas of calculated uncertainty correlate with structures that are known to be difficult to image. In addition, we also propose a MAP based solution, which solves the variational least-squares problem while using the trained Conditional Normalizing Flow as a prior distribution.},
  keywords = {ML4SEISMIC, SLIM, Photoacoustic, Normalizing flow, Variational inference, conditional prior, deep image, MAP},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/orozco2021ML4SEISMICvia/Tue-09-00-Orozco.html}
}

@PRESENTATION{louboutin2021ML4SEISMICrla,
  author = {Mathias Louboutin and Felix J. Herrmann},
  title = {Randomized linear algebra for inversion},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2021},
  month = {11},
  abstract = {Inverse problems in exploration geophysics or machine learning heavily relies on linear algebra and large matrices manipulations. To tackle the growing cost of storing these matrices, randomized algorithms have been developed to obtain information from these matrices via randomized sketching. Inspired by previous work on extended image volumes, we will first show in this talk how the seismic imaging condition can be expressed in a randomized linear algebra framework leading to drastic memory savings. In a second part, we will extend this idea to convolutional neural networks to reduce the memory cost of training by orders of magnitude. We will demonstrate the practicality of these methods on representative examples.},
  keywords = {ML4SEISMIC, SLIM, software, randomized linear algebra, HPC, inversion, FWI},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/louboutin2021ML4SEISMICrla/Tue-10-20-Louboutin.pdf}
}

@PRESENTATION{louboutin2021ML4SEISMICmos,
  author = {Mathias Louboutin},
  title = {ML4Seismic open source software environment},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2021},
  month = {11},
  abstract = {Software is at the core of research and development in inverse problems. At SLIM, we have experience developing scalable and performant software, such as our legacy parallel MATLAB framework. With ML4Seismic, we are dedicated to build on this experience to develop HPC open source software (OSS) for the scientific community in collaboration with our partners. In this talk, we will describe our OSS Julia and Python environment, our high-level abstraction principles, and the range of solutions we offer for seismic processing and inversion and for machine learning. We will emphasize our aim to provide scalable software that can be easily applied to industrial problems.},
  keywords = {ML4SEISMIC, SLIM, software},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/louboutin2021ML4SEISMICmos/Mon-10-10-Louboutin.pdf}
}

@PRESENTATION{herrmann2021ML4SEISMICintro,
  author = {Felix J. Herrmann},
  title = {Introduction inaugural ML4Seismic Partners Meeting},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2021},
  month = {11},
  abstract = {During this presentation, an overview of the 2021 ML4Seismic Program will be given including organization of the meeting, setup of ML4Seismic, and the Informal Sessions in the afternoon.},
  keywords = {ML4SEISMIC, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/herrmann2021ML4SEISMICintro/Mon-09-00-Herrmann.pdf}
}

@PRESENTATION{grady2021ML4SEISMICdfn,
  author = {Thomas J. Grady II and Rishi Khan and Felix J. Herrmann},
  title = {Distributed Fourier Neural Operators},
  booktitle = {ML4SEISMIC Partners Meeting},
  year = {2021},
  month = {11},
  abstract = {Fourier Neural Operators (FNOs) are a class of neural operator, which use weightings on the Fourier transform of their inputs to approximate infinite-dimensional mappings between function spaces. They are particularly useful in approximating the solutions of smooth parametric (e.g. by permeability) partial differential equations (PDEs), and once trained are capable of producing a nearly identical output to a traditional numerical solver roughly three orders of magnitude faster, making them very useful in a wide variety of engineering applications that require repeated PDE solves (e.g. during uncertainty quantification). Until now, FNOs have been limited to small problems, as their memory intensive design makes them difficult to scale in a traditional machine learning setting on a single computer or GPU. In this work, a decomposition scheme is described and implemented in PyTorch using the DistDL distributed deep learning framework, which is capable of scaling FNOs to arbitrary dimension and input size running on many nodes in a distributed memory system. This parallel implementations allows FNOs to be trained and run on problems of a practical scale on both CPU and GPU clusters.},
  keywords = {ML4SEISMIC, SLIM, Fourier neural operators, deep learning, CCS},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/grady2021ML4SEISMICdfn/Tue-12-10-Grady.pdf}
}

%----- 2020 -----%

@PRESENTATION{siahkoohi2020SEGCHAPTERGTdlbuq,
  title = {A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification},
  booktitle = {GT SEG Student Chapter},  
  year = {2020},
  abstract = {Uncertainty quantification is essential when dealing with
ill-conditioned inverse problems due to the inherent nonuniqueness of the
solution. Bayesian approaches allow us to determine how likely an estimation
of the unknown parameters is via formulating the posterior distribution.
Unfortunately, it is often not possible to formulate a prior distribution
that precisely encodes our prior knowledge about the unknown. Furthermore,
adherence to handcrafted priors may greatly bias the outcome of the Bayesian
analysis. To address this issue, we propose to use the functional form of a
randomly initialized convolutional neural network as an implicit structured
prior, which is shown to promote natural images and excludes images with
unnatural noise. In order to incorporate the model uncertainty into the final
estimate, we sample the posterior distribution using stochastic gradient
Langevin dynamics and perform Bayesian model averaging on the obtained
samples. Our synthetic numerical experiment verifies that deep priors
combined with Bayesian model averaging are able to partially circumvent
imaging artifacts and reduce the risk of overfitting in the presence of
extreme noise. Finally, we present pointwise variance of the estimates as a
measure of uncertainty, which coincides with regions that are more difficult
to image.},
  keywords = {deep learning, seismic imaging, stochastic gradient Langevin dynamics, uncertainty quantification},
  note = {(SEG Student Chapter)},
  url = {https://slim.gatech.edu/Publications/Public/Lectures/SEGCHAPTERGT/2020/siahkoohi2020SEGCHAPTERGTdlbuq/siahkoohi2020SEGCHAPTERGTdlbuq.pdf},
  author = {Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann}
}

%----- 2019 -----%

@PRESENTATION{Herrmann2019SEGDL,
  title = {Sometimes it pays to be cheap – Compressive time-lapse seismic data acquisition},
  booktitle = {SEG Distinguished Lecture},  
  organization = {Society of Exploration Geophysicists},
  year = {2019},
  abstract = {During these times of sustained low oil prices, it is essential to look for new innovative ways to collect (time-lapse) seismic data at reduced costs and preferably also at reduced environmental impact. By now, there is an increasing body of corroborating evidence — whether these are simulated case studies or actual acquisitions on land and marine — that seismic acquisition based on the principles of compressive sensing delivers on this premise by removing the need to acquire replicated dense surveys. Up to ten-fold increases in acquisition efficiency have been reported by industry while there are indications that this breakthrough is only the beginning of a paradigm shift where full-azimuth time-lapse processing will become a reality. To familiarize the audience with this new technology, I will first describe the basics of compressive sensing, how it relates to missing-trace interpolation and simultaneous source acquisition, followed by how this technology is driving innovations in full-azimuth (time-lapse) acquisition, yielding high-fidelity data with a high degree of repeatability and at a fraction of the costs.},
  keywords = {presentation, Compressive Sensing, Time-lapse Marine Acquisition},
  note = {(SEG Distinguished Lecture)},
  url = {https://slim.gatech.edu/Publications/Public/Lectures/SEG-DL/2019/Herrmann2019SEGDL/Herrmann2019SEGDL/},
  url2 = {https://slim.gatech.edu/Publications/Public/Lectures/SEG-DL/2019/Herrmann2019SEGDL/Herrmann2019SEGDL.pdf},
  author = {Felix J. Herrmann}
}

@PRESENTATION{witte2019HOTCSEdsagip,
  title = {Domain-specific abstractions for large-scale geophysical inverse problems},
  booktitle = {HotCSE Seminar},  
  year = {2019},
  abstract = {During these times of sustained low oil prices, it is essential to look for new innovative ways to collect (time-lapse) seismic data at reduced costs and preferably also at reduced environmental impact. By now, there is an increasing body of corroborating evidence — whether these are simulated case studies or actual acquisitions on land and marine — that seismic acquisition based on the principles of compressive sensing delivers on this premise by removing the need to acquire replicated dense surveys. Up to ten-fold increases in acquisition efficiency have been reported by industry while there are indications that this breakthrough is only the beginning of a paradigm shift where full-azimuth time-lapse processing will become a reality. To familiarize the audience with this new technology, I will first describe the basics of compressive sensing, how it relates to missing-trace interpolation and simultaneous source acquisition, followed by how this technology is driving innovations in full-azimuth (time-lapse) acquisition, yielding high-fidelity data with a high degree of repeatability and at a fraction of the costs.},
  keywords = {software, julia, large-scale, inversion, full-waveform-inversion, imaging},
  note = {(HotCSE)},
  url = {https://slim.gatech.edu/Publications/Public/Lectures/HotCSE/2019/witte2019HOTCSEdsagip/witte2019HOTCSEdsagip.pdf},
  author = {Philipp A. Witte and Mathias Louboutin and Felix J. Herrmann}
}

@PRESENTATION{herrmann2019HOTCSEliwcuq,
  title = {Learned imaging with constraints and uncertainty quantification},
  booktitle = {HotCSE Seminar},  
  year = {2019},
  abstract = {We outline new approaches to incorporate ideas from convolutional networks into wave-based least-squares imaging. The aim is to combine hand-crafted constraints with deep convolutional networks allowing us to directly train a network capable of generating samples from the posterior. The main contributions include combination of weak deep priors with hard handcrafted constraints and a possible new way to sample the posterior.},
  keywords = {HotCSE, Uncertainty quantification, Deep Learning, Imaging, Expectation Maximization},
  note = {(HotCSE)},
  url = {https://slim.gatech.edu/Publications/Public/Lectures/HotCSE/2019/herrmann2019HOTCSEliwcuq/herrmann2019HOTCSEliwcuq.pdf},
  author = {Felix J. Herrmann and Ali Siahkoohi and Gabrio Rizzuti}
}

%----- 2017 (FALL) -----%

@PRESENTATION{alfaraj2017SINBADFros,
  title = {Reconstruction of S-waves from low-cost randomized acquisition},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {Due to the lower shear wave velocity compared with compressional waves, finer spatial sampling is required to properly record the earlier according to the Nyquist sampling criterion. To avoid higher acquisition costs and to utilize the multicomponent data to its available full extent, we propose acquiring randomly undersampled ocean bottom seismic data. We present two up- and down-going shear wave reconstruction methods: (i) rank minimization reconstruction followed by elastic wavefield decomposition, and (ii) sparsity promoting joint interpolation decomposition using all the multicomponent data in one optimization problem.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/alfaraj2017SINBADFros/alfaraj2017SINBADFros.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/alfaraj2017SINBADFros/alfaraj2017SINBADFros.mov},
  author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}

@PRESENTATION{daskalakis2017SINBADFsof,
  title = {Stochastic Optimization from the perspective of dynamical systems},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {We present improvements to a family of methods (Linearized Bregman, Kaczmarz and Stochastic Gradient Descent) that are often use in optimization problems. We explain the link of those methods with dynamical systems and we draw ideas for improving their performance. We use a simple idea to improve the stability and the performance of our family of optimization methods especially for ill-posed, inconsistent large-scale problems. Finally we present an application at a least squares migration problem, which highlight the importance of the suggested improvements on large scale Geophysical problems.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/daskalakis2017SINBADFsof/daskalakis2017SINBADFsof.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/daskalakis2017SINBADFsof/daskalakis2017SINBADFsof.m4v},
  author = {Emmanouil Daskalakis and Rachel Kuske and Mengmeng Yang and Felix J. Herrmann}
}

@PRESENTATION{fang2017SINBADFpfg,
  title = {PDE-free Gauss-Newton Hessian for Wavefield Reconstruction Inversion},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {In this work, we present a PDE-free Gauss-Newton Hessian for Wavefield Reconstruction Inversion. With this PDE-free Gauss-Newton Hessian, we can compute matrix-vector products without additional PDE solves. Thus, we are able to use the second order optimization method Gauss-Newton method with a roughly equal computational cost of first-order methods such as the gradient-descent method.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/fang2017SINBADFpfg/fang2017SINBADFpfg.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/fang2017SINBADFpfg/fang2017SINBADFpfg.mov},
  author = {Zhilong Fang and Felix J. Herrmann}
}

@PRESENTATION{graff2017SINBADFlrp,
  title = {Low-rank representation of omnidirectional subsurface extended image volumes},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {Extended image volumes are an important migration tool in seismic exploration. However the computation and the storage of omnidirectional subsurface extended image volumes are usually prohibitive. That is why some solutions have been already proposed for instance by focusing on horizontal offsets only. In our work, we will consider a linear algebra approach to deal with the low-rank representation of extended image volumes with full offsets. We will never build entirely the resulting matrix but get only actions of it on well-chosen probing vectors, based on Low-Rank decomposition or randomized SVD. This representation allows us to have access to all the energy of the extended image volume matrix and still limits the storage of the information and the computational cost.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/graff2017SINBADFlrp/graff2017SINBADFlrp.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/graff2017SINBADFlrp/graff2017SINBADFlrp.mov},
  author = {Marie Graff-Kray and Rajiv Kumar and Felix J. Herrmann}
}

@PRESENTATION{herrmann2017SINBADFhrc,
  title = {Highly repeatable 3D compressive full-azimuth towed-streamer time-lapse acquisition –- a numerical feasibility study at scale},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {Most conventional 3D time-lapse (or 4D) acquisitions are ocean-bottom cable (OBC) or ocean-bottom node (OBN) surveys since these surveys are relatively easy to replicate compared to towed-streamer surveys. To attain high degrees of repeatability, survey replicability and dense periodic sampling has become the norm for 4D surveys that renders this technology expensive. Conventional towed-streamer acquisitions suffer from limited illumination of subsurface due to narrow azimuth. Although, acquisition techniques such as multi-azimuth, wide-azimuth, rich-azimuth acquisition, etc., have been developed to illuminate the subsurface from all possible angles, these techniques can be prohibitively expensive for densely sampled surveys. This leads to uneven sampling, i.e., dense receiver and coarse source sampling or vice-versa, in order to make these acquisitions more affordable. Motivated by the design principles of Compressive Sensing (CS), we acquire economic, randomly subsampled (or compressive) and simultaneous towed-streamer time-lapse data without the need of replicating the surveys. We recover densely sampled time-lapse data on one and the same periodic grid by using a joint-recovery model (JRM) that exploits shared information among different time-lapse recordings, coupled with a computationally cheap and scalable rank-minimization technique. The acquisition is low cost since we have subsampled measurements (about 70% subsampled), simulated with a simultaneous long-offset acquisition configuration of two source vessels travelling across a survey area at random azimuths. We analyze the performance of our proposed compressive acquisition and subsequent recovery strategy by conducting a synthetic, at scale, seismic experiment on a 3D time-lapse model containing geological features such as channel systems, dipping and faulted beds, unconformities and a gas cloud. Our findings indicate that the insistence on replicability between surveys and the need for OBC/OBN 4D surveys can, perhaps, be relaxed. Moreover, this is a natural next step beyond the successful CS acquisition examples discussed during this session.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/herrmann2017SINBADFhrc/herrmann2017SINBADFhrc.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/herrmann2017SINBADFhrc/herrmann2017SINBADFhrc.mov},
  author = {Felix J. Herrmann and Rajiv Kumar and Haneet Wason and Shashin Sharan and Felix Oghenekohwo}
}

@PRESENTATION{herrmann2017SINBADFofp,
  title = {Overview & {Future} {Plans} {SINBAD} {Consortium}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/herrmann2017SINBADFofp/herrmann2017SINBADFofp.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/herrmann2017SINBADFofp/herrmann2017SINBADFofp.mov},
  author = {Felix J. Herrmann}
}

@PRESENTATION{kumar2017SINBADFfas,
  title = {Full-azimuth seismic data processing w/ coil acquisition},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {In this work, we will demonstrate the performance of our in-house 5D low-rank based interpolation method on a seismic data acquired using coil shooting full-azimuth acquisition. We will show that we can recover full-azimuthal interpolated data from highly subsampled data, where the subsampling ratio is 4%. This is the first time, we are testing our interpolation ideas on real 3D marine seismic data acquisition. Our findings show that we can avoid the general practice of windowing the data while performing the interpolation, specially using rank-minimization based framework.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFfas/kumar2017SINBADFfas.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFfas/kumar2017SINBADFfas.mov},
  author = {Rajiv Kumar and Nick Moldoveanu and Keegan Lensink and Felix J. Herrmann}
}

@PRESENTATION{kumar2017SINBADFmdt,
  title = {Multi-domain target-oriented imaging using extreme-scale matrix factorization},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {In this work, we present an alternative approach to redatum both source and receivers at depth, under the framework of reflectivity-based extended images with two-way wave propagation in the background medium. We propose a randomized svd based probing scheme that takes advantage of the algebraic structure of the extended imaging system to overcome the computational cost and memory usage associated with the number of wave-equation solutions and explicit storage employed by conventional migration methods. Experimental results on complex geological models demonstrate the efficacy of proposed methodology in performing multi-domain target imaging.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFmdt/kumar2017SINBADFmdt.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFmdt/kumar2017SINBADFmdt.mov},
  author = {Rajiv Kumar and Marie Graff-Kray and Ivan Vasconcelos and Felix J. Herrmann}
}

@PRESENTATION{lopez2017SINBADFagf,
  title = {A Guide for Successful Low-Rank Matrix Recovery in Seismic Applications},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {This talk presents recent results in the theory of low-rank matrix recovery as heuristics for seismic practitioners. In the theory of matrix completion, we discuss the spectral gap as a means to quantify how successful a given sub sampling scheme will be for trace interpolation. Additionally, we consider previously proposed random sampling techniques and develop conditions on the sampling distribution that guarantees successful low-rank matrix recovery. The results apply to time-jittered acquisition, off-the-grid trace interpolation and source separation for simultaneous towed-streamer marine acquisition. Put together, the talk provides practical instruments that help design acquisition schemes in favor of rank penalization techniques.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFagf/lopez2017SINBADFagf.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFagf/lopez2017SINBADFagf.mov},
  author = {Oscar Lopez and Rajiv Kumar}
}

@PRESENTATION{lopez2017SINBADFmci,
  title = {Matrix Completion in Parallel Architectures: Julia Implementation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {Matrix completion techniques offer potential tools for frugal seismic data acquisition, where dense acquisition is replaced by optimization. This shift of focus means that efficient numerical methods are critical for the implementation of these techniques in large-scale seismic applications. To this end, this talk modifies rank-penalization methodologies to suit parallel architectures. By adopting factorization-based alternating minimization schemes, each program can be decoupled into independent sub-problems handled in parallel. We showcase a distributed parallel execution in Julia and explore the scalability of the approach.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFmci/lopez2017SINBADFmci.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFmci/lopez2017SINBADFmci.mov},
  author = {Oscar Lopez and Keegan Lensink and Rajiv Kumar and Henryk Modzelewski}
}

@PRESENTATION{louboutin2017SINBADFddg,
  title = {Data driven Gradient Sampling for seismic inversion},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {We present in this work an extension of the Gradient Sampling algorithm presented at the last EAGE in Paris. We previously showed the potential of this algorithm playing with implicit time-shifts to represent the wavefield of a slightly perturbed velocity model. We introduce an extension where the weights of the Gradient Sampling algorithm are obtained with the solve of data-based quadratic subproblem instead of at random. The update direction is the a more accurate representation of the true Gradient Sampling update direction.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFddg/louboutin2017SINBADFddg.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFddg/louboutin2017SINBADFddg.mov},
  author = {Mathias Louboutin and Felix J. Herrmann}
}

@PRESENTATION{louboutin2017SINBADFldi,
  title = {Latest developments in Devito},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {We present an overview of the latest developments in Devito. We introduced Devito in the previous meeting as a prototype finite-difference DSL for seismic modelling and inversion. We are presenting here the latest improvements and functionalities of Devito. We will also discuss the current future plans as well as non-supported features that the audience may be interested in. This presentation will be followed by/mixed with a hands-in tutorial if the time and resources allows it.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFldi/louboutin2017SINBADFldi.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFldi/louboutin2017SINBADFldi.mov},
  author = {Mathias Louboutin and Michael Lange and Fabio Luporini and Navjot Kurjeka and Jan Hueckelheim and Gerard Gorman and Philipp A. Witte and Felix J. Herrmann}
}

@PRESENTATION{peters2017SINBADFaaj,
  title = {Algorithms and Julia software for FWI with multiple constraints},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {We present a framework to add multiple convex and non-convex constraints to nonlinear inverse problems, specifically FWI. The constraints mitigate problems related to noisy data, artifacts arising from working with very few simultaneous sources, inaccurate starting models and using approximate physical forward models. Compared to earlier work at SLIM, the current framework is algorithmically simpler and computationally more efficient. We show examples where the model estimation is improved when we use very limited prior knowledge directly as constraints. We also present the software implementation in Julia and how it is used together with other software that compute data-misfit values and gradients w.r.t. the model parameters.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/peters2017SINBADFaaj/peters2017SINBADFaaj.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/peters2017SINBADFaaj/peters2017SINBADFaaj.m4v},
  author = {Bas Peters and Felix J. Herrmann}
}

@PRESENTATION{sharan2017SINBADFtts,
  title = {Tracking the spatial-temporal evolution of fractures by microseismic source collocation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {Unlike conventional reservoirs, unconventional plays are not naturally viable for economical production of oil and gas. They require stimulation by injecting high-pressure fluid causing fractures in the rocks. These fractures make the medium more permeable, hence, the extraction of oil and gas becomes feasible. For drilling purposes and to prevent potentially hazardous situations, we need to have good knowledge of the location of these fractures. Also, we need to have good knowledge about how these fractures originated in time. Hydraulic fracturing changes stress in rocks, which results in the emission of microseismic waves. The opening of cracks due to high pressure fluid injection during hydraulic fracturing mainly causes this change in stress in the rocks. Therefore, microseismic events are mostly localized along these fractures and have finite energy along time. To accurately track the evolution of fractures in both space and time, we need to locate closely spaced microseismic events along these fractures activating at very small time intervals. A naive approach can be the back propagation of the observed data to find out a point in space and time where maximum focusing of back propagating energy occurs. This point corresponds to the location and origin time of a microseismic source. This approach, although simpler, suffers from low resolution and requires scanning of complete 4D volume (3D in space and 1D in time). Hence, this method can be challenging when there are multiple closely spaced microseismic sources originating at different times. We in this work propose a sparsity promotion based method that can locate closely spaced microseismic events, with spatial separation as low as within half a wavelength, activating at small time intervals. We simultaneously estimate the origin time of microseismic events by estimating their source time functions. Our method exploits the fact that microseismic events are localized in space and have finite energy. We use accelerated Linearized Bregman algorithm with a preconditioning operator to arrive at a computationally feasible scheme.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/sharan2017SINBADFtts/sharan2017SINBADFtts.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/sharan2017SINBADFtts/sharan2017SINBADFtts.mov},
  author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}

@PRESENTATION{siahkoohi2017SINBADFsdi,
  title = {Seismic data interpolation with Generative Adversarial Networks},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {In this project we implement an algorithm to predict the missing traces in the seismic shot gathers. The missing traces can be either regular or irregular. Any interpolation scheme assumes a prior knowledge on the data. Here the prior information used to interpolate the data is obtained from interaction of two trained deep neural networks, namely Generator and Discriminator. The combination of these two neural networks is called Generative Adversarial Network (GAN). GAN is trained on finely sampled seismic shot gathers. By employing the trained GAN we can project shot gathers with missing traces into the domain of the generator network. Then by computing the output of generator given the found projection, we can fill in the initial gather.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/siahkoohi2017SINBADFsdi/siahkoohi2017SINBADFsdi.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/siahkoohi2017SINBADFsdi/siahkoohi2017SINBADFsdi.mov},
  author = {Ali Siahkoohi and Felix J. Herrmann}
}

@PRESENTATION{wang2017SINBADFnra,
  title = {Noise robust and time-domain formulations of Wavefield Reconstruction Inversion},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {We propose a wave-equation-based subsurface inversion method that in many cases is more robust than conventional Full-Waveform Inversion. The new formulation is written in a denoising form that allows the synthetic data to match the observed ones up to a small error. Compared to regular Full-Waveform Inversion, our method treats the noise arising from the data meassuring/recording process and that from the synthetic modelling process separately. Compared to Wavefields Reconstruction Inversion, the new formulation mitigates the difficulty of choosing the penalty parameter λ. To solve the proposed optimization problem, we develop an efficient frequency domain algorithm that alternatively updates the model and the data. Numerical experiments confirm strong stability of the proposed method by comparisons between the results of our algorithm with that from both plain FWI and a weighted formulation of the FWI. We also discuss a new memory efficient time-domain formulation for Wavefield Reconstruction Inversion based on duality.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/wang2017SINBADFnra/wang2017SINBADFnra.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/wang2017SINBADFnra/wang2017SINBADFnra.mov},
  author = {Rongrong Wang and Mathias Louboutin and Bas Peters and Emmanouil Daskalakis and Felix J. Herrmann}
}

@PRESENTATION{witte2017SINBADFals,
  title = {A large-scale framework in Julia for fast prototyping of seismic inversion algorithms},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {We present our progress on a large-scale seismic modeling workflow in Julia for wave-equation based inversion. The software offers a range of high-level abstractions to easily express PDE constrained optimization problems in terms of linear algebra expressions, while utilizing the DSL Devito to symbolically express the underlying PDEs and to generate fast and parallel code for solving them. Data containers and linear operators can be set up without much effort from input SEG-Y data and scale to large-scale 3D applications. This talk provides an overview of the basic functionalities of our software and applications to least squares imaging and 3D FWI.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/witte2017SINBADFals/witte2017SINBADFals.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/witte2017SINBADFals/witte2017SINBADFals.mov},
  author = {Philipp A. Witte and Mathias Louboutin and Felix J. Herrmann}
}

@PRESENTATION{yang2017SINBADFiwm,
  title = {Imaging with multiples in shallow water},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {Based on the latest developments of research in inversion technology with optimization, researchers have made significant progress in the implementation of least-squares reverse-time migration (LS-RTM) of primaries. In Marine data however, these applications rely on the success of a pre-imaging separation of primaries and multiples, which can be modeled as a multi-dimensional convolution between the vertical derivative of the surface-free Green’s function and the down-going receiver wavefield. Instead of imaging the primaries and multiples separately, we implement the LS-RTM of the total down-going wavefield by combining areal source injection and linearized Born modelling, where strong surface related multiples are generated from a strong density variation at the ocean bottom. The advantage including surface related multiples in LS-RTM is the extra illumination we obtain from these multiples without incurring additional computational costs related to carrying out multi-dimensional convolutions part of conventional multiple prediction procedures. Even though we are able to avert these computational costs, our approach shares the large costs of LS-RTM. We reduce these costs by combining randomized source subsampling with our sparsity-promoting imaging technology, which produces artifact-free, high-resolution images, with the surface-related multiples migrated properly.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/yang2017SINBADFiwm/yang2017SINBADFiwm.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/yang2017SINBADFiwm/yang2017SINBADFiwm.mov},
  author = {Mengmeng Yang and Emmanouil Daskalakis and Felix J. Herrmann}
}

@PRESENTATION{zhang2017SINBADFmsd,
  title = {Massive seismic data compression & recovery w/ on-the-fly data extraction},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2017},
  abstract = {Industrial seismic exploration has moved towards complex geological areas, which requires typically long-offset and dense sampling data in order to avoid aliasing and inaccuracy in wave-equation based inversion algorithms. These strict requirements lead to massive data volume size and prohibitive demands on computational resources. In this work, we propose to compress our dense data in hierarchical Tucker tensor format by exploiting the low-rank structure of the data in a transformed domain. Then, we devise on-the-fly common shot or receiver gather extraction directly via the highly compressed factors. In subsampling scenarios, by interpolating this novel tensor format, we can also reconstruct the shot or receiver gather on a per-query basis rather than expanding the data to its fully-sampled form. We demonstrate the effective performance of our proposed technique on 3D stochastic full-waveform inversion, which allows the stochastic algorithm to extract shot gathers as it requires them throughout the inversion process. Moreover, we finally show how to computational effectively generate the CIGs from this compressed low-rank tensor representation of the data with the help of fast simultaneous shot or receiver gather generation.},
  keywords = {presentation, SINBAD, SINBADFALL2017, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/zhang2017SINBADFmsd/zhang2017SINBADFmsd.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/zhang2017SINBADFmsd/zhang2017SINBADFmsd.mov},
  author = {Yiming Zhang and Curt Da Silva and Rajiv Kumar and Felix J. Herrmann}
}

%----- 2016 (FALL) -----%

@PRESENTATION{bougher2016SINBADFaaa,
  title = {Amplitude vs. angle analysis as an unsupervised learning problem},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {Amplitude vs. angle analysis (AVA) of pre-stack seismic
                  data is a commonly used method for inferring
                  petrophysical information from seismic
                  data. Conventionally, a two-term linearized rock
                  physics model (Shuey equation) is used to invert
                  angle-domain common-image gathers. Multivariate
                  analysis of the inverted terms leads to a background
                  of siliciclastic interfaces, where outlying points
                  are associated with hydrocarbon saturated sands.
                  The acquisition and processing of seismic data does
                  not result in highly-calibrated measurements that
                  adhere to the rock physics model, which often
                  inhibits the success of AVA analysis. We offer an
                  alternative approach that uses PCA-based methods to
                  learn projections directly from the data without the
                  need of a physical model. Results on synthetic and
                  field data show that PCA-based projections can
                  improve segmentation of potential reservoirs in
                  seismic data.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/bougher2016SINBADFaaa/bougher2016SINBADFaaa.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/bougher2016SINBADFaaa/bougher2016SINBADFaaa.mov},
  author = {Ben B. Bougher and Felix J. Herrmann}
}


@PRESENTATION{dasilva2016SINBADFccs,
  title = {Composite convex smooth optimization with seismic data processing applications},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {In this work, we show a general technique for solving
                  optimization problems that are comprised of
                  minimizing a composition of a convex, non-smooth
                  function with a smooth function. We demonstrate this
                  technique in the seismic data processing context
                  applied to robust missing trace interpolation in the
                  low-rank Hierarchical Tucker tensor format as well
                  as cosparsity-based missing trace interpolation.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/dasilva2016SINBADFccs/dasilva2016SINBADFccs.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/dasilva2016SINBADFccs/dasilva2016SINBADFccs.mov},
  author = {Curt Da Silva and Felix J. Herrmann}
}


@PRESENTATION{dasilva2016SINBADFuse,
  title = {A unified {2D}/{3D} software environment for large scale time-harmonic full waveform inversion},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {Full Waveform Inversion is a costly and complex
                  procedure, resulting in industrial codebases that
                  are often written entirely in low-level languages,
                  making them hard to understand, maintain, improve,
                  and extend. In this work, we propose a software
                  framework for organizing a 3D FWI environment that
                  helps mitigate design complexities inherent in the
                  problem in a straightforward way while delegating
                  the performance-critical portions to low level
                  languages to maintain efficiency. The result is a
                  software framework that has a unified interface for
                  2D and 3D and is flexible, efficient, scalable, and
                  demonstrably correct. We demonstrate the
                  effectiveness of this approach on a large scale 3D
                  FWI example.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/dasilva2016SINBADFuse/dasilva2016SINBADFuse.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/dasilva2016SINBADFuse/dasilva2016SINBADFuse.mov},
  author = {Curt Da Silva and Felix J. Herrmann}
}


@PRESENTATION{fang2016SINBADFeaq,
  title = {Efficient approach for quantifying uncertainty of wavefield reconstruction inversion},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {Due to the noisy measurements, sparse observations,
                  uncertain forward models, and uncertain prior
                  parameter information, significant uncertainty
                  arises in the inverted velocity model of
                  full-waveform inversion. The uncertain velocity
                  model leads to the uncertainty in the correct
                  positions of events in the migrated images, which
                  subsequently influences the important
                  decision-makings in the oil and gas exploration and
                  production (E&P) business such as the drilling
                  decisions and economic evaluations. Therefore,
                  understanding the uncertainty in the inverted
                  velocity model is essential for the mitigation of
                  the E&P risks. A common approach to accounting for
                  uncertainty in seismic inverse problems like
                  full-waveform inversion is to describe the unknown
                  parameters in probabilistic by the Bayesian
                  inference. The Bayesian inference aims at
                  incorporating the information from the data,
                  physics, and one’s prior knowledge to formulate a
                  posterior distribution function to characterize the
                  statistical properties of the unknown
                  parameters. However, characterizing uncertainties
                  for the large-scale full-waveform inversion in
                  seismic exploration is prohibitively expensive since
                  it requires thousands of function evaluations to
                  sample the parameter space (e.g. via Markov chain
                  Monte Carlo sampling). Apart from this, the existing
                  methods of uncertainty quantification for
                  full-waveform inversion is through applying the
                  Bayesian inference to the standard method such as
                  the adjoint-state method with the assumption that no
                  uncertainty arises from the wave-equation. However,
                  the wave-equation may not be able to reflect all the
                  physics, hence, incorporating uncertainty from
                  wave-equation might benefit the quantification of
                  uncertainty. In this work, we propose to perform
                  uncertainty quantification using the recently
                  proposed wavefield reconstruction inversion
                  technique, which gives an appropriate framework to
                  tackle the uncertainty in wave-equation solve by
                  adding the misfit of the wave-equation to the
                  likelihood distribution and relaxing the
                  wave-equation constraint. Moreover, we propose a
                  computationally feasible approach to analyzing the
                  posterior distribution of the wavefield
                  reconstruction inversion method, which incorporates
                  a Gaussian distribution that approximates the
                  posterior distribution with an optimization-driven
                  Gaussian simulator to sample the Gaussian
                  distribution efficiently. Numerical examples show
                  that comparing to the McMC method, our approach is
                  able to produce comparable results with less
                  computational cost. This is joint work with Curt da
                  Silva and Rachel Kuske.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/fang2016SINBADFeaq/fang2016SINBADFeaq.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/fang2016SINBADFeaq/fang2016SINBADFeaq.mov},
  author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Tristan van Leeuwen and Felix J. Herrmann and Rachel Kuske}
}


@PRESENTATION{gorman2016SINBADFopp,
  title = {Open {Performance} {portablE} {SeismiC} {Imaging}---{OPESCI}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {In this project, we introduce OPESCI-FD, a Python
                  package built on symbolic mathematics to
                  automatically generate Finite Difference models from
                  a high-level description of the model equations. We
                  investigate applying this framework to generate the
                  propagator program used in seismic imaging. We
                  implement the 3D acoustic and anisotropic FD scheme
                  as an example and demonstrate the advantages of
                  usability, flexibility and accuracy of the
                  framework. The design of OPESCI-FD aims to allow
                  rapid development, analysis and optimisation of
                  Finite Difference programs. OPESCI-FD is the
                  foundation for continuing development by the OPESCI
                  project team, building on the research presented in
                  this report. This talk concludes by reviewing the
                  further developments that are already under way, as
                  well as the scope for extension to cater for other
                  equations and numerical schemes. This joint work
                  with SINBAD and SENAI CIMATEC and has received
                  additional funding from Intel.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM, private},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/gorman2016SINBADFopp/gorman2016SINBADFopp.pdf},
  url2 = {https://slim.gatech.edu/Publications/Private/Conferences/SINBAD/2016/Fall/gorman2016SINBADFopp/gorman2016SINBADFopp.mov},
  author = {Gerard Gorman and Marcos de Aguiar and David Ham and Felix J. Herrmann and Paul H. J. Kelly and Navjot Kukreja and Michael Lange and Mathias Louboutin and Fabio Luporini and Paulius Velesko and Vincenzo Pandolfo and Felippe Vieira Zacarias}
}


@PRESENTATION{herrmann2016SINBADFcvp,
  title = {Constraints versus penalties for edge-preserving full-waveform inversion},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {Full-waveform inversion is challenging in complex
                  geological areas. Even when provided with an
                  accurate starting model, the inversion algorithms
                  often struggle to update the velocity
                  model. Contrary to other areas in applied
                  geophysics, including prior information in
                  full-waveform inversion is still relatively in its
                  infancy. In part this is due to the fact that
                  incorporating prior information that relates to
                  geological settings where strong discontinuities in
                  the velocity model dominate is difficult, because
                  these settings call for non-smooth
                  regularizations. We tackle this problem by including
                  constraints on the spatial variations and value
                  ranges of the inverted velocities, as opposed to
                  adding penalties to the objective as is more
                  customary in main stream geophysical inversion. By
                  demonstrating the lack of predictability of
                  edge-preserving inversion when the regularization is
                  in the form of an added penalty term, we advocate
                  the inclusion of constraints instead. Our examples
                  show that the latter lead to more predictable
                  results and to significant improvements in the
                  delineation of Salt bodies. This is joint work with
                  Bas Peters.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/herrmann2016SINBADFcvp/herrmann2016SINBADFcvp.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/herrmann2016SINBADFcvp/herrmann2016SINBADFcvp.mov},
  author = {Felix J. Herrmann and Bas Peters}
}


@PRESENTATION{herrmann2016SINBADFwom,
  title = {Welcome & {Overview} of the {Meeting}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/herrmann2016SINBADFwom/herrmann2016SINBADFwom.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/herrmann2016SINBADFwom/herrmann2016SINBADFwom.mov},
  author = {Felix J. Herrmann}
}


@PRESENTATION{kumar2016SINBADFels,
  title = {Efficient large-scale {5D} seismic data acquisition and processing using rank-minimization},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {Seismic data collection is becoming challenging because
                  of increased demands for high-quality, long-offset
                  and wide-azimuth data. Leveraging ideas from CS, in
                  this work we establish a cost effective acquisition
                  and processing techniques, which are no longer
                  dominated by survey area size but by the sparsity of
                  seismic data volumes. In the first part of abstract,
                  we establish connections between random time
                  dithering and jittered sampling in
                  space. Specifically, we recover high-quality 5D
                  seismic data volumes from time-jittered marine
                  acquisition where the average inter-shot time is
                  significantly reduced, leading to cheaper surveys
                  due to fewer overlapping shots. The time-jittered
                  acquisition, in conjunction with the shot separation
                  by Singular-Value Decomposition (SVD)-free
                  factorization based rank-minimization approach,
                  allows us to recover high quality 5D seismic data
                  volumes. Results are illustrated for simulations of
                  simultaneous time-jittered continuous recording for
                  a 3D ocean-bottom cable survey, where we outperforms
                  existing techniques, by an order of magnitude
                  computational speedup and using 1/20th of the
                  memory, that use sparsity in transforms domains. The
                  second part of abstract focussed on leveraging
                  low-rank structure in seismic data to solve
                  extremely large data recovery (interpolation)
                  problems. We introduced a large–scale SVD-free
                  optimization framework that is robust with respect
                  to outliers and that uses information on the
                  support. We test the efficacy of the proposed
                  interpolation framework on a large-scale 5D seismic
                  data, generated from the geologically complex
                  synthetic 3D Compass velocity model, where 80\% of
                  the data has been removed. Our findings show that
                  major computational and memory gains are possible
                  compared to curvelet-based reconstruction.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFels/kumar2016SINBADFels.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFels/kumar2016SINBADFels.mov},
  author = {Rajiv Kumar and Shashin Sharan and Haneet Wason and Felix J. Herrmann}
}


@PRESENTATION{kumar2016SINBADFlrm,
  title = {Low-rank methods for on-the-fly slicing & dicing of seismic data & image volumes},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {Conventional oil and gas fields are increasingly
                  difficult to explore and produce, which calls for
                  more complex wave-equation based inversion (WEI)
                  algorithms that require dense long-offset
                  samplings. These requirements result in an
                  exponential growth in data volumes and prohibitive
                  demands on computational resources. In this work, we
                  propose a fast, resilient, and scalable
                  wave-equation based inversion methodology for both
                  the data and image-domain, which can handle
                  complicated wave physics. First we show that both
                  the data and image domains exhibit low-rank
                  structure in a transform-domain, which can be
                  exploited to compress the dense data or image
                  volumes. Then, by accessing information from the
                  compressed volumes on-the fly, we devise a scalable
                  computational inversion framework driven by gradient
                  calculations, which works with small subsets of
                  source experiments. In the full-waveform inversion
                  context, we demonstrate the efficacy of low-rank
                  interpolation to improve downstream inversion
                  results compared to merely inverting the velocity
                  model using the subsampled data volume directly. We
                  demonstrate the effectiveness of the proposed
                  framework on two different waveform-inversion
                  formulations, specifically performing full-waveform
                  inversion on 5D data set generated using the
                  overthrust model, and wave-equation based migration
                  velocity analysis on the Marmousi model. This is
                  joint work with Curt Da Silva and Yiming Zhang.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFlrm/kumar2016SINBADFlrm.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFlrm/kumar2016SINBADFlrm.mov},
  author = {Rajiv Kumar and Curt Da Silva and Yiming Zhang and Felix J. Herrmann}
}


@PRESENTATION{lopez2016SINBADFlrm,
  title = {Low-rank matrix recovery for parallel architectures},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {Low-rank matrix recovery (LRMR) techniques offer
                  potential tools for frugal seismic data acquisition,
                  where dense acquisition is replaced by large-scale
                  optimization. This shift of focus means that
                  efficient numerical methods are critical for
                  successful LRMR implementation in seismic
                  applications. In this talk, we extend the
                  rank-penalization methodology to a parallelizable
                  framework. We adopt a factorization-based
                  alternating minimization scheme and decouple it into
                  an independent system of simpler sub-problems that
                  can be handled in parallel. The methodology is
                  flexible, where the approach can be adapted to the
                  number of workers available. Numerical experiments
                  are conducted to demonstrate that the quality of
                  reconstruction is comparable to existing methods at
                  a fraction of the computational time.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/lopez2016SINBADFlrm/lopez2016SINBADFlrm.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/lopez2016SINBADFlrm/lopez2016SINBADFlrm.mov},
  author = {Oscar Lopez and Rajiv Kumar and Felix J. Herrmann}
}


@PRESENTATION{louboutin2016SINBADFhps,
  title = {High-performance seismic applications of {OPESCI}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {We present our latest geophysical applications built on
                  OPESCI. By using a high-level symbolic API, we allow
                  for fast development and easy implementation of
                  various (acoustic, VT, TTI) wave propagators
                  relevant to exploration geophysics. We start by
                  highlighting possibilities in an acoustic setting
                  including classical operators such as forward
                  modelling and linearised forward (Born) modelling as
                  well as more advanced operators deriving from wave
                  equations with double dipoles and the application of
                  the PDE to a wavefield instead of applying its
                  inverse. We will also show that the performance
                  (time to solution) of this code is on par with
                  industrial software libraries (10\% faster on the
                  full SEAM model). We finally present our
                  implementation of 3D TTI modelling and its adjoint
                  including out comprehensive testing framework. This
                  is joint work with Gerard Gorman},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/louboutin2016SINBADFhps/louboutin2016SINBADFhps.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/louboutin2016SINBADFhps/louboutin2016SINBADFhps.mov},
  author = {Mathias Louboutin and Felix J. Herrmann}
}


@PRESENTATION{oghenekohwo2016SINBADFast,
  title = {Asymmetric sampling for time-lapse surveys},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {In this talk, we use the joint recovery model (JRM),
                  which derives from distributed compressed sensing,
                  to reconstruct time-lapse data acquired via
                  time-jittered marine sources that are significantly
                  subsampled in the monitor survey(s) in comparison to
                  the baseline survey, hence, saving acquisition
                  cost. We analyze the effects of two or more
                  asymmetrically subsampled monitor surveys on
                  wavefield reconstruction via JRM. We leverage the
                  common information shared amongst the vintages
                  during our sparsity-promoting recovery.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/oghenekohwo2016SINBADFast/oghenekohwo2016SINBADFast.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/oghenekohwo2016SINBADFast/oghenekohwo2016SINBADFast.mov},
  author = {Felix Oghenekohwo and Haneet Wason and Felix J. Herrmann}
}


@PRESENTATION{peters2016SINBADFcnc,
  title = {Convex & non-convex constraint sets for full-waveform inversion},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {We extend some of our previous work on constrained
                  formulations of full-waveform inversion. Some
                  motivating examples illustrate why solving
                  constrained problems directly is a simpler approach
                  for solving non-linear inverse problems than
                  penalized problem formulations. So far we used
                  constraints which can be represented as convex sets,
                  for which most optimization theory is
                  developed. Some non-convex counterparts of convex
                  sets are introduced and examples show that the
                  algorithms developed for convex sets also work well
                  with non-convex sets. The motivation to also explore
                  non-convex sets, is that they sometimes translate
                  prior geological knowledge into mathematical
                  constraints in a more direct way. All presented
                  material is available as a user-friendly
                  toolbox. The toolbox works with any code which can
                  provide a data-misfit function value and a gradient
                  direction.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/peters2016SINBADFcnc/peters2016SINBADFcnc.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/peters2016SINBADFcnc/peters2016SINBADFcnc.mov},
  author = {Bas Peters and Felix J. Herrmann}
}


@PRESENTATION{sharan2016SINBADFhrm,
  title = {High resolution microseismic source collocation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {Microseismic waves generated by hydraulic fracturing are
                  used to locate the fractures created in the
                  reservoir. Most of the existing source collocation
                  methods have their limitations in terms of
                  resolution of very closely spaced microseismic
                  events. In this work we propose a method to
                  collocate microseismic events with high resolution
                  along with simultaneous estimation of source time
                  function. In the context of waves, the
                  finite-difference modelling operator acts as an
                  analysis operator for wavefields. This implies that
                  wavefields under the action of a finite-difference
                  modelling kernel can be focused to sources
                  generating these wavefields. We exploit this
                  property of wavefields to locate microseismic events
                  and estimate the source time function jointly. To
                  arrive at a computational feasible scheme, we use
                  the accelerated version of Linearized Bregman (LBR)
                  algorithm to solve the above mentioned problem. This
                  is joint work with Rongrong Wang.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/sharan2016SINBADFhrm/sharan2016SINBADFhrm.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/sharan2016SINBADFhrm/sharan2016SINBADFhrm.mov},
  author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}


@PRESENTATION{wang2016SINBADFtmf,
  title = {Two methods for frequency down-extrapolation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {It is well known that low frequency data are essential
                  to the success of the Full-Waveform Inversion. Field
                  data from a usual marine acquisition, however,
                  typically lack reliable low frequencies due to the
                  existing physical limitations. One research
                  direction focuses on studying low-frequency
                  extrapolation, the process of extending reliable
                  frequency bands of the raw data towards the lower
                  end of the spectrum. We propose two optimization
                  problems, by solving which low frequencies can be
                  made out of the high SNR high frequency
                  component. We present both theoretical explanation
                  and numerical experiments to demonstrate the
                  efficacy as well as limitation of these methods.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wang2016SINBADFtmf/wang2016SINBADFtmf.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wang2016SINBADFtmf/wang2016SINBADFtmf.mov},
  author = {Rongrong Wang and Felix J. Herrmann}
}


@PRESENTATION{wason2016SINBADFlcr,
  title = {Low-cost, randomized {3D} towed-marine time-lapse seismic acquisition},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {Most often conventional 3D towed-marine seismic data
                  acquisition suffers from limited illumination of
                  subsurface due to narrow azimuth. Although,
                  acquisition techniques such as Ocean Bottom Cable
                  (OBC), Ocean Bottom Node (OBN), Rich-Azimuth (RAZ)
                  acquisition, Multi-Azimuth (MAZ) acquisition have
                  been developed to illuminate the subsurface from all
                  possible angles, these acquisition techniques are
                  expensive. Motivated by the design principles of
                  compressed sensing, we acquire randomly subsampled
                  and simultaneous (e.g., simultaneous long offset) 3D
                  towed-marine time-lapse seismic data for few
                  (random) azimuths, and recover densely sampled
                  multiple azimuth baseline and monitor data via
                  sparsity promotion and rank minimization. Our
                  acquisition is low cost since we have subsampled
                  measurements.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wason2016SINBADFlcr/wason2016SINBADFlcr.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wason2016SINBADFlcr/marine_acq_mask.pdf},
  url3 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wason2016SINBADFlcr/wason2016SINBADFlcr.mov},
  author = {Rajiv Kumar and Felix Oghenekohwo and Shashin Sharan and Haneet Wason and Felix J. Herrmann}
}


@PRESENTATION{witte2016SINBADFlst,
  title = {A large-scale time-domain modeling and inversion workflow in {Julia}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {We present our initial steps towards the development of
                  a large-scale seismic modeling workflow in Julia
                  which provides a framework for wave-equation based
                  inversion methods like full waveform inversion or
                  least squares migration. Our framework is based on
                  the Devito tool for optimized finite difference
                  computations that generates highly optimized code
                  from symbolic PDEs. We aim at developing a flexible
                  workflow which is based on abstract operators and
                  allows coding that is “close to the math”, while at
                  the same time relying on extremely fast and
                  efficient, state of the art wave equation solvers.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFlst/witte2016SINBADFlst.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFlst/witte2016SINBADFlst.mov},
  author = {Philipp A. Witte and Felix J. Herrmann}
}


@PRESENTATION{witte2016SINBADFpve,
  title = {Phase velocity error minimizing scheme for the anisotropic pure {P-wave} equation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {Pure P-wave equations for acoustic modeling in
                  transverse isotropic media are derived by
                  approximating the exact pure P-wave dispersion
                  relation. In this work, we present an alternative to
                  the common Taylor expansion based equations, in
                  which we approximate the exact dispersion relation
                  through a polynomial expansion and determine its
                  coefficients by solving a least squares problem that
                  minimizes the phase velocity error over the complete
                  range of phase angles. We show that this approach is
                  up to an order of magnitude more accurate than other
                  pure P-wave equations at a comparable computational
                  cost.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFpve/witte2016SINBADFpve.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFpve/witte2016SINBADFpve.mov},
  author = {Philipp A. Witte and Christiaan C. Stolk and Felix J. Herrmann}
}


@PRESENTATION{witte2016SINBADFtdl,
  title = {Time-domain least-squares {RTM} with sparsity promotion on field data},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {Least squares reverse time migration (LSRTM) is an
                  inversion based imaging algorithm which can surpress
                  acquisition related artifacts and provides images
                  with increased frequency content, decreased noise
                  level and balanced amplitudes. In this work
                  demonstrate our time-domain LSRTM workflow using
                  various pre-conditioners, source-subsampling and
                  sparsity promotion and show its application to the
                  2D Machar field data set.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFtdl/witte2016SINBADFtdl.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFtdl/witte2016SINBADFtdl.mov},
  author = {Philipp A. Witte and Felix J. Herrmann}
}


@PRESENTATION{yang2016SINBADFtds,
  title = {Time-domain sparsity-promoting least-squares migration with source estimation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2016},
  abstract = {Compared to traditional reverse-time migration (RTM),
                  least-squares RTM (LS_RTM) is able to obtain true
                  amplitude images as solutions of $\ell_2$-norm
                  minimization problems by fitting the synthetic and
                  observed reflection data. The shortcoming is that
                  solutions of these $\ell_2$ problems tend to be
                  overfitted and computationally too expensive. By
                  working with randomized subsets of data only, the
                  computational costs of LS-RTM can be brought down to
                  an acceptable level, producing artifact-free
                  high-resolution images. By including on-the-fly
                  source-time function estimation into the method of
                  Linearized Bregman (LB), we tackle the open issues
                  of these "compressive imaging" in the aspects of
                  algorithmic complexity of solver, guaranteed
                  convergence and source estimation. We also
                  inbvestigate whether the algorithm can be
                  accelerated when we combine LB with the Nesterov
                  method. Application of our algorithm on a 2D
                  synthetic shows that we are able to get
                  high-resolution images, with accurate estimates of
                  the wavelet, for one single data pass.},
  keywords = {presentation, SINBAD, SINBADFALL2016, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/yang2016SINBADFtds/yang2016SINBADFtds.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/yang2016SINBADFtds/yang2016SINBADFtds.m4v},
  author = {Mengmeng Yang and Philipp A. Witte and Zhilong Fang and Felix J. Herrmann}
}


%----- 2015 (FALL) -----%

@PRESENTATION{dasilva2015SINBADFnso,
  title = {A new software organization for {3D} {FWI}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {In this work, I have implemented a scalable and
                  extendable framework for 3D Full Waveform Inversion
                  in Matlab. This approach uses modern software design
                  principles to create a codebase that is easy to
                  understand, maintain, and extend and that also
                  allows for rapid prototyping of new algorithmic
                  ideas that can be easily transferred to large scale
                  problems. The code itself is modularized in a proper
                  way, which allows straightforward testing of each
                  component (e.g., Taylor error test, adjoint tests,
                  etc.). Improvements to the computational kernel
                  (i.e., Helmholtz solves with more efficient
                  matrix-vector products, new preconditioners, etc.)
                  propagate to the entire framework.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFnso/dasilva2015SINBADFans.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFnso/dasilva2015SINBADFsdd.pdf},
  url3 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFnso/dasilva2015SINBADFsdd.mov},
  author = {Curt Da Silva and Felix J. Herrmann}
}


@PRESENTATION{dasilva2015SINBADFsss,
  title = {Scaling {SINBAD} software to {3-D} on {Yemoja}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {We present early results on the scalability of SINBAD’s
                  wavefield reconstruction and wave-equation based
                  inversion technologies on Yemoja, a 17k core cluster
                  made available to us by BG Group at SENAI CIMATEC
                  Supercomputing Centre in Brazil.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFsss/dasilva2015SINBADFsss.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFsss/dasilva2015SINBADFsss.mov},
  author = {Curt Da Silva and Haneet Wason and Mathias Louboutin and Bas Peters and Shashin Sharan and Zhilong Fang and Felix J. Herrmann}
}


@PRESENTATION{esser2015SINBADFasd,
  title = {Automatic salt delineation — {Wavefield} {Reconstruction} {Inversion} with convex constraints},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {We extend full-waveform inversion by Wavefield
                  Reconstruction Inversion by including convex
                  constraints on the model. Contrary to the
                  conventional adjoint-state formulations, Wavefield
                  Reconstruction Inversion has the advantage that the
                  Gauss-Newton Hessian is well approximated by a
                  diagonal scaling, which allows us to add convex
                  constraints, such as the box- and the
                  edge-preserving total-variation constraint, on the
                  square slowness without incurring significant
                  increases in computational costs. As the examples
                  demonstrate, including these constraints yields far
                  superior results in complex geological areas that
                  contain high-velocity high-contrast bodies
                  (e.g. salt or basalt). Without these convex
                  constraints, adjoint-state and Wavefield
                  Reconstruction Inversion get trapped in local minima
                  for poor starting models.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFasd/esser2015SINBADFasd.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFasd/esser2015SINBADFasd.mov},
  author = {Ernie Esser and Llu\'{i}s Guasch and Felix J. Herrmann}
}


@PRESENTATION{esser2015SINBADFrsa,
  title = {Resolving scaling ambiguities with the {L1}/{L2} norm in a blind deconvolution problem with feedback},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {Compared to more mundane blind deconvolution problems,
                  blind deconvolution in seismic applications involves
                  a feedback mechanism related to the free surface.
                  The presence of this feedback mechanism gives us an
                  unique opportunity to remove ambiguities that have
                  plagued blind deconvolution for a long time. While
                  beneficial, this feedback by itself is insufficient
                  to remove the ambiguities even with L1
                  constraints. However, when paired with an L1/L2
                  constraint the feedback allows us to resolve the
                  scaling ambiguity under relatively mild
                  assumptions. Inspired by lifting approaches, we
                  propose to split the sparse signal into positive and
                  negative components and apply an \ell_1/\ell_2
                  constraint to the difference, thereby obtaining a
                  constraint that is easy to implement.  Numerical
                  experiments demonstrate robustness to the
                  initialization as well as to noise in the data.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFrsa/esser2015SINBADFrsa.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFrsa/esser2015SINBADFrsa.mov},
  author = {Ernie Esser and Rongrong Wang and Tim T.Y. Lin and Felix J. Herrmann}
}


@PRESENTATION{fang2015SINBADFuqw,
  title = {Uncertainty quantification for {Wavefield}-{Reconstruction} {Inversion} using a positive-definite approximated {Hessian}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {We analyze the Hessian of wavefield reconstruction
                  inversion (WRI) and propose a new approximated
                  Hessian. Instead of requiring PDE solves, the
                  matrix-vector multiplication action of the
                  approximate Hessian can be achieved with several
                  matrix-vector multiplications. The diagonal part of
                  the approximated Hessian can be also calculated
                  without additional PDE solves. We apply this
                  approximated Hessian to uncertainty quantification
                  and obtain statistical parameters such as standard
                  deviation and confidence interval. Numerical example
                  illustrate the accuracy of the estimated Hessian and
                  the feasibility of the method to quantify
                  uncertainties.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFuqw/fang2015SINBADFuqw.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFuqw/fang2015SINBADFuqw.mov},
  author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Felix J. Herrmann and Rachel Kuske}
}


@PRESENTATION{fang2015SINBADFwri,
  title = {Wavefield-reconstruction inversion with source estimation and minimum smoothness constraint – application to the {Chevron} 2014 dataset},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {We present a robust wavefield reconstruction inversion
                  with source estimation. The source wavelet is
                  estimated with the reconstruction of wavefield
                  simultaneously by solving an extended data-augmented
                  problem. We apply this method to the 2014 Chevron
                  synthetic blind test dataset and show the robustness
                  of our method.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFwri/fang2015SINBADFwri.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFwri/fang2015SINBADFwri.mov},
  author = {Zhilong Fang and Bas Peters and Felix J. Herrmann}
}


@PRESENTATION{herrmann2015SINBADFwom,
  title = {Welcome & {Overview} of the {Meeting}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/herrmann2015SINBADFwom/herrmann2015SINBADFwom.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/herrmann2015SINBADFwom/herrmann2015SINBADFwom.mov},
  author = {Felix J. Herrmann}
}


@PRESENTATION{kumar2015SINBADFaoi,
  title = {Affordable omnidirectional image volumes extension to {3D}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {Image gathers are an important tool for velocity
                  analysis, AVA analysis and targeted imaging,
                  however, the computational cost involving in forming
                  the image volumes makes them prohibitively expensive
                  in case of large scale 3D seismic data
                  acquisition. In this work, we propose the extension
                  of our probing techniques from 2D to 3D to form the
                  extended image volumes efficiently. We show the
                  efficacy of proposed formulation on synthetic data
                  set.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/kumar2015SINBADFaoi/kumar2015SINBADFaoi.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/kumar2015SINBADFaoi/kumar2015SINBADFaoi.mov},
  author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}


@PRESENTATION{lin2015SINBADFosd,
  title = {Our student-driven {HPC} environment},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {A major role of academic environments is to provide
                  learning experiences for students to critically
                  analyze and develop methods, both at a high-level of
                  mathematical rigour, and at a low-enough level of
                  implementation in order to yield experimental
                  results on real datasets. Often these two goals are
                  in conflict with each other, in terms of both
                  learning time and attention. At SLIM, we strive to
                  strike a balance between the two by abstracting away
                  many of the low-level aspects of distributed HPC
                  under a framework that matches syntactically with
                  the mathematics of our field, while exposing enough
                  control parameters for tuning performance
                  characteristics. This talk will touch on the history
                  of our efforts at SLIM, and culminate in an overview
                  of our current method of interacting with the
                  in-house compute cluster.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/lin2015SINBADFosd/lin2015SINBADFosd.pdf},
  author = {Tim T.Y. Lin and Felix J. Herrmann}
}


@PRESENTATION{lopez2015SINBADFumc,
  title = {Universal matrix completion: applications to seismic data acquisition},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {This talk discusses the potential applications of
                  universal matrix completion for infill management
                  and acquisition design. We consider recent
                  developments in the theory of matrix completion that
                  produce practical tools to quantify how successful a
                  given sub sampling mask will be for seismic trace
                  interpolation via nuclear norm minimization. This
                  approach provides an on the fly infill management
                  system as well as instruments to design acquisition
                  schemes that favor interpolation via rank
                  penalization techniques.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/lopez2015SINBADFumc/lopez2015SINBADFumc.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/lopez2015SINBADFumc/lopez2015SINBADFumc.mov},
  author = {Oscar Lopez and Haneet Wason and Rajiv Kumar and Felix J. Herrmann}
}


@PRESENTATION{louboutin2015SINBADFess,
  title = {Extending the search space of time-domain adjoint-state {FWI} w/ randomized implicit time shifts},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {We introduce a modified adjoint-state method for time
                  domain FWI that allows us to extend the research
                  space. As a result, we arrive at a formulation where
                  the sensitivity to cycle skipping is reduced. Our
                  method obtains results with the same computational
                  costs as FWI (The PDE solved is the same) but with
                  significantly reduced memory costs. We use new
                  results in non-convex optimization to justify the
                  method as well as new regularization techniques and
                  stochastic optimization to improve the behavior of
                  the algorithm.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/louboutin2015SINBADFess/louboutin2015SINBADFess.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/louboutin2015SINBADFess/louboutin2015SINBADFess.mov},
  author = {Mathias Louboutin and Felix J. Herrmann}
}


@PRESENTATION{oghenekohwo2015SINBADFcst,
  title = {Comparative study of time-lapse {FWI} approaches},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {I will illustrate the performance of our joint recovery
                  model for time-lapse FWI. Specifically, I will
                  compare our method to other conventional methods,
                  namely, parallel difference and sequential
                  difference FWI. I will illustrate the robustness of
                  our method compared to others especially in the
                  presence of missing data caused by large acquisition
                  gaps. In addition, I will assess the performance of
                  these methods in the detectability of the time-lapse
                  change in synthetic data. Analysis of these methods
                  under different starting FWI model will also be
                  highlighted.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/oghenekohwo2015SINBADFcst/oghenekohwo2015SINBADFcst.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/oghenekohwo2015SINBADFcst/oghenekohwo2015SINBADFcst.mov},
  author = {Felix Oghenekohwo and Felix J. Herrmann}
}


@PRESENTATION{oghenekohwo2015SINBADFtli,
  title = {Time-lapse imaging with multiples and distributed compressed sensing},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {In this talk, we leverage our joint-recovery model,
                  which uses the fact that time-lapse images generally
                  have a lot in common, to improve imaging problems
                  with large gaps. We demonstrate that the adverse
                  effects of large acquisition gaps can be mitigated
                  by using this joint-recovery model especially in
                  combination with imaging with surface-related
                  multiples.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/oghenekohwo2015SINBADFtli/oghenekohwo2015SINBADFtli.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/oghenekohwo2015SINBADFtli/oghenekohwo2015SINBADFtli.mov},
  author = {Felix Oghenekohwo and Felix J. Herrmann}
}


@PRESENTATION{peters2015SINBADF3dw,
  title = {3D {WRI}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {The Wavefield Reconstruction Inversion (WRI) approach to
                  seismic inversion is a topic of active research in
                  the SLIM group. Several variants have been proposed,
                  all of which rely on the solution of a linear
                  least-squares problem with a Helmholtz
                  discretization. While algorithms based on LU or QR
                  factorization are very efficient for 2D problems, 3D
                  problems required the development of a
                  factorization-free iterative method. This talk
                  introduces the current version of the algorithm,
                  which requires significantly less memory and
                  computation and uses existing Helmholtz solvers as
                  an important building block.  We plan to discuss
                  early results of 3-D WRI on a small 3-D synthetic
                  example.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADF3dw/peters2015SINBADF3dw.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADF3dw/peters2015SINBADF3dw.mov},
  author = {Bas Peters and Chen Greif and Felix J. Herrmann}
}


@PRESENTATION{peters2015SINBADFqpf,
  title = {A quadratic-penalty full-space method for waveform inversion},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {In PDE-constrained optimization problems other than
                  geophysical full-waveform inversion, full-space
                  optimization methods are commonly used. This type of
                  optimization method updates both the medium
                  parameters and the wavefields, instead of solving
                  wave equations explicitly. In the FWI context, this
                  means the objective function value and gradient can
                  be obtained at very little computational cost. A
                  major obstacle is, however, the requirement to have
                  all wavefields in memory.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADFqpf/peters2015SINBADFqpf.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADFqpf/peters2015SINBADFqpf.mov},
  author = {Bas Peters and Felix J. Herrmann}
}


@PRESENTATION{peters2015SINBADFrwi,
  title = {Regularizing waveform inversion by projections onto intersections of convex sets},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {Common strategies to regularize waveform inversion (and
                  other geophysical inverse problems) are adding
                  quadratic penalty terms to the objective function or
                  filtering the gradients used to update the model
                  estimate. An example are penalties or filters to
                  prevent/filter spurious high spatial frequency
                  oscillations in the model while working with low
                  frequency data. We present an alternative way of
                  regularization, which works by projecting the model
                  onto an intersection of convex sets, where each sets
                  encodes certain desired model properties. This
                  approach has certain theoretical and practical
                  advantages over quadratic penalties or gradient
                  filters. Some examples of useful convex sets in
                  various challenging waveform inversion settings are
                  shown on both real and synthetic data.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADFrwi/peters2015SINBADFrwi.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADFrwi/peters2015SINBADFrwi.mov},
  author = {Bas Peters and Brendan R. Smithyman and Mathias Louboutin and Felix J. Herrmann}
}


@PRESENTATION{sharan2015SINBADFscu,
  title = {Source collocation using the method of {Linearized} {Bregman}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {In this work, we present a method to collocate sources
                  from seismic reflections recorded at receiver
                  locations. We use the method of linearized
                  Bregman. This algorithm focuses unknown sources by
                  promoting sparsity in the “Helmholtz domain”—i.e.,
                  the wavefield under the action of the the Helmholtz
                  system—under the constraint of a data misfit within
                  a particular tolerance. We extend this method to
                  noisy measurements. We are in particular interested
                  in situations where the noise is coherent because it
                  is in the range of the Helmholtz equation.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/sharan2015SINBADFscu/sharan2015SINBADFscu.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/sharan2015SINBADFscu/sharan2015SINBADFscu.mov},
  author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}


@PRESENTATION{wang2015SINBADFifw,
  title = {Improving full waveform inversion with spectral extrapolation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {We develop a sparsity based frequency extrapolation
                  method to obtain low frequency data from the high
                  frequency ones. As other frequency extrapolation
                  methods, the extrapolation introduces noise due to
                  dispersion and possibly densely distributed
                  reflectors. To mitigate such noise, we incorporate
                  Curvlet coefficient regularization in the
                  extrapolation algorithm. Numerical results
                  demonstrate the effectiveness of the extrapolation
                  in frequency domain FWI in models of moderate
                  complexity.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wang2015SINBADFifw/wang2015SINBADFifw.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wang2015SINBADFifw/wang2015SINBADFifw.mov},
  author = {Rongrong Wang and Felix J. Herrmann}
}


@PRESENTATION{wason2015SINBADFrtl,
  title = {Randomization of time-lapse marine surveys},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {We present an extension of our time-jittered
                  (simultaneous) marine acquisition for time-lapse
                  surveys, where we work on more realistic field
                  acquisition scenarios by incorporating irregular
                  spatial grids without insisting on repeatability
                  between the surveys. Since we are always subsampled
                  in both the baseline and monitor surveys, we recover
                  the densely sampled baseline and monitor and then
                  the (complete) 4-D difference from subsampled
                  baseline and monitor data.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFrtl/wason2015SINBADFrtl.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFrtl/wason2015SINBADFrtl.mov},
  author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}


@PRESENTATION{wason2015SINBADFsss,
  title = {Source separation for simultaneous towed-streamer acquisition via compressed sensing},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {We show a comparison between two compressed-sensing
                  based source-separation algorithms, i.e., sparsity
                  promotion and rank minimization, for simultaneous
                  data.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFsss/wason2015SINBADFsss.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFsss/wason2015SINBADFsss.mov},
  author = {Haneet Wason and Rajiv Kumar and Felix J. Herrmann}
}


@PRESENTATION{witte2015SINBADFara,
  title = {Anisotropic {RTM} applied to field data},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {In this talk we present our latest reverse
                  time-migration results of the BP Machar data set, a
                  2D seismic line from the North sea featuring a large
                  chalk dome. In contrast to our prior imaging
                  attempts, we now account for anisotropic effects in
                  the data using our new TTI pure quasi-P wave
                  modeling code in the time domain.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFara/witte2015SINBADFara.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFara/witte2015SINBADFara.mov},
  author = {Philipp A. Witte and Felix J. Herrmann}
}


@PRESENTATION{witte2015SINBADFtdf,
  title = {Time-domain {FWI} in {TTI} media},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {We develop an inversion workflow for tilted transverse
                  isotropic (TTI) media using a purely acoustic
                  formulation of the wave equation. The anisotropic
                  modeling kernel is used for the forward modeling
                  operator, as well as for the adjoint Jacobian to
                  back propagate the data residual, thus providing the
                  true gradient of the FWI objective function. We
                  apply this workflow on a synthetic FWI example and
                  perform RTM on a field data set. Furthermore, we
                  discuss alternatives for the pseudo-spectral
                  Laplacian operator of the current implementation.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFtdf/witte2015SINBADFtdf.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFtdf/witte2015SINBADFtdf.mov},
  author = {Philipp A. Witte and Felix J. Herrmann}
}


@PRESENTATION{yang2015SINBADFfis,
  title = {Fast imaging with surface-related multiples: – shallow water multiples},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {In this talk, we will show surface-related multiples
                  imaging with source estimation. Here the algorithms
                  are based on linearized bregman, and we give the
                  source estimation formulation in this case. With the
                  featured blocked structure of linearized bregman, we
                  can do efficient imaging in straightforward way by
                  randomly choosing sources and frequencies. Another
                  benefit is the simplicity to delivery the
                  algorithm.},
  keywords = {presentation, SINBAD, SINBADFALL2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/yang2015SINBADFfis/yang2015SINBADFfis.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/yang2015SINBADFfis/yang2015SINBADFfis.mov},
  author = {Mengmeng Yang and Ning Tu and Felix J. Herrmann}
}


%----- 2015 (SPRING) -----%

@PRESENTATION{dasilva2015SINBADrii,
  title = {Recent improvements to the {2/3—D} imaging & inversion algorithms in the {SLIM} software release},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {The SLIM software release contains algorithms for
                  frequency domain 2D imaging and inversion based on
                  direct LU and QR factorizations. Recent improvments
                  include significant speedups, by utilizing the
                  latest parallel direct factorization algorithms and
                  inversion strategies which solve for each frequency
                  on its corresponding grid. The iterative Helmholtz
                  solvers available in the software release are now
                  also applicable to 2D problems, extending the 2D
                  algorithms to very large & high frequency 2D imaging
                  and inversion.},
  keywords = {presentation, SINBAD, SINBADSPRING2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/dasilva2015SINBADrii/dasilva2015SINBADrii.pdf},
  author = {Curt Da Silva and Bas Peters and Felix J. Herrmann}
}


@PRESENTATION{dasilva2015SINBADogt,
  title = {Off the grid tensor completion for seismic data interpolation},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {In this work, we extend our previous low-rank tensor
                  techniques for seismic data interpolation to the
                  “off-the-grid” case, wherein the data is not
                  acquired on a regularly-spaced grid. This irregular
                  sampling scheme causes the underlying tensor to
                  become high-rank, thus preventing our previous
                  Hierarchical Tucker tensor algorithms from
                  successfully recovering the volume. Instead of
                  applying these techniques naively, we introduce a
                  fictitious, regular grid on which the volume
                  exhibits low-rank behaviour and a resampling
                  operator that interpolates the data from the
                  regular, low-rank domain to the irregular sampling
                  domain. By incorporating these changes in to our
                  previous framework, we can interpolate irregularly
                  sampled data volumes with costs determined primarily
                  by the choice of resampling transform. In our case,
                  we use a non-uniform Fast Fourier Transform, but
                  other choices are possible.},
  keywords = {presentation, SINBAD, SINBADSPRING2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/dasilva2015SINBADogt/dasilva2015SINBADogt.pdf},
  author = {Curt Da Silva and Felix J. Herrmann}
}


@PRESENTATION{esser2015SINBADasd,
  title = {Automatic salt delineation — {Wavefield} {Reconstruction} {Inversion} with convex constraints},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {We extend full-waveform inversion by Wavefield
                  Reconstruction Inversion by including convex
                  constraints on the model. Contrary to the
                  conventional adjoint-state formulations, Wavefield
                  Reconstruction Inversion has the advantage that the
                  Gauss-Newton Hessian is well approximated by a
                  diagonal scaling, which allows us to add convex
                  constraints, such as the box- and the
                  edge-preserving total-variation constraint, on the
                  square slowness without incurring significant
                  increases in computational costs. As the examples
                  demonstrate, including these constraints yields far
                  superior results in complex geological areas that
                  contain high-velocity high-contrast bodies
                  (e.g. salt or basalt). Without these convex
                  constraints, adjoint-state and Wavefield
                  Reconstruction Inversion get trapped in local minima
                  for poor starting models.},
  keywords = {presentation, SINBAD, SINBADSPRING2015, SLIM},
  note = {posthumously presented by Felix J. Herrmann},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/esser2015SINBADasd/esser2015SINBADasd.pdf},
  author = {Ernie Esser and Llu\'{i}s Guasch and Felix J. Herrmann}
}


@PRESENTATION{fang2015SINBADwri,
  title = {Wavefield reconstruction inversion with source estimation and its application to 2014 {Chevron} synthetic blind test dataset},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {We present a robust wavefield reconstruction inversion
                  with source estimation. The source wavelet is
                  estimated with the reconstruction of wavefield
                  simultaneously by solving an extended data
                  augmentation problem. We apply this method to the
                  2014 Chevron synthetic blind test dataset and show
                  the robustness of our method.},
  keywords = {presentation, SINBAD, SINBADSPRING2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/fang2015SINBADwri/fang2015SINBADwri.pdf},
  author = {Zhilong Fang and Felix J. Herrmann}
}


@PRESENTATION{herrmann2015SINBADfom,
  title = {Fast “online” migration with Compressive Sensing},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {We present a novel adaptation of a recently developed
                  relatively simple iterative algorithm to solve
                  large-scale sparsity-promoting optimization
                  problems. Our algorithm is particularly suitable to
                  large-scale geophysical inversion problems, such as
                  sparse least-squares reverse-time migration or
                  Kirchoff migration since it allows for a tradeoff
                  between parallel computations, memory allocation,
                  and turnaround times, by working on subsets of the
                  data with different sizes. Comparison of the
                  proposed method for sparse least-squares imaging
                  shows a performance that rivals and even exceeds the
                  performance of state-of-the art one-norm solvers
                  that are able to carry out least-squares migration
                  at the cost of a single migration with all data.},
  keywords = {presentation, SINBAD, SINBADSPRING2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/herrmann2015SINBADfom/herrmann2015SINBADfom.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{lopez2015SINBADdmc,
  title = {Deterministic matrix completion: applications to seismic trace interpolation},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {This talk discusses recent developments in the theory of
                  matrix completion that allow us to analyze how
                  successful a given sub sampling scheme will be for
                  seismic trace interpolation via nuclear norm
                  minimization. By adopting a graph theory
                  perspective, we utilize the spectral gap of our
                  sampling operator as a means to quantify our
                  acquisition design. This approach offers a heuristic
                  technique that is practical and with the potential
                  to achieve trace interpolation via rank penalization
                  in a deterministic manner.},
  keywords = {presentation, SINBAD, SINBADSPRING2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/lopez2015SINBADdmc/lopez2015SINBADdmc.pdf},
  author = {Oscar Lopez and Felix J. Herrmann}
}


@PRESENTATION{oghenekohwo2015SINBADrdc,
  title = {Recent developments in compressive sensing for time-lapse studies},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {We present an overview of the advances we have made with
                  respect to extending compressive sensing ideas to
                  time-lapse studies. We show applications in
                  compressive time-lapse seismic data acquisition,
                  imaging and full-waveform inversion.},
  keywords = {presentation, SINBAD, SINBADSPRING2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/oghenekohwo2015SINBADrdc/oghenekohwo2015SINBADrdc.pdf},
  author = {Felix Oghenekohwo and Rajiv Kumar and Haneet Wason and Ernie Esser and Ning Tu and Felix J. Herrmann}
}


@PRESENTATION{peters2015SINBADrwi,
  title = {Regularizing waveform inversion by projections onto convex sets},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {A framework is proposed for regularizing the waveform
                  inversion problem by projections onto intersections
                  of convex sets. Multiple pieces of prior information
                  about the geology are represented by multiple convex
                  sets, for example limits on the velocity or minimum
                  smoothness conditions on the model. The data-misfit
                  is then minimized, such that the estimated model is
                  always in the intersection of the convex
                  sets. Therefore, it is clear what properties the
                  estimated model will have at each iteration. This
                  approach does not require any quadratic penalties to
                  be used and thus avoids the known problems and
                  limitations of those types of penalties. It is shown
                  that by formulating waveform inversion as a
                  constrained problem, regularization ideas such as
                  Tikhonov regularization and gradient filtering can
                  be incorporated into one framework. The algorithm is
                  generally applicable, in the sense that it works
                  with any (differentiable) objective function and
                  does not require significant additional
                  computation. The method is demonstrated on the
                  inversion of the 2D marine isotropic elastic
                  synthetic seismic benchmark by Chevron using an
                  acoustic modeling code. To highlight the effect of
                  the projections, we apply no data pre-processing.},
  keywords = {presentation, SINBAD, SINBADSPRING2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/peters2015SINBADrwi/peters2015SINBADrwi.pdf},
  author = {Bas Peters and Brendan R. Smithyman and Felix J. Herrmann}
}


@PRESENTATION{peters2015SINBADswr,
  title = {Solving {WRI’s} data-augmented wave equation in {3D}},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {The Wavefield Reconstruction Inversion (WRI) approach to
                  seismic inversion is a topic of active research in
                  the SLIM group. Several variants have been proposed,
                  all of which rely on the solution of a linear
                  least-squares problem with a Helmholtz
                  discretization. While algorithms based on LU or QR
                  factorization are very efficient for 2D problems, 3D
                  problems required the development of a
                  factorization-free iterative method. This talk
                  introduces the current version of the algorithm,
                  which requires significantly less memory and
                  computation then a few months ago and uses existing
                  Helmholtz solvers as an important building block.},
  keywords = {presentation, SINBAD, SINBADSPRING2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/peters2015SINBADswr/peters2015SINBADswr.pdf},
  author = {Bas Peters and Chen Greif and Felix J. Herrmann}
}


@PRESENTATION{wason2015SINBADldr,
  title = {Latest developments in randomized marine acquisition and source separation},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2015},
  abstract = {We present the latest developments in randomized
                  (simultaneous) marine acquisition for time-lapse
                  surveys. We also show a comparison between two
                  source separation algorithms — sparsity-promotion
                  and rank-minimization — for simultaneous data.},
  keywords = {presentation, SINBAD, SINBADSPRING2015, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/wason2015SINBADldr/wason2015SINBADldr.pdf},
  author = {Haneet Wason and Rajiv Kumar and Felix Oghenekohwo and Felix J. Herrmann}
}


%----- 2014 (FALL) -----%

@PRESENTATION{dasilva2014SINBADlss,
  title = {Large-scale seismic data interpolation in a parallel computing environment},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {This talk will outline the details for a parallel
                  implementation of the SPG-LR algorithm for matrix
                  completion and its applications to large-scale
                  seismic data interpolation. Previous work done in
                  our group on LR-based matrix factorization has been
                  shown to be extremely efficient for seismic data
                  interpolation that fits in memory. We extend the
                  SPGL1 framework that this method is built on to
                  instances where the full seismic data volume cannot
                  entirely fit in the memory of one computational node
                  and must be run in a distributed environment. We
                  will also look at off-the-grid regularization for
                  tensor completion using a non-uniform FFT operator
                  and examine its effects on interpolating an
                  irregularly sampled, 3D seismic frequency slice.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/dasilva2014SINBADlss/dasilva2014SINBADlss.pdf},
  author = {Curt da Silva and Felix J. Herrmann}
}


@PRESENTATION{esser2014SINBADlcs,
  title = {A lifted $\ell_1$/$\ell_2$ constraint for sparse blind deconvolution},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {We propose a modification to a sparsity constraint based
                  on the ratio of l1 and l2 norms for solving blind
                  seismic deconvolution problems in which the data
                  consist of linear convolutions of different sparse
                  reflectivities with the same source wavelet. No
                  assumptions are made about the location of the
                  support of either the wavelet or the sparse
                  signals. Minimizing the ratio of l1 and l2 norms has
                  been previously shown to promote sparsity in a
                  variety of applications including blind
                  deconvolution. Most existing implementations are
                  heuristic or require smoothing the l1/l2
                  penalty. Lifted versions of l1/l2 constraints have
                  also been proposed but are still challenging to
                  implement. Inspired by the lifting approach, we
                  propose to split the sparse signals into positive
                  and negative components and apply an l1/l2
                  constraint to the difference, thereby obtaining a
                  constraint that is easy to implement without
                  smoothing the l1 or l2 norms. We show that a method
                  of multipliers implementation of the resulting model
                  can recover source wavelets that are not necessarily
                  minimum phase and approximately reconstruct the
                  sparse reflectivities. It appears to be robust to
                  the initialization and to small amounts of noise in
                  the data. We also discuss extensions to the
                  Estimation of Primaries by Sparse Inversion (EPSI)
                  convolution model.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/esser2014SINBADlcs/esser2014SINBADlcs.pdf},
  author = {Ernie Esser and Tim T.Y. Lin and Rongrong Wang and Felix J. Herrmann}
}


@PRESENTATION{esser2014SINBADtvc,
  title = {Total variation constrained full waveform inversion with continuation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {Adding a total variation constraint to the Wavefield
                  Reconstruction Inversion model proposed by van
                  Leeuwen and Herrmann can improve robustness and
                  remove artifacts by encouraging piecewise smooth
                  solutions. However, if the parameter controlling the
                  strength of the TV regularization is not well
                  chosen, important details can be lost. We
                  investigate continuation strategies that gradually
                  reduce the strength of the TV constraint so that
                  these important details are eventually allowed back
                  into the solution. Preliminary numerical experiments
                  suggest that this can improve the solution path and
                  lead to better velocity models than if TV
                  regularization were not used during the intermediate
                  steps.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/esser2014SINBADtvc/esser2014SINBADtvc.pdf},
  author = {Ernie Esser and Llu\'{i}s Guasch and Tristan van Leeuwen and Aleksandr Y. Aravkin and Felix J. Herrmann}
}


@PRESENTATION{fang2014SINBADsew,
  title = {Source estimation for {WRI} and its application},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/fang2014SINBADsew/fang2014SINBADsew.pdf},
  author = {Zhilong Fang and Xiang Li and Bas Peters and Felix J. Herrmann}
}


@PRESENTATION{fang2014SINBADuqw,
  title = {Uncertainty quantification for {Wavefield} {Reconstruction} {Inversion}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {This talk discusses the uncertainty quantification for
                  the wavefield reconstruction inversion. The Hessian
                  and gradient of the WRI can be easily obtained once
                  the wave field is generated. As a result, we use a
                  Newton type McMC method to sample the posterior
                  distribution. Statistical property such as mean,
                  standard deviation, confidence interval are
                  calculated based on the sampled distribution.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/fang2014SINBADuqw/fang2014SINBADuqw.pdf},
  author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Felix J. Herrmann and Rachel Kuske}
}


@PRESENTATION{ghadermarzy2014SINBADwrr,
  title = {Wavefield reconstruction via randomized sampling},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {Regular sampling along the time axis is a common
                  approach for seismic imaging. However, seismic data
                  are often spatially undersampled due to economical
                  reasons as well as ground surface
                  limitations. Operational conditions might result in
                  noisy traces or even gaps in coverage and irregular
                  sampling which often leads to image
                  artifacts. Seismic trace interpolation aims to
                  interpolate missing traces in an otherwise regularly
                  sampled seismic data to a complete regular grid. In
                  this talk, we explain some techniques, with low
                  computational complexity, that can be used to
                  improve the results. In particular, we talk about
                  weighting, switching to midpoint-offset domain from
                  frequency-space domain, and uniform versus jittered
                  undersampling. We illustrate the advantages of these
                  modifications using a real seismic line from the
                  Gulf of Suez.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/ghadermarzy2014SINBADwrr/ghadermarzy2014SINBADwrr.pdf},
  author = {Navid Ghadermarzy and Ozgur Yilmaz and Felix J. Herrmann}
}


@PRESENTATION{herrmann2014SINBADldm2,
  title = {Latest developments in randomized ({4D}) acquisition},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {We will present the latest developments in randomized
                  (4D) seismic data acquisition.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/herrmann2014SINBADldm2/herrmann2014SINBADldm2.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{herrmann2014SINBADwri,
  title = {Wavefield {Reconstruction} {Inversion} ({WRI}) - a new take on wave-equation based inversion},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {We discuss a recently proposed novel method for waveform
                  inversion: Wavefield Reconstruction Inversion
                  (WRI). As opposed to conventional FWI --- which
                  attempts to minimize the error between observed and
                  predicted data obtained by solving a wave equation
                  --- WRI reconstructs a wave-field from the data and
                  extracts a model-update from this wavefield by
                  minimizing the wave-equation residual. The method
                  does not require explicit computation of an adjoint
                  wavefield as all the necessary information is
                  contained in the reconstructed wavefield. We show
                  how the corresponding model updates can be
                  interpreted physically analogously to the
                  conventional imaging-condition-based approach.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/herrmann2014SINBADwri/herrmann2014SINBADwri.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{kumar2014SINBADeia,
  title = {Extended images in action --- image gather with surface-related multiples},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {Common image gathers are used in building velocity
                  models, invert anisotropy parameters and to analyze
                  reservoir attributes. Often primary reflections are
                  used to form the image gathers and multiples are
                  typically attenuated in processing. However,
                  researchers have shown that, if correctly used, they
                  can provide useful information about the subsurface
                  during reverse-time migration. In this work, i will
                  show how we can use multiples along with primaries
                  to form the image-gathers.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/kumar2014SINBADeia/kumar2014SINBADeia.pdf},
  author = {Rajiv Kumar and Ning Tu and Tristan van Leeuwen and Felix J. Herrmann}
}


@PRESENTATION{kumar2014SINBADtjm,
  title = {Time-jittered marine acquisition: low-rank v/s sparsity},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {In this work we will show how simultaneous or blended
                  acquisition can be setup as a rank-minimization
                  (deblending) problem. The simultaneous acquisition
                  scenario is a pragmatic time-jittered marine
                  acquisition scheme, where a source vessel (with two
                  airgun arrays) sails across an ocean-bottom array
                  firing at jittered source locations and instances in
                  time, resulting in better spatial sampling and
                  acquisition speedup. We make comparisons with
                  sparsity-promoting based techniques and demonstrate
                  that rank-minimization based debelending techniques
                  are computationally faster and memory
                  efficient. This is joint work with Haneet Wason.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/kumar2014SINBADtjm/kumar2014SINBADtjm.pdf},
  author = {Rajiv Kumar and Haneet Wason and Felix J. Herrmann}
}


@PRESENTATION{lago2014SINBADmri,
  title = {Minimal-residual iterative methods for time-harmonic wave-equation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {In this work we aim at comparing the performance of some
                  iterative methods for solving the time-harmonic
                  acoustic wave-equation, one of the most challenging
                  problems in the numerical linear algebra
                  community. Widely used in frequency domain full
                  waveform inversion, the discrete wave-equation
                  yields very large, complex, sparse, ill-conditioned
                  linear systems. In this extended abstract we aim at
                  further comparing our recently proposed method,
                  CRMN, against CGMN and another method renown for
                  being very memory efficient, the two-level
                  preconditioner with shifted Laplacian operator. We
                  propose several improvements for the two-level
                  preconditioner by combining this method with CRMN in
                  several senses.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/lago2014SINBADmri/lago2014SINBADmri.pdf},
  author = {Rafael Lago and Felix J. Herrmann}
}


@PRESENTATION{li2014SINBADtsi,
  title = {2D/{3D} time-stepping for imaging and inversion},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {In this talk, we present a SLIM version of 2D/3D
                  time-stepping modeling kernel for inversion and
                  imaging. One key aspect of this framework is that
                  the implementation follows some mathematic
                  principles, such as linear operator test. Aside from
                  obeying mathematic principles, we also developed a
                  strategy that allows us to compute the gradient of
                  conventional FWI objective or RTM imaging without
                  storing extra wavefield. We also show some
                  applications of this framework on the Chevron 2014
                  benchmark elastic synthetic data set.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/li2014SINBADtsi/li2014SINBADtsi.pdf},
  author = {Xiang Li and Felix J. Herrmann}
}


@PRESENTATION{li2014SINBADwmg,
  title = {Why the modified {Gauss-Newton} method?},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {In our earlier work, we develop the modified
                  Gauss-Newton method for FWI, which requires each
                  update of FWI to be sparse in the curvelet
                  domain. Our empirical observation of the MGN method
                  is that it can find us a solution for FWI problem
                  with sparse perturbation of the initial guess
                  without changing the underlying objective. In this
                  talk, we will analyze the MGN method to find out the
                  reason why it can generate a sparse perturbation of
                  the initial model, because sum of spares updates
                  could easily generate non-sparse
                  perturbations. Moreover, we will illustrate when do
                  we expect the modified Gauss-Newton method to yield
                  a solution with sparse perturbation and in what
                  circumstances should we use it in place of other
                  algorithms like standard Gauss-Newton.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/li2014SINBADwmg/li2014SINBADwmg.pdf},
  author = {Xiang Li and Ernie Esser and Felix J. Herrmann}
}


@PRESENTATION{lin2014SINBADdag,
  title = {Dealing with acquisition gaps in {Robust} {EPSI} without interpolation using wavefield auto-convolution},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {Straightforward modifications to the Estimation of
                  Primaries by Sparse Inversion (EPSI) problem allows
                  us to mitigate acquisition holes without any
                  reconstruction of the missing traces, neither prior
                  to nor during the inversion process. This is
                  achieved by simulating the missing multiple
                  contributions with terms involving auto-convolutions
                  of the primary wavefield. In this formulation we no
                  longer need to treat the missing data as another
                  unknown in the inversion process, which is important
                  in eliminating a significant source of possible
                  local minima from attempting to invent data using
                  incorrect primary and multiple models. In this talk
                  we investigate the necessary modifications to the
                  Robust EPSI problem, as well as algorithms that
                  account for the resulting non-linear modelling
                  operator. We will also investigate the
                  reconstruction limits of this approach in relation
                  to acquisition geometry and subsurface
                  characteristics.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/lin2014SINBADdag/lin2014SINBADdag.pdf},
  author = {Tim T.Y. Lin and Felix J. Herrmann}
}


@PRESENTATION{lopez2014SINBADogm,
  title = {Off-the-grid matrix completion},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {This talk discusses a modified rank minimization
                  workflow designed to handle unstructured matrix
                  completion problems. We extend current rank
                  minimization based trace interpolation techniques to
                  optimally handle undersampled irregular data, in
                  such a way that allows us to recover regularized
                  densely sampled data. This is achieved by
                  introducing a regularization operator, that
                  accurately allows us to simulate the effect of an
                  unstructured grid. We provide reconstruction error
                  bounds to demonstrate the potential of our
                  procedure.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/lopez2014SINBADogm/lopez2014SINBADogm.pdf},
  author = {Oscar Lopez and Rajiv Kumar and Ernie Esser and Felix J. Herrmann}
}


@PRESENTATION{lopez2014SINBADrma,
  title = {Rank minimization via alternating optimization},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {This talk discusses the benefits of an alternating
                  optimization approach for seismic trace
                  interpolation via rank minimization. We consider
                  recent factorization approaches to rank minimization
                  problems where we write our low rank matrix in
                  bi-linear form, and modify this workflow by
                  alternating our optimization to handle a single
                  matrix factor at a time. This allows for a more
                  tractable procedure that can better handle highly
                  sub sampled data sets without increasing the time
                  complexity. We demonstrate the potential of this
                  approach with several seismic trace interpolation
                  experiments.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/lopez2014SINBADrma/lopez2014SINBADrma.pdf},
  author = {Oscar Lopez and Rajiv Kumar and Felix J. Herrmann}
}


@PRESENTATION{oghenekohwo2014SINBADrst,
  title = {Randomized subsampling in time-lapse surveys and recovery techniques},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {This talk centres on new insights on how we think of
                  acquisition of time-lapse data using ideas from
                  compressed sensing. Specifically, we will show how
                  simultaneous processing of time-lapse data from
                  randomized subsampling gives more reliable estimates
                  of the vintages and time-lapse signal, compared to
                  parallel processing.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/oghenekohwo2014SINBADrst/oghenekohwo2014SINBADrst.pdf},
  author = {Felix Oghenekohwo and Rajiv Kumar and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}


@PRESENTATION{oghenekohwo2014SINBADuwc,
  title = {Use what`s in common: time-lapse {FWI} with distributed {Compressive} {Sensing}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {In this talk, we will extend our joint recovery
                  formulation to FWI in time-lapse seismic, using the
                  modified Gauss-Newton approach. Leveraging ideas
                  from distributed compressive sensing, we exploit
                  shared information in the sparse model perturbations
                  of the baseline and monitor data.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/oghenekohwo2014SINBADuwc/oghenekohwo2014SINBADuwc.pdf},
  author = {Felix Oghenekohwo and Rajiv Kumar and Ernie Esser and Felix J. Herrmann}
}


@PRESENTATION{peters2014SINBADcda,
  title = {Challenges and developments arising from {2D} {FWI} of a land {VSP} dataset in the {Permian} {Basin}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {Full-waveform inversion was carried out to map 3D
                  velocity structures for a site in the Permian Basin
                  of Texas, USA. The data come from co-located surface
                  and 3D VSP seismic surveys that share common source
                  vibration points. This challenging on-land setting
                  has been used as a testbed to evaluate our FWI
                  algorithms and their effectiveness when applied to
                  real-data problems. We discuss the challenges posed
                  by this case study, and several new techniques and
                  methodological refinements that have been developed
                  and tested as a result. These include:
                  regularization and incorporation of multiple
                  datasets in an on-land FWI workflow; restrictions on
                  the types of models allowed (viz., smoothness
                  regularization) by quadratic penalty and by
                  Projected Quasi-Newton (PQN) inversion; corrections
                  to enable 2D FWI and recovery of 3D models; the
                  state of ongoing work in WRI (``Penalty Method''
                  inversion) and the handling of anisotropy.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/peters2014SINBADcda/peters2014SINBADcda.pdf},
  author = {Brendan R. Smithyman and Bas Peters and Felix J. Herrmann}
}


@PRESENTATION{peters2014SINBADiss,
  title = {Iterative solution strategy for least-squares problem with a {PDE}-block},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {Waveform inversion using the Wavefield Reconstruction
                  Inversion (WRI) is a quadratic-penalty based method
                  developed in our group and has shown to have
                  advantages over the reduced Lagrangian based
                  conventional FWI methods. WRI involves no explicit
                  PDE solves, but a least-squares problem with a PDE
                  block. This type of least-squares problem is very
                  challenging to solve using iterative methods. In
                  this talk we show algorithms we developed
                  specifically for this application and which involve
                  a combination of preconditioning, low-rank
                  decomposition and deflation.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/peters2014SINBADiss/peters2014SINBADiss.pdf},
  author = {Bas Peters and Felix J. Herrmann}
}


@PRESENTATION{peters2014SINBADqpb,
  title = {Quadratic-penalty based full-space methods for waveform inversion},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {Waveform inversion problems are commonly solved by
                  eliminating the field variables. Full-space methods
                  store and update the fields, rather than solving for
                  them. This leads to a problem with nice properties,
                  such as free function value evaluation and exact
                  gradients and Hessians at no cost. The existing
                  literature is almost exclusively based on the
                  Lagrangian formulation. We propose to work with a
                  quadratic penalty formulation, which allows us to
                  reduce the storage requirements and gives the Newton
                  system a favourable structure which we exploit to
                  obtain a intrinsically parallel updating scheme for
                  the fields and medium parameters. We will show a
                  comparison with reduced-space methods.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/peters2014SINBADqpb/peters2014SINBADqpb.pdf},
  author = {Bas Peters and Felix J. Herrmann}
}


@PRESENTATION{peters2014SINBADsmp,
  title = {Single- and multi-parameter WRI --- synthetic examples},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {We demonstrate the performance of WRI on a number of
                  single- and multi-parameter synthetic examples. The
                  examples shot that WRI is less reliant on accurate
                  starting models while this formulation is also
                  conducive to multi-parameter inversion.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/peters2014SINBADsmp/peters2014SINBADsmp.pdf},
  author = {Bas Peters and Felix J. Herrmann}
}


@PRESENTATION{tu2014SINBADfis,
  title = {Fast imaging with surface-related multiples by sparse inversion},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {When used correctly, surface-related multiples can
                  provide extra illumination coverage compared with
                  primaries. In this talk, I will discuss how to
                  jointly image primaries and surface-related
                  multiples in a computationally efficient fashion. We
                  bring down the computational cost by two
                  means. First we use wave-equation solvers to
                  implicitly carry out the expensive dense
                  matrix-matrix multiplications in the prediction of
                  surface-related multiples. Second we bring down the
                  simulation cost by subsampling the frequencies and
                  monochromatic source experiments together with
                  curvelet-domain sparsity-promoting and
                  rerandomization. As a result, we obtain
                  true-amplitude least-squares migrated seismic images
                  with computational costs that are comparable to a
                  single RTM with all the data. We demonstrate the
                  efficacy of the proposed method using realistic
                  synthetic examples.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADfis/tu2014SINBADfis.pdf},
  author = {Ning Tu and Felix J. Herrmann}
}


@PRESENTATION{tu2014SINBADfis2,
  title = {Fast imaging with source estimation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {In seismic imaging, an inaccurate estimate of the source
                  wavelet may result in degraded seismic images. While
                  conventional reverse-time migration requires the
                  knowledge of the source wavelet as prior
                  information, we propose to include source estimation
                  in the fast compressive imaging procedure. Using the
                  proposed method, we can obtain seismic images that
                  are comparable to those imaged with the true source
                  wavelet, with computational costs that are
                  comparable to conventional RTM images with all the
                  data. We also extend the proposed method to image
                  data with surface-related multiples, where these
                  multiples help to mitigate the amplitude ambiguity
                  in source estimation. We verify the proposed method
                  using realistic synthetic examples.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADfis2/tu2014SINBADfis2.pdf},
  author = {Ning Tu and Aleksandr Y. Aravkin and Tristan van Leeuwen and Tim T.Y. Lin and Felix J. Herrmann}
}


@PRESENTATION{tu2014SINBADind,
  title = {Imaging the {Nelson} data set using surface-related multiples},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {We will present out latest finding on migration with
                  multiples on the Nelson dataset.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADind/tu2014SINBADind.pdf},
  author = {Ning Tu and Felix J. Herrmann}
}


@PRESENTATION{tu2014SINBADsis,
  title = {Sparse inversion simplified},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {Implementation of the SPGl1 solver in low-level computer
                  languages such as C or Fortran may sound daunting to
                  many. This talk is tailored for you if you would
                  like to reap benefits from using sparse constraints
                  to regularize your inversion problem, and at the
                  same time want some easy-to-implement algorithm to
                  solve it. We discuss in this talk the linearized
                  Bregman projection method that embraces both
                  ingredients, and show promising applications in fast
                  least-squares imaging and seismic data interpolation
                  problems.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADsis/tu2014SINBADsis.pdf},
  author = {Ning Tu and Navid Ghadermarzy and Rajiv Kumar and Felix J. Herrmann}
}


@PRESENTATION{wang2014SINBADdwi,
  title = {Denoising the wavefield inversion problem through source blending and penalty method},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {Previous FWI denoising techniques, such as source
                  blending and stacking, are designed applied to the
                  seismic traces. Hence no physical information is
                  used to improve the fidelity of the reconstruction,
                  even when a good initial model is
                  available. Moreover, traditional source blending
                  requires the existence of common source gathers,
                  which might be missing from the data. In this talk,
                  we incorporate the source blending idea into the
                  penalty method, and extract the synthetic sources
                  corresponding to the principle directions of the
                  gradient update. By blending the sources, we can not
                  only achieve an acceleration and storage efficiency
                  of the penalty method, but also make the
                  reconstruction more robust to the random noise. This
                  method is applicable to any source receiver position
                  but is more efficient if common source gathers are
                  available.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/wang2014SINBADdwi/wang2014SINBADdwi.pdf},
  author = {Rongrong Wang and Felix J. Herrmann}
}


@PRESENTATION{wason2014SINBADrrt,
  title = {Randomization and repeatability in time-lapse marine acquisition},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {During this talk, we will show how randomization
                  techniques from (distributed) compressed sensing
                  affect the way we think about time-lapse surveys.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/wason2014SINBADrrt/wason2014SINBADrrt.pdf},
  author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}


@PRESENTATION{wason2014SINBADsss,
  title = {Source separation via {SVD}-free rank-minimization in the hierarchical semi-separable representation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {During this talk, we will show a source separation
                  algorithm for blended marine acquisition, where two
                  sources are deployed at different depths (over/under
                  acquisition). The separation method incorporates the
                  Hierarchical Semi-Separable (HSS) structure inside
                  rank-regularized least-squares formulations. We also
                  compare this deblending scheme with the
                  sparsity-promoting one-norm minimization scheme.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/wason2014SINBADsss/wason2014SINBADsss.pdf},
  author = {Haneet Wason and Rajiv Kumar and Felix J. Herrmann}
}


@PRESENTATION{yilmaz2014SINBADadc,
  title = {Analog-to-digital conversion in compressive sampling},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {Compressive sampling theory is now established as an
                  effective method for dimension reduction when the
                  underlying signals (e.g., seismic data) are sparse
                  with respect to some suitable basis or frame (e.g.,
                  curvelets in the case of seismic). One important
                  problem directly related to the acquisition of
                  analog signals is how to perform analog-to-digital
                  conversion. This is directly related to two
                  important issues: (i) how accurately one can acquire
                  a signal using compressive sampling (in terms of bit
                  depth), and (ii) how ``compressed'' is compressive
                  sampling (in terms of the total number of bits one
                  ends up using after acquiring the signal). We will
                  present recent results that provide answers to these
                  questions. Specifically, we provide an
                  analog-to-digital conversion method that achieves
                  nearly optimal compression using cheap analog
                  devices with a large margin for imperfections.},
  keywords = {presentation, SINBAD, SINBADFALL2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/yilmaz2014SINBADadc/yilmaz2014SINBADadc.pdf},
  author = {Ozgur Yilmaz}
}


%----- 2014 (SPRING) -----%

@PRESENTATION{dasilva2014SINBADlrp,
  title = {Low-rank promoting transformations and tensor interpolation - applications to seismic data denoising},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {In this presentation, we extend our previous work in
                  Hierarchical Tucker (HT) tensor completion, which
                  uses an extremely efficient representation for
                  representing high-dimensional tensors exhibiting
                  low-rank structure, to handle subsampled tensors
                  with noisy entries. We consider a `low-noise' case,
                  so that the energies of the noise and the signal are
                  nearly indistinguishable, and a `high-noise' case,
                  in which the noise energy is now scaled to the
                  amplitude of the entire data volume. For the
                  low-noise case in particular, standard
                  trace-by-trace energy comparisons cannot distinguish
                  noise from signal. We examine the behaviour of noise
                  in terms of the singular values along different
                  matricizations of the data, i.e. reshaping of the
                  tensor along different dimensions. By interpreting
                  this effect in the context of tensor completion, we
                  demonstrate the inefficacy of denoising by this
                  method in the source-receiver domain. In light of
                  this observation, we transform the decimated, noisy
                  data in to the midpoint-offset domain, which
                  promotes low-rank behaviour in the signal and
                  high-rank behaviour in the noise. This distinction
                  between signal and noise allows low-rank
                  interpolation to effectively denoise the signal,
                  without knowledge of the noise location, with only a
                  marginal increase in computational cost. We
                  demonstrate the effectiveness of this approach on a
                  4D frequency slice.},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/dasilva2014SINBADlrp/dasilva2014SINBADlrp.pdf},
  author = {Curt da Silva and Felix J. Herrmann}
}


@PRESENTATION{esser2014SINBADsgp,
  title = {A scaled gradient projection method for total variation regularized full waveform inversion},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {We propose a modification to the quadratic penalty
                  formulation for seismic full waveform inversion
                  proposed by van Leeuwen and Herrmann that includes
                  convex constraints on the model. In particular, we
                  show how to simultaneously constrain the total
                  variation of the slowness squared while enforcing
                  bound constraints to keep it within a physically
                  realistic range. Synthetic experiments show that
                  including total variation regularization can improve
                  the recovery of a high velocity perturbation to a
                  smooth background model.},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/esser2014SINBADsgp/esser2014SINBADsgp.pdf},
  author = {Ernie Esser and Tristan van Leeuwen and Aleksandr Y. Aravkin and Felix J. Herrmann}
}


@PRESENTATION{fang2014SINBADsqn,
  title = {A stochastic quasi-{Newton} {McMC} method for uncertainty quantification of full-waveform inversion},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {In this work, we present a fast McMC method using the
                  stochastic l-BFGS Hessian to quantify the
                  uncertainty of full-waveform inversion. Using the
                  stochastic l-BFGS Hessian, we do not need the
                  assumption that the Hessian of data misfit is low
                  rank and we also reduce the computational cost of
                  estimating the Hessian. Numerical result shows the
                  capability of this fast McMC method.},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/fang2014SINBADsqn/fang2014SINBADsqn.pdf},
  author = {Zhilong Fang and Chia Ying Lee and Felix J. Herrmann}
}


@PRESENTATION{herrmann2014SINBADdns,
  title = {{DNOISE III} – the next step},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {During this presentation I will discuss our plans for
                  the NSERC Collaborative Research and Development
                  Grant DNOISE III. With this grant, we aim to match
                  the industry contributions of SINBAD
                  dollar-for-dollar.},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/herrmann2014SINBADdns/herrmann2014SINBADdns.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{herrmann2014SINBADldm,
  title = {Latest developments in marine ({4D}) acquisition},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {During this talk, we will show the advantages of
                  randomly dithered marine acquisition with ocean
                  bottom nodes and how randomization techniques from
                  compressive sensing affect the way we think about
                  time-lapse surveys. This is joint work with Felix
                  and Haneet.},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/herrmann2014SINBADldm/herrmann2014SINBADldm.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{herrmann2014SINBADrpe,
  title = {Relax the physics & expand the search space – {FWI} via {Wavefield} {Reconstruction} {Inversion}},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {During this talk, we will present a new formulation of
                  full-waveform inversion that combines the best of
                  full-space constrained methods and reduced-space
                  unconstrained methods. Instead of eliminating the
                  constraint, which leads to the reduced adjoint-state
                  method that underpins most formulations of
                  full-waveform inversion, our method relaxes the PDE
                  constraint by replacing it by an additive
                  (least-squares) penalty term. By using the method of
                  variable projection, we arrive at a formulation that
                  alternates between solving for the wavefield, given
                  the velocity model & data, and solving for
                  velocity-model updates, given the wavefields. We
                  named this method Wavefield Reconstruction Inversion
                  (WRI) because it inverts for the model updates by
                  reconstructing the wavefield everywhere given data
                  observed at the receivers and the physics of the
                  wave equation. During the talk, we present this new
                  method and discuss how the increased search space,
                  now consisting of the wavefields and model,
                  mitigates the effects of local minima. We will also
                  discuss recent extensions including multi-parameter
                  inversion and regularization. This is joint work
                  with Tristan van Leeuwen, Bas Peters, and Ernie
                  Esser.},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/herrmann2014SINBADrpe/herrmann2014SINBADrpe.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{kumar2014SINBADsfm,
  title = {{SVD}-free matrix completion for seismic data reconstruction},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {Seismic data interpolation via rank-minimization
                  techniques has been recently introduced in the
                  seismic community. All the existing
                  rank-minimization techniques assumes the underlying
                  grid to be regular. Irregularity is one of the
                  common impediments in acquisition. In this work, we
                  studied the effect of irregularity on structured and
                  show that how we can modify the existing techniques
                  to handle it. Other then irregularity, we often have
                  missing data. We also show that we can tackle both
                  regularization and interpolation issue
                  simultaneously. The objective of this work is to
                  extend our existing method of interpolation on
                  structured grid to unstructured grid. We illustrate
                  the advantages of the modification in existing
                  methodology using a seismic line from Gulf of Suez
                  to obtain high quality results for regularization
                  and interpolation, a key application in exploration
                  geophysics.},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/kumar2014SINBADsfm/kumar2014SINBADsfm.pdf},
  author = {Rajiv Kumar and Oscar Lopez and Ernie Esser and Felix J. Herrmann}
}


@PRESENTATION{lago2014SINBADhfw,
  title = {Heuristics in full-waveform inversion},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {For many full-waveform inversion techniques, the most
                  computationally intensive step is the computation of
                  a numerical solution for the wave equation on every
                  iteration. In the frequency domain approach, this
                  requires the solution of very large, complex,
                  sparse, ill-conditioned linear systems. In this
                  abstract we bring out attention specifically to CGMN
                  method for solving PDEs, known for being flexible
                  (i.e. it is able to treat equally acoustic data as
                  well as visco-elastic or more complex scenarios)
                  efficient with respect both to memory and
                  computation time, and controllable accuracy of the
                  final approximation. We propose an improvement for
                  the known CGMN method by imposing a minimal residual
                  condition, which incurs in one extra model vector
                  storage. The resulting algorithm called CRMN enjoys
                  several interesting properties as monotonically
                  nonincreasing behaviour of the norm of the residual
                  and minimal residual, guaranteeing optimal
                  convergence for the relative residual criterion. We
                  discuss numerical experiments both in an isolated
                  PDE solve and also within the inversion procedure,
                  showing that in a realistic scenario we can expect a
                  speedup around 25\% when using CRMN rather than
                  CGMN. Joint work with Art Petrenko, Zhilong Fang,
                  Felix J. Herrmann.},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/lago2014SINBADhfw/lago2014SINBADhfw.pdf},
  author = {Rafael Lago and Felix J. Herrmann}
}


@PRESENTATION{lin2014SINBADiit,
  title = {Implicit interpolation of trace gaps in {REPSI} using auto-convolution terms},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {It is possible to solve the Estimation of Primaries by
                  Sparse Inversion problem from a sesimic record with
                  large holes without any explicit data
                  reconstruction, by instead simulating the missing
                  multiple contributions with terms involving
                  auto-convolutions of the primary
                  wavefield. Exclusion of the unknown data as an
                  inversion variable from the REPSI process is
                  desireable, since it eliminates a significant source
                  of local minima that arises from attempting to
                  invert for the unobserved traces using primary and
                  multiple models that may be far-away from the true
                  solution. In this talk we investigate the necessary
                  modifications to the REPSI algorithm to account for
                  the resulting non-linear modeling operator, and
                  demonstrate that just a few auto-convolution terms
                  are enough to satisfactorily mitigate the effects of
                  data gaps during the inversion process.},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/lin2014SINBADiit/lin2014SINBADiit.pdf},
  author = {Tim T.Y. Lin and Felix J. Herrmann}
}


@PRESENTATION{lin2014SINBADmas,
  title = {Multilevel acceleration strategy for {REPSI}},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {This talk discusses a multilevel inversion strategy that
                  aims to substantially reduce the computational costs
                  of the Robust Estimation of Primaries by Sparse
                  Inversion algorithm. The proposed method solves
                  early iterations of REPSI at very coarse spatial
                  sampling grids while gradually ramping-up the
                  spatial sampling when more accuracy is desired. No
                  changes to the the core implementation of the
                  original algorithm are necessary while in addition
                  only requiring trace decimation, low-pass filtering,
                  and rudimentary interpolation techniques.},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/lin2014SINBADmas/lin2014SINBADmas.pdf},
  author = {Tim T.Y. Lin and Felix J. Herrmann}
}


@PRESENTATION{oghenekohwo2014SINBADrsw,
  title = {Randomized sampling without repetition in time-lapse seismic surveys},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {In this talk, we will show a method for acquiring
                  time-lapse data, where we do not have to repeat the
                  survey geometry. Our method works provided our
                  acquisition is randomized, where sources and
                  receivers are at random locations on a computational
                  grid, and provided we know the spatial locations of
                  the shots and receivers. In addition, we show the
                  implications of either (sub)sampling more for the
                  baseline and less for the monitor or vice-versa, and
                  how this sampling scheme affects the 4-D signal.},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/oghenekohwo2014SINBADrsw/oghenekohwo2014SINBADrsw.pdf},
  author = {Felix Oghenekohwo and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}


@PRESENTATION{peters2014SINBADmpw,
  title = {Multi-parameter waveform inversion; exploiting the structure of penalty-methods},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {In this talk I consider the problem of inverting
                  waveforms for multiple medium parameters. The
                  governing PDE is chosen to be the Helmholtz equation
                  with compressibility and buoyancy as the
                  unknowns. Both unknowns occur in the same equation
                  and practice has shown it is very hard to estimate
                  both equally accurate; the buoyancy estimate (or
                  density if a slightly different parametrization is
                  used) is typically much smoother than the
                  compressibility (or velocity). Here I introduce a
                  new waveform inversion algorithm: a full Newton-type
                  method based on a penalty method which adds the PDE
                  constraint as a quadratic penalty term. This method
                  updates both the 'wavefields' and medium parameters,
                  without explicitly solving PDE's. One of the main
                  advantages is the availability of a sparse Hessian
                  and exact gradient which are not the result of any
                  PDE solves. We asses if the availability of the
                  Hessian, which includes information about the
                  coupling between the two medium parameters, can help
                  reconstruct both compressibility and buoyancy.},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/peters2014SINBADmpw/peters2014SINBADmpw.pdf},
  author = {Bas Peters and Felix J. Herrmann}
}


@PRESENTATION{petrenko2014SINBADaih,
  title = {Accelerating an iterative {Helmholtz} solver with {FPGAs}},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {An implementation of seismic wave simulation on a
                  platform consisting of a conventional host processor
                  and a reconfigurable hardware accelerator is
                  presented. This research is important in the field
                  of exploration for oil and gas resources, where a 3D
                  model of the subsurface is frequently required. By
                  comparing seismic data collected in a real-world
                  survey with synthetic data generated by simulated
                  waves, it is possible to deduce such a
                  model. However this requires many time-consuming
                  simulations with different Earth models to find the
                  one that best fits the measured data. Speeding up
                  the wave simulations would allow more models to be
                  tried, yielding a more accurate estimate of the
                  subsurface. The reconfigurable hardware accelerator
                  employed in this work is a field programmable gate
                  array (FPGA). FPGAs are computer chips that consist
                  of electronic building blocks that the user can
                  configure and reconfigure to represent their
                  algorithm in hardware. Whereas a traditional
                  processor can be viewed as a pipeline for processing
                  instructions, an FPGA is a pipeline for processing
                  data. The chief advantage of the FPGA is that all
                  the instructions in the algorithm are already
                  hardwired onto the chip. This means that execution
                  time depends only on the amount of data to be
                  processed, and not on the complexity of the
                  algorithm. The main contribution is an
                  implementation of the well-known Kaczmarz row
                  projection algorithm on the FPGA, using techniques
                  of dataflow programming. This kernel is used as the
                  preconditioning step of CGMN, a modified version of
                  the conjugate gradients method that is used to solve
                  the time-harmonic acoustic isotropic constant
                  density wave equation. Using one FPGA accelerator,
                  the current implementation allows seismic wave
                  simulations to be performed over twice as fast,
                  compared to running on one Intel Xeon E5-2670
                  core. I also discuss the effect of modifications of
                  the algorithm necessitated by the hardware on the
                  convergence properties of CGMN. Finally, a specific
                  plan for future work is set-out in order to fully
                  exploit the accelerator platform, and my work is set
                  in its larger context},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/petrenko2014SINBADaih/petrenko2014SINBADaih.pdf},
  author = {Art Petrenko and Tristan van Leeuwen and Diego Oriato and Simon Tilbury and Felix J. Herrmann}
}


@PRESENTATION{warner2014SINBADawi,
  title = {Adaptive waveform inversion - {FWI} without cycle skipping},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {Conventional FWI minimises the direct differences
                  between observed and predicted seismic
                  datasets. Because seismic data are oscillatory, this
                  approach will suffer from the detrimental effects of
                  cycle skipping if the starting model is
                  inaccurate. We reformulate FWI so that it instead
                  adapts the predicted data to the observed data using
                  Wiener filters, and then iterates to improve the
                  model by forcing the Wiener filters towards zero-lag
                  delta functions. This adaptive FWI scheme is
                  demonstrated on synthetic data where it is shown to
                  be immune to cycle skipping, and is able to invert
                  successfully data for which conventional FWI fails
                  entirely. The new method does not require low
                  frequencies or a highly accurate starting model to
                  be successful. Adaptive FWI has some features in
                  common with wave-equation migration velocity
                  analysis, but it works for all types of arrivals
                  including multiples and refractions, and it does not
                  have the high computational costs of WEMVA in 3D.},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/warner2014SINBADawi/warner2014SINBADawi.pdf},
  author = {Mike Warner}
}


@PRESENTATION{zheglova2014SINBADead,
  title = {Exploring applications of depth stepping in seismic inverse problems},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2014},
  abstract = {We are exploring applications of stable depth
                  extrapolation with the full wave equation to imaging
                  and inversion. Depth stepping with full wave
                  equation can be advantageous to the time and
                  frequency domain modelling if special care is taken
                  to stabilize the depth exptrapolator efficiently,
                  since it reduces the higher dimensional modelling
                  problem to a number of lower dimensional
                  subproblems. We are interested in exploring
                  applications in inversion, modelling and
                  imaging. For example, just as the reverse time
                  migration can be shown to be the gradient of the
                  reduced formulation of the full waveform inversion
                  problem, it is interesting to explore whether a
                  formulation of the inversion problem can be achieved
                  whose gradient can be computed using depth stepping
                  techniques. We are also interested in such
                  applications as preconditioning of iterative methods
                  for Helmholtz equation and imaging.},
  keywords = {presentation, SINBAD, SINBADSPRING2014, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/zheglova2014SINBADead/zheglova2014SINBADead.pdf},
  author = {Polina Zheglova and Felix J. Herrmann}
}


%----- 2013 (FALL) -----%

@PRESENTATION{fang2013SINBADsfwi,
  title = {Swift {FWI}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {In 3D case, there is much more data and modeling is much
                  more expensive. As a result, parallel computing is
                  very important for 3D full-waveform inversion. Both
                  domain decomposition and data decomposition need a
                  large number of parallel computing
                  resources. However, programs based on parallel
                  MATLAB suffer from the limitation of licenses. In
                  order to obtain a MATLAB licenses free solution, we
                  use SWIFT, which is a fast and easy parallel
                  scripting language. Once the original Mat file is
                  complied to an executable file, SWIFT can run the
                  code inside the executable file in parallel without
                  using parallel MATLAB. We use the SWIFT to compute
                  the object functions and gradients of different
                  shots in parallel, and test the parallel 3D FWI code
                  with overthrust data. This is joint work with Thomas
                  Lai, Harsh Juneja, Bas Peters.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/fang2013SINBADsfwi/fang2013SINBADsfwi.pdf},
  author = {Zhilong Fang and Henryk Modzelewski and Tristan van Leeuwen and Thomas Lai and Harsh Juneja and Bas Peters and Felix J. Herrmann}
}


@PRESENTATION{vanleeuwen2013SINBADsda,
  title = {Solving the data-augmented wave equation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {The recently proposed penalty method promises to
                  mitigate some of the non-linearity inherent in
                  full-waveform inversion by relaxing the requirement
                  that the wave-equation needs to be solved
                  exactly. The basic workflow of this new method is as
                  follows; i) solve an overdetermined wave-equation
                  (the data-augmented wave-equation), where the data
                  serves as additional constraints for the wavefields,
                  ii) compute the wavefield-residual by substituting
                  this wavefield in the wave-equation, and iii)
                  correlate the wavefield with the wavefield-residual
                  to obtain a model-update. As opposed to the
                  conventional workflow, no explicit adjoint solve is
                  needed to compute the model-update. However, instead
                  of solving a wave-equation, we need to solve a
                  data-augmented wave-equation. In this talk we
                  explore some of the challenges of solving this
                  data-augmented wave-equation and review some
                  possible solution strategies for both time and
                  frequency-domain applications.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/vanleeuwen2013SINBADsda/vanleeuwen2013SINBADsda.pdf},
  author = {Tristan van Leeuwen and Bas Peters and Felix J. Herrmann}
}


@PRESENTATION{petrenko2013SINBADaih,
  title = {Accelerating an iterative {Helmholtz} solver with {FPGAs}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Solution of the Helmholtz equation is the main
                  computational burden of full-waveform inversion in
                  the frequency domain. For this task we employ the
                  CARP-CG algorithm (Gordon & Gordon 2010), an
                  iterative solver that preconditions the original
                  Helmholtz system into an equivalent symmetric
                  positive definite system and then applies the method
                  of conjugate gradients. Forming the matrix for the
                  new system is not necessary as its multiplicative
                  action on a vector is implemented using a series of
                  projections onto the rows of the original
                  system. Our contribution is implementing CARP-CG for
                  a host + accelerator (FPGA) computing
                  environment. The computational paradigm is one of
                  dataflow: vector and matrix elements are streamed
                  from memory through the accelerator which applies
                  the row projections. The advantage of an FPGA to
                  process streams of data is that unless the algorithm
                  is memory bandwidth limited, computation time is
                  directly proportional to the amount of data. The
                  complexity of the algorithm implemented on the FPGA
                  is irrelevant since all the operations programmed
                  onto the FPGA happen in the same clock tick. In
                  contrast, on a CPU, more complex algorithms require
                  more clock ticks as the instructions are executed
                  sequentially, or with only a small amount of
                  parallelism. Ongoing work porting the CARP-CG
                  algorithm to the accelerator is presented.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/petrenko2013SINBADaih/petrenko2013SINBADaih.pdf},
  author = {Art Petrenko and Tristan van Leeuwen and Felix J. Herrmann and Diego Oriato and Simon Tilbury}
}


@PRESENTATION{lago2013SINBADksf,
  title = {Krylov solvers in frequency domain {FWI}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {We briefly discuss here several aspects rising from the
                  use of Krylov solvers for frequency domain
                  FWI. Although several powerful preconditioners are
                  in constant development by the linear algebra
                  community targeting this application, some issues as
                  the multishot and multifrequency scenarios as well
                  as advanced Krylov method techniques in combination
                  with these powerful preconditioners are rarely
                  addressed. We provide an overview of some of the
                  recent research on this regard and discuss
                  possibility of use of some of these techniques in
                  the context of an inversion.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/lago2013SINBADksf/lago2013SINBADksf.pdf},
  author = {Rafael Lago and Felix J. Herrmann}
}


@PRESENTATION{yeung2013SINBADcsr,
  title = {Compressed sensing, recovery of signals using random {Turbo} matrices},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Compressed sensing is an emerging technique that allows
                  us to recover an image using far fewer number of
                  measurements than classical sampling
                  techniques. Designing the measurement matrices with
                  certain properties are critical to this
                  task. Gaussian matrices are most commonly used. We
                  discover a new class of random matrices that can
                  outperform the Gaussian matrices when we are in a
                  situation of taking an outrageously small number of
                  samples.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/yeung2013SINBADcsr/yeung2013SINBADcsr.pdf},
  author = {Enrico Au-Yeung and Ozgur Yilmaz and Felix J. Herrmann}
}


@PRESENTATION{wang2013SINBADnru,
  title = {Noise reduction by using interferometric measurements},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {The interferometric formulation of linear wave-based
                  inversion problem was proposed by Demanet and Jugnon
                  recently. Instead of directly fitting the data, they
                  proposed to fit a subset of the data's
                  cross-correlation. It can be verified that if the
                  full cross-correlation is used, then the problem is
                  equivalent to the usual least square problem. The
                  subsampling, which is usually considered to cause
                  instability to the solution, is surprisingly useful
                  in this setting. Numerical experiments for the
                  inverse source problem and the inverse scatting
                  problem have both suggested that a 'good' sampling
                  strategy can actually increase the stability under
                  modeling error caused by the uncertainty of a
                  kinetic nature. We will study the mathematical
                  mechanism behind this phenomenon, and try to see
                  whether or not there exists a universally 'good'
                  sampling strategy independent of the types of
                  forward operators we use.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/wang2013SINBADnru/wang2013SINBADnru.pdf},
  author = {Rongrong Wang and Bas Peters and Felix J. Herrmann}
}


@PRESENTATION{esser2013SINBADasr,
  title = {Applications of phase retrieval methods to blind seismic deconvolution},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Phase retrieval is the non-convex optimization problem
                  of recovering a signal from magnitudes of complex
                  linear measurements. Solving convex semi-definite
                  program (SDP) relaxations has been shown to be a
                  robust approach, but it remains too expensive to
                  apply to large problems. We will discuss methods for
                  accelerating computations and explore applications
                  to seismic deconvolution.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/esser2013SINBADasr/esser2013SINBADasr.pdf},
  author = {Ernie Esser and Felix J. Herrmann}
}


@PRESENTATION{hargreaves2013SINBADbfo,
  title = {The bridge from orthogonal to redundant transforms and weighted $\ell_1$ optimization},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Traditional arguments in synthesis $\ell_1$
                  -optimization require our forward operator to be
                  orthogonal, though we use redundant transforms in
                  practice. These traditional arguments do not
                  translate to redundant transforms, and other
                  arguments require impractical conditions on our
                  effective measurement matrix. Recent theory in
                  one-norm analysis, namely the optimal dual $\ell_1$
                  analysis of Shidong et al, have provided point-wise
                  reconstruction error estimates for synthesis using
                  an equivalence relationship where we can use weaker
                  assumptions. This exposes an important model
                  assumption indicating why analysis might outperform
                  synthesis, for which careful consideration in
                  seismic is necessary, and the need for models such
                  as the cosparse model. In this talk we will discuss
                  these ideas, provide evidence which indicates this
                  theory should generalize to uniform error
                  estimates(and thus not signal dependent), and how
                  redundancy, support information, and weighting play
                  important roles.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/hargreaves2013SINBADbfo/hargreaves2013SINBADbfo.pdf},
  author = {Brock Hargreaves and Ozgur Yilmaz and Felix J. Herrmann}
}


@PRESENTATION{goh2013SINBADttt,
  title = {Taming time through tangents},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Given two vectors of (possibly) different lengths, the
                  edit distance considers all possible alignments
                  between the two and picks the one that minimizes the
                  number of operations needed to turn one into the
                  other. Though highly non-smooth and riddled with
                  local minima, we show a way to compute the convex
                  envelope of this function, which opens the door to
                  using the approximate edit distance as a surrogate
                  for the L2 distance and comparing vectors of
                  different lengths.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/goh2013SINBADttt/goh2013SINBADttt.pdf},
  author = {Gabriel Goh and Michael P. Friedlander and Felix J. Herrmann}
}


@PRESENTATION{macedo2013SINBADdap,
  title = {A dual approach to {PhaseLift} via gauge programming and bundle methods},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {A feature common to many sparse optimization problems is
                  that the number of variables may be significantly
                  larger than the number of constraints- e.g., the
                  matrix-lifting approach taken by PhaseLift for phase
                  retrieval results in a problem where the number of
                  variables is quadratic in the number of
                  constraints. We consider a duality framework and
                  numerical methods to leverage the relatively small
                  number of constraints. Preliminary numerical results
                  illustrate our approach and its flexibility.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/macedo2013SINBADdap/macedo2013SINBADdap.pdf},
  author = {Ives Macedo and Michael P. Friedlander and Felix J. Herrmann}
}


@PRESENTATION{nutini2013SINBADpcb,
  title = {Putting the curvature back into sparse solvers},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {For many problems in signal and image processing, we
                  seek a sparse solution that solves that
                  approximately solves the problem Ax $\approx$ b,
                  where A is an m-by-n matrix and b is an
                  m-vector. Many of the most used approaches to
                  problem thissuch as iterative soft thresholding
                  SPGL1 andare first-order methods. As a result, these
                  methods can sometimes be slow to converge. In this
                  talk, we present an approach that takes advantage of
                  the easily-obtainable second-order information. By
                  exploiting this available second-order information,
                  we are able to put the curvature back into sparse
                  solvers and improve upon the convergence rates of
                  existing solvers.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/nutini2013SINBADpcb/nutini2013SINBADpcb.pdf},
  author = {Julie Nutini and Michael P. Friedlander and Felix J. Herrmann}
}  


@PRESENTATION{pong2013SINBADppg,
  title = {The proximal-proximal gradient algorithm},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {In many applications, one has to minimize the sum of a
                  smooth loss function modeling misfit and a
                  regularization term inducing structures. In this
                  talk, we consider the case when the regularization
                  is a composition of a convex function, whose
                  proximal mapping is easy to compute, and a nonzero
                  linear map. Such instances arise in system
                  identification and realization problems. In this
                  talk, we present a new algorithm, the
                  proximal-proximal gradient algorithm, which admits
                  easy subproblems. Our algorithm reduces to the
                  proximal gradient algorithm if the linear map is
                  just the identity map, and can be viewed as "very
                  inexact" inexact proximal gradient algorithm. We
                  show that the whole sequence generated from the
                  algorithm converges to an optimal solution, and
                  establish an upper bound on iteration complexity.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/pong2013SINBADppg/pong2013SINBADppg.pdf},
  author = {Ting Kei Pong and Michael P. Friedlander and Felix J. Herrmann}
}  


@PRESENTATION{akalin2013SINBADmtc,
  title = {Matrix and tensor completion for large-scale seismic interpolation: a comparative study},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Owing to their high dimensionality, interpolating 3D
                  seismic data volumes remains a computationally
                  daunting task. In this work, we outline a
                  comprehensive framework for sampling and
                  interpolating such volumes based on the
                  well-understood theory of Matrix and Tensor
                  completion. This interpolation theory consists of
                  three components major signal structure,
                  structure-destroying sampling, and
                  structure-restoring optimization. By viewing
                  interpolation in the context of this theory, we are
                  able to specify exactly when these approaches are
                  expected to perform well. We also introduce
                  structure-revealing transformations that promote the
                  inherent low-rank structure in seismic data as well
                  as a factorization approach that scales to large
                  problem sizes. Our methods are able to handle
                  large-scale data volumes more accurately and more
                  quickly compared to other more ad-hoc approaches, as
                  we will demonstrate. This is joint work with Curt Da
                  Silva, Rajiv Kumar, Ben Recht, and Felix
                  J. Herrmann.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/akalin2013SINBADmtc/akalin2013SINBADmtc.pdf},
  author = {Okan Akalin and Curt Da Silva and Rajiv Kumar and Ben Recht and Felix J. Herrmann}
}  


@PRESENTATION{dasilva2013SINBADstf,
  title = {Structured tensor formats for missing-trace interpolation and beyond},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {High-dimensional data, alternatively known as tensors,
                  occurs in a variety of seismic problems. By
                  exploiting the fact that seismic data can be well
                  represented as a structured tensor, we design
                  algorithms that operate on much lower dimensional
                  parameters. In this talk, we will review some recent
                  developments in interpolating seismic data volumes
                  in the so-called Hierarchical Tucker format as well
                  as demonstrate the need for such formats when
                  tackling high-dimensional problems such as
                  Uncertainty Quantification.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/dasilva2013SINBADstf/dasilva2013SINBADstf.pdf},
  author = {Curt da Silva and Felix J. Herrmann}
}  


@PRESENTATION{kumar2013SINBADwrs,
  title = {Wavefield reconstruction with {SVD}-free low-rank matrix factorization},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {As shown in past, we can leverage the ideas from the
                  field of compressed sensing to cast problems like
                  seismic data interpolation or sequential shot data
                  recovery from simultaneous data, as a compressed
                  sensing problem. In this work we will show how we
                  can borrow the same ideas of compressed sensing and
                  cast these problems as matrix completion
                  problems. Instead of sparsity we will show that we
                  can exploit the low-rank structure of seismic data
                  to solve these problems. One of the impediments in
                  rank-minimization problem is the computation of
                  singular values. We will also show how we can solve
                  the rank minimization problems SVD-free. The
                  practical application is divided into three parts:
                  1. In case of sequential seismic data acquisition,
                  how jittered subsampling helps to recover the better
                  quality data as compared to random
                  subsampling. 2. How the incorporation of reciprocity
                  principles help to enhance the quality of recovered
                  fully sampled data. 3. How we can recover the
                  sequential source data from simultaneous source
                  data.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADwrs/kumar2013SINBADwrs.pdf},
  author = {Rajiv Kumar and Aleksandr Y. Aravkin and Hassan Mansour and Ernie Esser and Felix J. Herrmann}
}  


@PRESENTATION{ghadermarzy2013SINBADups,
  title = {Using prior support information in approximate message passing algorithms},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Consider the standard compressed sensing problem. We
                  want to recover sparse compressible aorsignal from
                  few linear measurements. In this talk we investigate
                  recovery performance when we have prior information
                  about the support, i.e., the indices of the non-zero
                  entries, of the signal to be recovered. First we
                  briefly review the results of "weighted $\ell_p$
                  minimization algorithm with p = 1 and 0 < p <
                  1". Then we derive a weighted approximate message
                  passing (AMP) algorithm which incorporates prior
                  support information into the AMP algorithm. We
                  empirically show that this algorithm recovers sparse
                  signals significantly faster than weighted $\ell_1$
                  minimization. We also introduce a reweighting scheme
                  for AMP and weighted AMP which, we observe,
                  substantially improves the recovery conditions of
                  these algorithms. We illustrate our results with
                  extensive numerical experiments on synthetic data
                  and seismic data reconstruction.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/ghadermarzy2013SINBADups/ghadermarzy2013SINBADups.pdf},
  author = {Navid Ghadermarzy and Ozgur Yilmaz and Felix J. Herrmann}
}  


@PRESENTATION{lin2013SINBADbre,
  title = {Bootstrapping robust {EPSI} with coarsely sampled data},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {The EPSI method of surface multiple removal directly
                  inverts for the free-surface operator, i.e., the
                  multiple-free Greens function of the subsurface seismic
                  response. One peculiar feature of this approach is
                  the theoretical independence of the spectrum of the
                  free-surface operator from the spectrum of the
                  observed data. The SRME approach requires coarsely
                  sampled data to be low-pass filtered sufficiently in
                  order to avoid aliasing in multiple contribution
                  gathers, which in turn limits the temporal
                  resolution of the demultipled result. Conversely,
                  such limitations in temporal resolution do not
                  directly apply to the inversion solution of
                  EPSI. This property can in turn be exploited both to
                  significantly lower the cost of EPSI and to mitigate
                  the effect of under sampled data in a controlled
                  way.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/lin2013SINBADbre/lin2013SINBADbre.pdf},
  author = {Tim T.Y. Lin and Felix J. Herrmann}
}  
  

@PRESENTATION{oghenekohwo2013SINBADedt,
  title = {Estimating {4D} differences in time-lapse using randomized sampling techniques},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Repeatability in the seismic survey and processing has
                  been cited as the main reason for which 4D seismic
                  technology work. In the last decade, concerted
                  efforts have been spent to make the 4D seismic
                  process highly repeatable, without significant
                  success. On the contrary, Compressed Sensing, which
                  is also a relatively new sampling paradigm proposes
                  that one can recover an estimate of a fully sampled
                  signal from a noisy, under-sampled measurements
                  provided the acquisition architecture satisfies some
                  properties. By observing different under-sampled and
                  random measurements from each vintage, corresponding
                  to different acquisition geometries, we show that
                  one can still detect the 4D change in time using
                  recent ideas from Compressed sensing. Using a
                  realistic synthetic model, we show two methods of
                  estimating the 4D difference and compare their
                  relative performance to each other.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/oghenekohwo2013SINBADedt/oghenekohwo2013SINBADedt.pdf},
  author = {Felix Oghenekohwo and Ernie Esser and Felix J. Herrmann}
}  


@PRESENTATION{wason2013SINBADtjm,
  title = {Time-jittered marine sources},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Current efforts towards dense shot (and/or receiver)
                  sampling and full azimuthal coverage to produce
                  higher-resolution images have led to the deployment
                  of multiple source vessels across the survey area. A
                  step ahead from multi-source seismic acquisition is
                  simultaneous or blended acquisition where different
                  source arrays/vessels fire shots at
                  near-simultaneous or slightly random times. Seismic
                  data acquisition with simultaneous (or blended)
                  sources has helped improve acquisition efficiency
                  and mitigate acquisition related costs. Deblending
                  then aims to recover unblended data, as acquired
                  during conventional acquisition, from blended data
                  since many processing techniques rely on full,
                  regular sampling. We present a simultaneous/blended
                  marine acquisition setup where shots fire at
                  significantly jittered instances in time resulting
                  in jittered shot locations for a given speed of the
                  source vessel. The conventional, unblended data is
                  recovered from the blended, jittered/irregular data
                  by sparsity-promoting inversion using the
                  non-equispaced fast discrete curvelet transform. The
                  optimization scheme aims to deblend the blended data
                  along with regularization and interpolation to a
                  (finer) regular grid.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/wason2013SINBADtjm/wason2013SINBADtjm.pdf},
  author = {Haneet Wason and Felix J. Herrmann}
}  


@PRESENTATION{kumar2013SINBADava,
  title = {Extended images in action: efficient {AVA} via probing},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Common image gathers (CIG) are an important tool to
                  perform AVA analysis in areas of complex
                  geology. Unfortunately, it is prohibitively very
                  expensive to compute these CIG for all the
                  subsurface points. In this work, we present an
                  efficient way to compute CIG for all subsurface
                  offsets without explicitly calculating the source
                  and receiver wavefields for all the sources. Because
                  the CIG contain all possible subsurface offsets, we
                  compute the angle-domain image gathers by selecting
                  the subsurface offset that is aligned with the local
                  geologic dip. We propose a method to compute the
                  local dip information directly from
                  common-image-point gathers. To assess the quality of
                  the angle-domain common-image-points gathers we
                  compute the angle-dependent reflectivity
                  coefficients and compare them with theoretical
                  reflectivity coefficients yielded by the
                  (linearized) Zoeppritz equations for a few synthetic
                  models.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADava/kumar2013SINBADava.pdf},
  author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}  


@PRESENTATION{miao2013SINBADfid,
  title = {Fast imaging via depth stepping with the two-way wave equation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {In this presentation we propose a fast imaging algorithm
                  via depth stepping with the two-way wave
                  equation. Within the framework of survey sinking, a
                  stabilized depth extrapolation operator is computed
                  using a spectral projector which can efficiently
                  split evanescent wave components. The computation of
                  the spectral projector features with an
                  Hierarchically Semi-Seperable (HSS) matrix
                  representation speeded up polynomial recursion,
                  resulting in an accleration from cubic numerical
                  complexity to linear numerical complexity.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/miao2013SINBADfid/miao2013SINBADfid.pdf},
  author = {Lina Miao and Felix J. Herrmann}
}  


@PRESENTATION{zheglova2013SINBADihss,
  title = {Imaging with hierarchical semi separable matrices},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Hierarchically Semi Separable (HSS) matrices are (in
                  general) dense matrices that have low rank
                  off-diagonal blocks that can be represented
                  economically. Exploiting a specific structure of HSS
                  representation, fast algorithms have been devised
                  for matrix matrix multiplication, addition and
                  computing a matrix inverse. We are interested in
                  developing fast algorithms for seismic imaging using
                  ideas from this approach. An overview of HSS
                  representation and some methods using HSS
                  representations of operators will be shown in this
                  talk.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/zheglova2013SINBADihss/zheglova2013SINBADihss.pdf},
  author = {Polina Zheglova and Felix J. Herrmann}
}  


@PRESENTATION{tu2013SINBADfim,
  title = {Fast imaging with multiples and source estimation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {During this talk, we present a computationally efficient
                  (cost of 1-2 RTMs with all data) iterative
                  sparsity-promoting inversion framework where
                  surface-related multiples are jointly imaged with
                  primaries and where the source signature is
                  estimated on the fly. Our imaging algorithm is
                  computationally efficient because it works during
                  each iteration with small independent randomized
                  subsets of data. The multiples are handled by
                  introducing an areal source term that includes the
                  upgoing wavefield. We update the source signature
                  for each iteration using a variable projection
                  method. The resulting algorithm removes imaging
                  artifacts from surface-related multiples, estimates
                  and removes the imprint of the source, recovers true
                  amplitudes, is fast, and robust to linearization
                  errors by virtue of the statistical independence of
                  the subsets of data we are working with at each
                  iteration.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/tu2013SINBADfim/tu2013SINBADfim.pdf},
  author = {Ning Tu and Felix J. Herrmann}
}  


@PRESENTATION{fang2013SINBADuafwi,
  title = {Uncertainty analysis for {FWI}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Uncertainty analysis is important for seismic
                  interpretation. Based on the framework of Bayesian,
                  we can analyse different statistic parameters of our
                  FWI result. However, directly sampling the posterior
                  probability density function (pdf) is
                  computationally intractable. In order to make this
                  problem computationally tractable, in this work, we
                  use Gaussian distribution approximation and low rank
                  approximation to generate the posterior
                  pdf. Simultaneous shots are also used to reduce the
                  computational costs.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/fang2013SINBADuafwi/fang2013SINBADuafwi.pdf},
  author = {Zhilong Fang and Felix J. Herrmann}
}  


@PRESENTATION{li2013SINBADmsd,
  title = {Model-space versus data-space {FWI} with the acoustic wave equation},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Inverting data with elastic phases using an acoustic
                  wave equation can lead to erroneous results,
                  especially when the number of iterations is too
                  high, which may lead to over fitting the
                  data. Several approaches have been proposed to
                  address this issue. Most commonly, people data-
                  filtering operations that are aimed to deemphasize
                  the elastic phases in the data in favor of the
                  acoustic phases. Examples of this approach are
                  nested loops over offset range and Laplace
                  parameters. In this presentation, we discuss two
                  complementary optimization-driven methods where the
                  minimization process decides adaptively which of the
                  data or model components are consistent with the
                  objective. Specifically, we compare the s t misfit
                  function as the data-space alternative and
                  curvelet-domain sparsity promotion as the
                  model-space alternative. Application of these two
                  methods to a realistic synthetic lead to comparable
                  results that we believe can be improved by combining
                  these two methods.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/li2013SINBADmsd/li2013SINBADmsd.pdf},
  author = {Xiang Li and Anais Tamalet and Tristan van Leeuwen and Felix J. Herrmann}
}  


@PRESENTATION{tu2013SINBADmachar,
  title = {{SLIM's} findings on the {Machar} dataset},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {By courtesy of BP, we are able to get some hands-on
                  experience imaging seismic data from the Machar
                  field in North Sea. We performed reverse-time
                  migration and sparsity-promoting migration with
                  source-estimation to this dataset (or a cropped
                  section of the dataset in many cases due to
                  computational constraints), and had some interesting
                  findings. In this presentation, we will show some
                  conclusive results we have got, and explain the key
                  techniques in imaging this dataset.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/tu2013SINBADmachar/tu2013SINBADmachar.pdf},
  author = {Ning Tu and Felix J. Herrmann}
}  


@PRESENTATION{li2013SINBADgom,
  title = {Lessons learned from {Chevron} {Gulf} of {Mexico} data set},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Chevron Gulf of Mexico data set is very challenging for
                  FWI because of elastic phases, limited offset, lack
                  of low frequencies and salt structure. To overcome
                  these issue, we first use ray-based tomography on
                  the hand-picked first breaks to generate initial
                  model for FWI, and then we apply curvelet-denosing
                  techniques to improve the poor signal-to-noise ratio
                  of the observed data at low frequencies. Finally,
                  Curvelet domain sparsity promoting Gauss-Newton FWI
                  helps to suppress model space artifacts caused by
                  elastic phases. This is joint work with Andrew
                  J. Calvert, Ian Hanlon, Mostafa Javanmehri, Rajiv
                  Kumar, Tristan van Leeuwen, Brendan R. Smithyman,
                  Haneet Wason and Felix J. Herrmann},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/li2013SINBADgom/li2013SINBADgom.pdf},
  author = {Xiang Li and Andrew J. Calvert and Ian Hanlon and Mostafa Javanmehri and Rajiv Kumar and Tristan van Leeuwen and Brendan R. Smithyman and Haneet Wason and Felix J. Herrmann}
}  


@PRESENTATION{warner2013SINBADrfwi,
  title = {Reflection {FWI} with a poor starting model},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/warner2013SINBADrfwi/warner2013SINBADrfwi.pdf},
  author = {Mike Warner}
}  


@PRESENTATION{smithyman2013SINBADprb,
  title = {Phase-residual based quality-control methods and techniques for mitigating cycle skips},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Most full-waveform inversion algorithms use local
                  optimization methods to iteratively improve the
                  numerical earth model. All of these make the
                  implicit assumption that the the model close to the
                  true earth to avoid cycle. In practice this may not
                  be true. We explore two questions: 1. How do we
                  understand and visualize the cycle skip phenomenon
                  in order to recognize it if it exists? 2. How do we
                  automate this quality control step and use the rich
                  information from multiple data to mitigate cycle
                  skips and avoid local minima?},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/smithyman2013SINBADprb/smithyman2013SINBADprb.pdf},
  author = {Brendan R. Smithyman and Felix J. Herrmann}
}  


@PRESENTATION{fang2013SINBADp3dfwi,
  title = {Parallel {3D} {FWI} with simultaneous shots},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {In this work, we manage to build the workflow for the
                  parallel 3D full-waveform inversion. In the forward
                  simulation part, we generate the Helmholtz matrix in
                  parallel and use parallel CARPCG to solve the
                  Helmholtz equation. In the inversion process,
                  simultaneous shots are used to reduce the
                  computational costs. Additionally, we propose a
                  method to select the number of simultaneous shots
                  and tolerance of CARPCG dynamically, to reach a
                  compromise between the computational costs and
                  accuracy.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/fang2013SINBADp3dfwi/fang2013SINBADp3dfwi.pdf},
  author = {Zhilong Fang and Tristan van Leeuwen and Felix J. Herrmann}
}  


@PRESENTATION{kumar2013SINBADeia,
  title = {Extended images in action: efficient {WEMVA} via randomized probing},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Extended images as a function of the full subsurface
                  offset are an important tool to perform
                  wave-equation based migration velocity analysis
                  (WEMVA) in areas of complex geology. Unfortunately,
                  computation & storage of these extended images is
                  prohibitively expensive. In this work, we present an
                  efficient way to compute extended images for all
                  subsurface offsets without explicitly calculating
                  the source and receiver wavefields for all the
                  sources. Instead, we calculate actions of extended
                  image volumes on probing vectors that live in the
                  image space. The probing can either be defined as
                  vectors from the Dirac basis, which allows us to
                  form the extended image at the location of the point
                  diffractor, or they can be defined in terms of
                  Gaussian noise. The latter corresponds to sources
                  with random weights firing simultaneously at every
                  grid point. We demonstrate that this probing leads
                  to a computationally efficient implementation of
                  WEMVA. This is joint work with Tristan van Leeuwen.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADeia/kumar2013SINBADeia.pdf},
  author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}  


@PRESENTATION{peters2013SINBADepm,
  title = {Examples from the penalty-method},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {A novel penalty method for PDE-constrained optimization
                  was recently proposed by van Leeuwen and Herrmann
                  (2013). The conventional PDE-constrained
                  optimization formulation used in seismic waveform
                  inversion is based on calculating the gradient of
                  the data misfit objective functional via the
                  adjoint-state method, at the cost of two PDE
                  solves. The penalty method requires only one
                  solution of an overdetermined linear system, in the
                  least-squares sense. In this talk some numerical
                  properties of this linear system will be
                  exposed. The penalty method for PDE-constrained
                  optimization involves a parameter, balancing the
                  data misfit and PDE-misfit parts of the objective
                  functional. This talk will address how to select
                  this very important parameter. Some examples will be
                  shown in which the penalty method outperforms the
                  conventional method in non-linear waveform
                  inversion, as well as linearized seismic imaging by
                  migration.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/peters2013SINBADepm/peters2013SINBADepm.pdf},
  author = {Bas Peters and Tristan van Leeuwen and Felix J. Herrmann}
}  


@PRESENTATION{herrmann2013SINBADffwi,
  title = {Frugal {FWI}},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Seismic waveform inversion aims at obtaining detailed
                  estimates of subsurface medium parameters, such as
                  soundspeed, from seismic data. A formulation in the
                  frequency-domain leads to an optimization problem
                  constrained by a Helmholtz equation with many
                  right-hand sides. Application of this technique in
                  3D precludes the use of factorization techniques to
                  solve the Helmholtz equation due the large number of
                  gridpoints and the bandwidth of the matrix. While
                  many sophisticated pre-conditioned iterative
                  techniques have been developed for the Helmholtz
                  equation, they often include model-specific tuning
                  parameters and are thus not very attractive for
                  inversion since the medium parameters change from
                  one iteration to the next. In this paper, we propose
                  a method for 3D seismic waveform inversion that
                  addresses both the need to efficiently solve the
                  Helmholtz equation as well as the computational cost
                  induced by the many right-hand sides. To solve the
                  Helmholtz equation, we consider a simple generic
                  preconditioned iterative method (CARP-CG) that is
                  well-suited for inversion because of its
                  robustness. We extend this method to a
                  block-iterative method that can efficiently handle
                  multiple right-hand sides. To reduce the
                  computational cost of of the overall optimization
                  procedure, we use recently proposed techniques from
                  stochastic optimization that allow us to work with
                  approximate gradient information. These
                  approximations are obtained by evaluating only a
                  small portion of the right-hand sides and/or by
                  solving the PDE approximately. We propose heuristics
                  to adaptively determine the required accuracy of the
                  PDE solves and the sample-size and illustrate the
                  algorithms on synthetic benchmark models. This is
                  joint work with Tristan van Leeuwen.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/herrmann2013SINBADffwi/herrmann2013SINBADffwi.pdf},
  author = {Felix J. Herrmann}
}  


@PRESENTATION{vanleeuwen2013SINBADrpp,
  title = {Relaxing the physics: a penalty method for full-waveform inversion},
  booktitle = {SINBAD Fall consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Computationally efficient method of solving partial
                  differential equation (PDE)-constrained optimization
                  problems that occur in (geophysical) inversion
                  problems. The method takes measured data from a
                  physical system as input and minimizes an objective
                  function that depends on an unknown model for the
                  physical parameters, fields, and additional nuisance
                  parameters such as the source function. The
                  invention consists of a minimization procedure
                  involving a cost functional comprising of a
                  data-misfit term and a penalty term that measures
                  how accurately the fields satisfy the PDE. The
                  method is composed of two alternating steps, namely
                  the solution of a system of equations forming the
                  discretization of the data-augmented PDE, and the
                  solution of physical model parameters from the PDE
                  itself given the field that solves the
                  data-augmented system and an estimate for the
                  sources. Compared to all-at-once approaches to
                  PDE-constrained optimization, there is no need to
                  update and store the fields for all sources leading
                  to significant memory savings. As in the all-at-once
                  approach, the proposed method explores a larger
                  search space and is therefore less sensitive to
                  initial estimates for the physical model
                  parameters. Contrary to the reduced formulation, the
                  proposed method does not require the solution of an
                  adjoint PDE, effectively halving the number of PDE
                  solves and memory requirement. As in the reduced
                  formulation, fields can be computed independently
                  and aggregated, possibly in parallel.},
  keywords = {presentation, SINBAD, SINBADFALL2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/vanleeuwen2013SINBADrpp/vanleeuwen2013SINBADrpp.pdf},
  author = {Tristan van Leeuwen and Felix J. Herrmann}
}  


%----- 2013 (SPRING) -----%

@PRESENTATION{dasilva2013SINBADSPRhtuck,
  title = {Hierarchical {Tucker} tensor optimization - applications to {4D} seismic data interpolation},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {In this work, we develop optimization algorithms on the
                  manifold of Hierarchical Tucker (HT) tensors, an
                  extremely efficient format for representing
                  high-dimensional tensors exhibiting particular
                  low-rank structure. With some minor alterations to
                  existing theoretical developments, we develop an
                  optimization framework based on the geometric
                  understanding of HT tensors as a smooth manifold, a
                  generalization of smooth curves/surfaces. Building
                  on the existing research of solving optimization
                  problems on smooth manifolds, we develop Steepest
                  Descent and Conjugate Gradient methods for HT
                  tensors. The resulting algorithms converge quickly,
                  are immediately parallelizable, and do not require
                  the computation of SVDs. We also derive efficient
                  Gauss-Newton based algorithms which converge much
                  faster than standard, first-order methods. We also
                  extend ideas about favourable sampling conditions
                  for missing-data recovery from the field of Matrix
                  Completion to Tensor Completion and demonstrate how
                  the organization of data can affect the success of
                  recovery. As a result, if one has data with randomly
                  missing source pairs, using these ideas, coupled
                  with an efficient solver, one can interpolate
                  large-scale seismic data volumes with missing
                  sources and/or receivers by exploiting the
                  multidimensional dependencies in the data. We are
                  able to recover data volumes amidst extremely high
                  subsampling ratios (in some cases, > 75\%) using this
                  approach and we demonstrate our recovery on a
                  synthetic 5D data set provided to us by BG.},
  keywords = {presentation, SINBAD, SINBADSPRING2013, SLIM, Hierarchical Tucker, Structured Tensor, 3D Data interpolation, structured tensor},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/dasilva2013SINBADSPRhtuck/dasilva2013SINBADSPRhtuck.pdf},
  author = {Curt Da Silva and Felix J. Herrmann}
}


@PRESENTATION{herrmann2013SINBADmlm,
  title = {Mitigating local minima in full-waveform inversion by expanding the search space with the penalty method},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Wave-equation based inversions, such as full-waveform
                  inversion, are challenging because of their
                  computational costs, memory requirements, and
                  reliance on accurate initial models. To confront
                  these issues, we propose a novel formulation of
                  full-waveform inversion based on a penalty
                  method. In this formulation, the objective function
                  consists of a data-misfit term and a penalty term
                  which measures how accurately the wavefields satisfy
                  the wave-equation. Because we carry out the
                  inversion over a larger search space, including both
                  the model and synthetic wavefields, our approach
                  suffers less from local minima. Our main
                  contribution is the development of an efficient
                  optimization scheme that avoids having to store and
                  update the wavefields by explicit
                  elimination. Compared to existing optimization
                  strategies for full-waveform inversion, our method
                  differers in two main aspects; i. The wavefields are
                  solved from an augmented wave-equation, where the
                  solution is forced to solve the wave-equation and
                  fit the observed data, ii. no adjoint wavefields are
                  required to update the model, which leads to
                  significant computational savings. We demonstrate
                  the validity of our approach by carefully selected
                  examples and discuss possible extensions and future
                  research. This is joint work with Tristan van
                  Leeuwen.},
  keywords = {presentation, SINBAD, SINBADSPRING2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/herrmann2013SINBADmlm/herrmann2013SINBADmlm.pdf},
  author = {Felix J. Herrmann}  
}


@PRESENTATION{herrmann2013SINBADfwi,
  title = {Frugal {FWI}},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Seismic waveform inversion aims at obtaining detailed
                  estimates of subsurface medium parameters, such as
                  soundspeed, from seismic data. A formulation in the
                  frequency-domain leads to an optimization problem
                  constrained by a Helmholtz equation with many
                  right-hand- sides. Application of this technique in
                  3D precludes the use of factorization techniques to
                  solve the Helmholtz equation due the large number of
                  gridpoints and the bandwidth of the matrix. While
                  many sophisticated pre-conditioned iterative
                  techniques have been developed for the Helmholtz
                  equation, they often include model-specific tuning
                  parameters and are thus not very attractive for
                  inversion since the medium parameters change from
                  one iteration to the next. In this paper, we propose
                  a method for 3D seismic waveform inversion that
                  addresses both the need to efficiently solve the
                  Helmholtz equation as well as the computational cost
                  induced by the many right-hand-sides. To solve the
                  Helmholtz equation, we consider a simple generic
                  preconditioned iterative method (CARP-CG) that is
                  well-suited for inversion because of its
                  robustness. We extend this method to a
                  block-iterative method that can efficiently handle
                  multiple right-hand sides. To reduce the
                  computational cost of of the overall optimization
                  procedure, we use recently proposed techniques from
                  stochastic optimization that allow us to work with
                  approximate gradient information. These
                  approximations are obtained by evaluating only a
                  small portion of the right-hand sides and/or by
                  solving the PDE approximately. We propose heuristics
                  to adaptively determine the required accuracy of the
                  PDE solves and the sample-size and illustrate the
                  algorithms on synthetic benchmark models. This is
                  joint work with Tristan van Leeuwen.},
  keywords = {presentation, SINBAD, SINBADSPRING2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/herrmann2013SINBADfwi/herrmann2013SINBADfwi.pdf},
  author = {Felix J. Herrmann}  
}


@PRESENTATION{herrmann2013SINBADeia,
  title = {Extended images in action},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2013},
  abstract = {Image gathers as a function of subsurface offset are an
                  important tool for migration-velocity and
                  amplitude-versus-angle analysis in areas of complex
                  geology. Traditionally, these gathers are thought of
                  as multidimensional correlations of the source and
                  receiver wavefields. The bottleneck in computing the
                  gathers lies in the fact that one needs to store and
                  compute these wavefields and in correlating the
                  wavefields to obtain the desired image
                  gathers. Therefore, the image gathers are typically
                  only computed for a limited number of subsurface
                  points and for a limited range of subsurface
                  offsets. In this presentation, we offer a new
                  perspective on such gathers by organizing the
                  extended image as a function of all subsurface
                  offsets for all subsurface points in a matrix whose
                  (i,j) entry captures the interaction between
                  gridpoints i and j. Of course, it is infeasible to
                  form and store this matrix. Instead, we propose an
                  efficient algorithm to glean information from the
                  image volume via the action of matrix-vector
                  products with this matrix. We illustrate how this
                  can be used to (i) form conventional image gathers
                  and construct objective functions for automated MVA
                  and (ii) calculate true two-way wave-equation angle
                  gathers that allow us to carry out linearized
                  angle-versus-offset inversion with geologic-dip
                  correction. This is joint work with Tristan van
                  Leeuwen and Rajiv Kumar.},
  keywords = {presentation, SINBAD, SINBADSPRING2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/herrmann2013SINBADeia/herrmann2013SINBADeia.pdf},
  author = {Felix J. Herrmann}  
}


@PRESENTATION{herrmann2013SINBADldc,
  title = {Latest developments on the {Chevron} {GOM} and other datasets},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2013},  
  abstract = {During this talk, we will give a brief overview on our
                  imaging and FWI results on various synthetic and
                  field datasets we have been working on. This is
                  joint work with Andrew Calvert, Brendan R. Smithyman,
                  and the SLIM team.},
  keywords = {presentation, SINBAD, SINBADSPRING2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/herrmann2013SINBADldc/herrmann2013SINBADldc.pdf},
  author = {Felix J. Herrmann}  
}


@PRESENTATION{kumar2013SINBADhss,
  title = {Seismic data interpolation via low-rank matrix factorization in the hierarchical semi-separable representation},
  booktitle = {SINBAD Spring consortium talks},  
  organization = {SINBAD},
  year = {2013},  
  abstract = {Recent developments in matrix rank optimization have
                  allowed for new computational approaches in the
                  field of seismic data interpolation.  One of the
                  main requirements of exploiting rank-minimization
                  approaches is that the target data set should
                  exhibit a low-rank structure. Seismic frequency
                  slices exhibit low-rank structure at the
                  low-frequencies, but not at the high
                  frequencies. This behavior is due to the increase in
                  oscillations as we move from low to high-frequency
                  slices, even though the energy remains focused
                  around the diagonal. Therefore, interpolation via
                  rank minimization in the high-frequency range
                  requires extended formulations that incorporate
                  low-rank structure. We propose an approach for
                  seismic data interpolation which incorporates the
                  Hierarchical Semi-Separable Structure (HSS) inside
                  rank-regularized least-squares formulations for the
                  missing-trace interpolation problem. The proposed
                  approach is suitable for large scale problems, since
                  it avoids SVD computations and uses a low-rank
                  factorized formulation instead. We illustrate the
                  advantages of the new HSS approach by interpolating
                  a seismic line from the Gulf of Suez and compare the
                  reconstruction with conventional rank minimization.},
  keywords = {presentation, SINBAD, SINBADSPRING2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/kumar2013SINBADhss/kumar2013SINBADhss.pdf},
  author = {Rajiv Kumar and Hassan Mansour and Aleksandr Y. Aravkin and Felix J. Herrmann}  
}


@PRESENTATION{lin2013SINBADcsd,
  title = {Cosparse seismic data interpolation},
  booktitle = {SINBAD Spring consortium talks},
  organization = {SINBAD},
  year = {2013},
  abstract = {Over the years we have investigated seismic data
                  interpolation and redatuming algorithms rely on on
                  the assumption that seismic records and images
                  permit sparse approximations under certain
                  representations, such as Curvelet
                  coefficients. Recent findings have suggested that
                  for redundant representations (of which Curvelet is
                  an example), the analysis operator that maps the
                  physical signal to coefficients may also play a
                  crucial role in recovering data from incomplete
                  observations. This insight elevates the significance
                  of a question that often goes unaddressed: is it
                  better for the transform-domain sparsity to be
                  achieved through explicit construction of sparse
                  representations (e.g., by thresholding of small
                  transform-domain coefficients), or by demanding that
                  the algorithm return physical signals which produces
                  sparse coefficients when hit with the forward
                  transform? Recent results show that the two
                  approaches give rise to different solutions when the
                  transform is redundant, and that the latter approach
                  imposes a whole new class of constraints. In
                  particular, the number of zero-valued coefficients
                  given by the analysis operator acting on the signal,
                  referred to as its "cosparsity", have an analogous
                  role to the sparsity of the signal in terms of the
                  coefficients. From this framework, a new
                  reconstruction algorithm is proposed which may allow
                  better reconstruction from subsampled signaled than
                  what the sparsity assumption alone would predict. In
                  this work we apply the new framework and algorithm
                  to the case of seismic data interpolation under the
                  curvelet domain, and show that it admits better
                  reconstruction than some existing L1 sparsity-based
                  methods derived from compressive sensing for a range
                  of subsampling factors. We will also investigate
                  different analysis operators and their impact on
                  both sparsity and cosparsity-based algorithms.},
  keywords = {presentation, SINBAD, SINBADSPRING2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/lin2013SINBADcsd/lin2013SINBADcsd.pdf},
  author = {Tim T.Y. Lin and Felix J. Herrmann}
}


@PRESENTATION{tu2013SINBADvp,
  title = {Fast imaging with multiples and source estimation},
  booktitle = {SINBAD Spring consortium talks},
  organization = {SINBAD},
  year = {2013},
  abstract = {Multiples are usually treated as unwanted components in
                  seismic data. However, if correctly used, they can
                  provide valuable information about the
                  subsurface. In this presentation, I will talk about
                  how to make use of multiples in $\ell_1$ regularized
                  least-squares imaging, and how to estimate the
                  source wavelet on the fly using multiples. Synthetic
                  examples show that using multiples not only helps to
                  retrieve the true-amplitude seismic image and the
                  source wavelet, it also increases the wavenumber
                  contents in the seismic image. By using
                  dimensionality reduction techniques with
                  rerandomization, we also greatly decrease the
                  simulation cost without compromising the image
                  quality.},
  keywords = {presentation, SINBAD, SINBADSPRING2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/tu2013SINBADvp/tu2013SINBADvp.pdf},
  author = {Ning Tu and Aleksandr Y. Aravkin and Tristan van Leeuwen and Felix J. Herrmann}
}


@PRESENTATION{tu2013SINBADrerand,
  title = {Controlling linearization errors with rerandomization},
  booktitle = {SINBAD Spring consortium talks},
  organization = {SINBAD},
  year = {2013},      
  abstract = {Least squares migration aims to fit the observed seismic
                  data with data predicted by linearized modelling, by
                  solving an PDE-constrained optimization
                  problem. This problem is challenging mostly because
                  of its prohibitive computational cost. To address
                  the issue, dimensionality reduction techniques were
                  proposed in the literature. However, the solution of
                  the reduced problem can deviate from that of the
                  full problem when there are components in the
                  observed data that cannot be explained by linearized
                  modelling. We solve the problem by rerandomizing our
                  $\ell_1$ regularized inversion. In this
                  presentation, I will explain the method and
                  demonstrate what we can achieve with
                  rerandomization, especially for resolving fine
                  sub-salt structures.},
  keywords = {presentation, SINBAD,  SINBADSPRING2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/tu2013SINBADrerand/tu2013SINBADrerand.pdf},
  author = {Ning Tu and Felix J. Herrmann}
}


@PRESENTATION{wason2013SINBADtjo,
  title = {Time-jittered ocean bottom seismic acquisition},
  booktitle = {SINBAD Spring consortium talks},
  organization = {SINBAD},
  year = {2013},
  abstract = {Leveraging ideas from the field of compressed sensing,
                  we show how simultaneous or blended acquisition can
                  be setup as a -- compressed sensing problem. This
                  helps us to design a pragmatic time-jittered marine
                  acquisition scheme where multiple source vessels
                  sail across an ocean-bottom array firing airguns at
                  -- jittered source locations and instances in time,
                  resulting in better spatial sampling, and speedup
                  acquisition. Furthermore, we can significantly
                  impact the reconstruction quality of conventional
                  seismic data (from jittered data) and demonstrate
                  successful recovery by sparsity promotion. In
                  contrast to random (under)sampling, acquisition via
                  jittered (under)sampling helps in controlling the
                  maximum gap size, which is a practical requirement
                  of wavefield reconstruction with localized
                  sparsifying transforms. Results are illustrated with
                  simulations of time-jittered marine acquisition,
                  which translates to jittered source locations for a
                  given speed of the source vessel.},
  keywords = {presentation, SINBAD, SINBADSPRING2013, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/wason2013SINBADtjo/wason2013SINBADtjo.pdf},
  author = {Haneet Wason and Felix J. Herrmann}
}


%----- 2012 (FALL) -----%

@PRESENTATION{Akalin2012SINBADlss,
  title = {Large scale seismic data interpolation with matrix completion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {Seismic surveys amass large and incomplete data
                  sets, and designing algorithms to interpolate the
                  missing data at very large scales poses a daunting
                  and critical challenge.  We study how to apply
                  scalable matrix completion methods to such
                  interpolation problems.  Recent studies in matrix
                  completion have shown that a matrix that has low
                  rank can be exactly completed when only a small
                  number of observations are available. However, there
                  are two challenges to applying matrix completion to
                  seismic data. Matrix completion is typically applied
                  to two dimensional or dyadic data whereas seismic
                  data is often tensorial. Also successful matrix
                  completion requires a low-rank matrix structure. We
                  address these problems by organizing the seismic
                  data on a matrix grid which exhibits a low-rank
                  structure. This encoding allows us to apply the
                  Jellyfish algorithm, developed at the University of
                  Wisconsin, which achieves state-of-the-art
                  performance for large-scale matrix completion. The
                  proposed framework makes it possible to complete
                  high-SNR interpolations of gigabytes of 4-D seismic
                  data in minutes on standard multicore
                  workstations. Our preliminary experimental results
                  suggest that matrix completion provides a promising
                  new approach to the seismic data interpolation
                  problem.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/Akalin2012SINBADlss/Akalin2012SINBADlss_pres.pdf},
  author = {Okan Akalin}
}


@PRESENTATION{aravkin2012SINBADenp,
  title = {Estimating nuisance parameters in inverse problems},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {Many inverse problems include nuisance parameters
                  which, while not of direct interest, are required to
                  recover primary parameters. In this talk, we present
                  the idea of "projecting out" these variables, and
                  how this idea allows us to design methods for
                  solving a broad class of problems with nuisance
                  parameters, such as variance or degrees of
                  freedom. We then discuss several geophysical
                  applications, including including estimation of
                  unknown variance parameters in the Gaussian model
                  for full waveform inversion, degree of freedom
                  (d.o.f.) parameter estimation in the context of
                  robust imaging problems, and robust source
                  estimation.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/aravkin2012SINBADenp/aravkin2012SINBADenp_pres.pdf},
  author = {Aleksandr Y. Aravkin}
}


@PRESENTATION{aravkin2012SINBADgsf,
  title = {Generalized {SPGL1}: from theory to applications},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {The SPGL1 solver has been effectively used for many
                  geophysical applications, including curvelet data
                  interpolation, imaging, and as a subroutine in full
                  waveform inversion. In this talk, we present an
                  overview of the theoretical foundation of the
                  solver, along with a broad generalization of this
                  foundation. We then introduce several applications,
                  including robust & sparse imaging, sparse
                  deconvolution, and data interpolation by matrix
                  completion.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/aravkin2012SINBADgsf/aravkin2012SINBADgsf_pres.pdf},
  author = {Aleksandr Y. Aravkin}
}


@PRESENTATION{au-yeung2012SINBADcs,
  title = {Compressed sensing, random {Fourier} matrix and jitter sampling},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {Compressed sensing is an emerging signal processing
                  technique that allows signals to be sampled well
                  below the Nyquist rate, when the signal has a sparse
                  representation in an orthonormal basis. By using a
                  random Fourier matrix or a Gaussian matrix as our
                  measurement matrix, we can reconstruct a signal from
                  far fewer measurements than required by Shannon
                  sampling theorem. In this talk, we will discuss the
                  role of uniform versus jitter sampling, both in a
                  theoretical and practical viewpoint.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/au-yeung2012SINBADcs/au-yeung2012SINBADcs_pres.pdf},
  author = {Enrico Au-Yeung and Hassan Mansour and Ozgur Yilmaz}
}


@PRESENTATION{dasilva2012SINBADhtt,
  title = {Hierarchical {Tucker} tensor optimization - applications to {4D} seismic data interpolation},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {There has been a swell of research in the scientific
                  computing community in the last couple of years
                  which tries to extend notions of linear algebra
                  (rank, the SVD, linear systems, etc.) to higher
                  dimensional arrays, or tensors. Much work has been
                  proposed to try to overcome the so called "curse of
                  dimensionality", the O(N^d) storage required for a
                  d-dimensional array, where N is the size of each
                  dimension. The hierarchical Tucker format is one
                  such tensor representation which manages to
                  decompose a hierarchy of dimensions into parameter
                  matrices of very manageable size, requiring at most
                  dNK + (d - 2)K^3 + K^2 parameters, where K is an
                  internal rank parameter. In this work, we extend
                  ideas of matrix completion to the tensor case, where
                  we only know a small number of randomly distributed
                  entries from various 4D frequency slices, and try to
                  recover the fully sampled tensor based on the
                  knowledge that it has low hierarchical tucker rank
                  in a particular arrangement of dimensions. Using
                  this approach, we exploit the multi-dimensional
                  dependencies within the full data in order to
                  achieve very promising interpolation results even
                  from heavily subsampled data.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/dasilva2012SINBADhtt/dasilva2012SINBADhtt_pres.pdf},
  author = {Curt Da Silva}
}


@PRESENTATION{friedlander2012SINBADrsh,
  title = {Randomized sampling: {How} confident are you?},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {At last year's consortium meeting, I described an
                  inexact gradient method and sampling scheme for data
                  fitting. The randomization method has good
                  convergence properties, at least as measured by the
                  distance to the solution----in expectation. But as
                  one insightful critic rightly pointed out, we don't
                  usually observe the expectation, at least not in a
                  single run. In this talk I will characterize the
                  convergence of the method in terms of bounds on the
                  probability of being too far away from the
                  solution.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/friedlander2012SINBADrsh/friedlander2012SINBADrsh_pres.pdf},
  author = {Michael P. Friedlander}
}


@PRESENTATION{ghadermarzy2012SINBADncc,
  title = {Non-convex compressed sensing using partial support information},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {In this talk, we will address the recovery
                  conditions of weighted $\ell_p$ minimization for
                  signal reconstruction from compressed sensing
                  measurements when (possibly inaccurate) partial
                  support information is available. First we will
                  motivate the use of (weighted) $\ell_p$ minimization
                  with $p<1$ and point out its advantages over
                  weighted $\ell_1$ minimization when there is prior
                  information on the support of the signal that is
                  possibly partial and inaccurate. Then we will
                  provide theoretical guarantees of sufficient
                  recovery conditions for weighted $\ell_p$
                  minimization, which are better than those for
                  (unweighted) $\ell_p$ minimization as well as those
                  for weighted $\ell_1$. In the last part of the talk,
                  we will illustrate our results with some numerical
                  experiments stylized applications.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/ghadermarzy2012SINBADncc/ghadermarzy2012SINBADncc_pres.pdf},
  author = {Navid Ghadermarzy}
}


@PRESENTATION{hargreaves2012SINBADavs,
  title = {Analysis versus synthesis in weighted sparse recovery},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {The synthesis model for compressive sensing has been the
                  model of choice for many years and various weighting
                  schemes have been shown to improve it's performance
                  (see Yilmaz, Mansour, and Ghadermarzy
                  talks). However, there is a counterpart model to
                  synthesis, namely the analysis model, which has been
                  less popular but recently attracted more attention
                  (see Lin's talk). In this talk, weighting in the
                  analysis model is discussed and applied to the
                  seismic trace interpolation problem.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/hargreaves2012SINBADavs/hargreaves2012SINBADavs_pres.pdf},
  author = {Brock Hargreaves}
}


@PRESENTATION{herrmann2012SINBADals,
  title = {Fast sparsity-promoting imaging with message passing},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {To meet current-day challenges, exploration
                  seismology increasingly relies on more and more
                  sophisticated algorithms that require multiple paths
                  through all data. This requirement leads to problems
                  because the size of seismic data volumes is
                  increasing exponentially, exposing bottlenecks in IO
                  and computational capability. To overcome these
                  bottlenecks, we follow recent trends in machine
                  learning and compressive sensing by proposing a
                  sparsity-promoting inversion technique that works on
                  small randomized subsets of data only. We boost the
                  performance of this algorithm significantly by
                  modifying a state-of-the-art l1-norm solver to
                  benefit from message passing, which breaks the build
                  up of correlations between model iterates and the
                  randomized linear forward model. We demonstrate the
                  performance of this algorithm on a toy
                  sparse-recovery problem and on a realistic
                  reverse-time-migration example with random source
                  encoding. The improvements in speed, memory use, and
                  output quality are truly remarkable.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/herrmann2012SINBADals/herrmann2012SINBADals_pres.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{herrmann2012SINBADfwi,
  title = {Our findings on the {Chevron} benchmark dataset},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {During this presentation, we will review our findings
                  working with the synthetic GOM data released as part
                  of the post SEG workshop:"Gulf of Mexico Imaging
                  Challenges: What Can Full Waveform Inversion
                  Achieve?". This is joint work with Andrew
                  J. Calvert, Ian Hanlon, Mostafa Javanmehri, Rajiv
                  Kumar, Tristan van Leeuwen, Xiang Li, Brendan
                  R. Smithyman, Eric Takam Takougang, and Haneet
                  Wason.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/herrmann2012SINBADfwi/herrmann2012SINBADfwi_pres.pdf},
  author = {Andrew J. Calvert and Ian Hanlon and Mostafa Javanmehri and Rajiv Kumar and Tristan van Leeuwen and Xiang Li and Brendan R. Smithyman and Eric Takam Takougang and Haneet Wason and Felix J. Herrmann}
}


@PRESENTATION{herrmann2012SINBADhpc,
  title = {{SLIM's} perspective on {HPC} & big data},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/herrmann2012SINBADhpc/herrmann2012SINBADhpc_pres.pdf},
  author = {Tim T.Y. Lin and Tristan van Leeuwen and Felix J. Herrmann}
}


@PRESENTATION{krislock2012SINBADwsn,
  title = {Wireless sensor network localization},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {Locating the position of sensors connected together
                  in a wireless network given only the position of a
                  small number of the sensors and estimates of some of
                  the distances between the sensors is a difficult
                  problem with many modern applications. Within the
                  last few years research in wireless sensor network
                  localization has greatly increased due to the many
                  new applications using wireless sensors, from
                  lightweight sensors used to monitor the environment
                  to ocean-bottom sensors used in geophysical
                  applications. A second reason for this increased
                  interest is our recent ability to efficiently solve
                  these problems using modern semidefinite
                  optimization solvers. We will discuss how
                  semidefinite optimization can be used to solve such
                  problems and possible directions for future work.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/krislock2012SINBADwsn/krislock2012SINBADwsn_pres.pdf},
  author = {Nathan Krislock}
}


@PRESENTATION{kumar2012SINBADsdi,
  title = {Seismic data interpolation using {SVD} free {Pareto} curve based low rank optimization},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {Seismic data acquisition is cursed by missing data
                  caused by physical and/or budget constraints. Aim of
                  interpolation technique is to spatially transform
                  irregularly acquired data to regularly sampled data
                  while maintaining the events coherency. While
                  transform-domain sparsity promotion has proven to be
                  an effective tool to solve this recovery problem,
                  recent developments in Rank penalizing techniques
                  opens new horizon to improved recovery by exploiting
                  low-rank structure. A major downside of current
                  state of the art techniques is their reliance on the
                  SVD of seismic data structures, which can be
                  prohibitively expensive. Fortunately, recent work
                  allows us to circumvent this problem by working with
                  matrix factorizations. We review a novel approach to
                  rank penalization, and successfully apply it to the
                  seismic interpolation problem by exploiting the
                  low-rank structure of seismic data. Experiments for
                  the recovery of 2D and 3D acquisition support the
                  feasibility and potential of the new approach.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/kumar2012SINBADsdi/kumar2012SINBADsdi_pres.pdf},
  author = {Rajiv Kumar}
}


@PRESENTATION{vanleeuwen2012SINBAD3dfd,
  title = {{3D} frequency-domain waveform inversion using a row-projected {Helmholtz} solver},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {3D frequency-domain full waveform inversion relies on
                  being able to efficiently solve the 3D Helmholtz
                  equation.  Iterative methods require sophisticated
                  preconditioners because the Helmholtz matrix is
                  typically indefinite. In the first part of the talk
                  I review a preconditioning technique that is based
                  on row-projections. Notable advantages of this
                  preconditioner over existing ones are that it has
                  low algorithmic complexity, is easily parallelizable
                  and extendable to time-harmonic vector equations. In
                  the second part of the talk I discuss how the
                  row-projected solver can be used in the context of
                  waveform inversion. Key aspects are: the use of
                  block-iterative methods for multiple sources and
                  adapting the accuracy of the solver.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/vanleeuwen2012SINBAD3dfd/vanleeuwen2012SINBAD3dfd_pres.pdf},
  author = {Tristan van Leeuwen}
}


@PRESENTATION{vanleeuwen2012SINBADyap,
  title = {Yet another perspective on image volumes},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {An extended image is defined as the multi-dimensional
                  cross-correlation of the source and receiver
                  wavefields used for imaging. This extended image
                  will reveal velocity errors by de-focusing and can
                  thus be used for velocity analysis. However, for
                  optimal sensitivity to velocity errors, the
                  subsurface offset has to be aligned with the local
                  dip. As this dip is not known a priori, we consider
                  forming the extended image for subsurface offsets in
                  all directions. However, computing and storing such
                  a large image volume is not computationally
                  feasible.  We organize the image volume in a matrix
                  and use matrix-probing techniques to glean
                  information form the matrix without explicitly
                  forming it. A matrix-vector multiply with the
                  image-volume matrix can be performed at the cost of
                  two wave-equation solves and does not require any
                  explicit cross-correlations of the wavefields. Such
                  techniques can also be used to evaluate focusing
                  penalties without forming the whole image volume.
                  Finally, the matrix-viewpoint allows us to derive a
                  2-way equivalent of the DSR equation in a
                  straightforward manner and provides a possible
                  avenue for developing new velocity-continuation
                  techniques.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/vanleeuwen2012SINBADyap/vanleeuwen2012SINBADyap_pres.pdf},
  author = {Tristan van Leeuwen}
}


@PRESENTATION{lin2012SINBADics,
  title = {An introduction to cosparse signal reconstruction},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {Undersampling techniques in exploration seismology
                  usually relies on the assumption that seismic
                  records and images permit sparse approximations
                  under certain representations, such as Curvelet
                  coefficients. Recent findings have suggested that
                  for redundant representations (of which Curvelet is
                  an example), the analysis operator that maps the
                  physical signal to coefficients may also play a
                  crucial role in recovering data from incomplete
                  observations. In particular, the number of
                  zero-valued coefficients given by the analysis
                  operator acting on the signal, referred to as its
                  "cosparsity", have an analogous role to the sparsity
                  of the signal in terms of the coefficients. The
                  cosparsity of the signal permits recovery guarantees
                  that are completely separate from sparsity-based
                  models, and gives rise to distinct sets of
                  reconstruction algorithms and performances compared
                  to sparsity-based approaches. We present in this
                  talk some initial findings on the viability of
                  cosparse reconstruction for a variety of seismic
                  applications that previously relied on sparse signal
                  reconstruction, such as data interpolation and
                  source separation.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/lin2012SINBADics/lin2012SINBADics_pres.pdf},
  author = {Tim T.Y. Lin}
}


@PRESENTATION{Lin2012SINBADrdr,
  title = {Recent developments on the robust estimation of primaries by sparse inversion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {Robust estimation of primaries by sparse inversion
                  is a next-generation surface multiple removal
                  technique with an objective to truly invert an
                  operator that models the free-surface. Key to the
                  success of this approach is the imposition of a
                  sparsity constraint on the primary impulse response
                  in the time domain. This is accomplished by
                  carefully applying large-scale convex optimization
                  techniques on an extended L1 minimization
                  problem. One of the benefits of our approach is that
                  many extensions to the algorithm can be devised
                  under this optimization framework to improve the
                  quality of the solution given fixed computational
                  costs and mitigating various shortcomings in field
                  data. This talk will first review the basic
                  technique of Robust EPSI and follow with some
                  highlights on recent further developments of the
                  algorithm, including a discussion on the role of
                  regularization by reciprocity and the interpolation
                  of near-offset data, as well as investigations into
                  optimality and robustness to data outliers.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/Lin2012SINBADrdr/Lin2012SINBADrdr_pres.pdf},
  author = {Tim T.Y. Lin}
}


@PRESENTATION{mansour2012SINBADsti,
  title = {Seismic trace interpolation via sparsity promoting reweighted algorithms},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {Missing-trace interpolation aims to reconstruct
                  regularly sampled wavefields from periodically
                  sampled data with gaps caused by physical
                  constraints. While transform-domain sparsity
                  promotion has proven to be an effective tool to
                  solve this recovery problem, current recovery
                  techniques make no use of a priori information on
                  the transform-domain coefficients. To overcome these
                  vulnerabilities in solving the recovery problem for
                  large-scale problems, we propose recovery by
                  weighted one-norm minimization, which exploits
                  correlations between locations of significant
                  coefficients of different partitions, e.g., shot
                  records, common-offset gathers, or frequency slices,
                  of the acquired data. Moreover, in situations where
                  no prior support estimate is available, we propose
                  the WSPGL1 algorithm that outperforms standard
                  $\ell_1$ minimization in finding sparse solutions to
                  underdetermined linear systems of equations. Our
                  algorithm is a modification of the SPGL1 algorithm
                  and enjoys better sparse recovery performance at no
                  additional computational cost. We illustrate the
                  improved recovery using WSPGL1 for randomly
                  subsampled seismic traces.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/mansour2012SINBADsti/mansour2012SINBADsti_pres.pdf},
  author = {Hassan Mansour}
}


@PRESENTATION{miao2012SINBADasp,
  title = {Accelerating on sparse promoting recovery and its benefits in seismic application},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {Sparse promoting recovery problem arises more and more
                  frequently with the broad application of compressed
                  sensing tool in exploration seismology. Because of
                  the curse of dimensionality, the prohibitive
                  computation burden on iteratively evaluating
                  objective functions is one of the key issues that
                  constrain high performance l1 solver. In this paper,
                  we try to further improve the convergence
                  performance of SPGl1, one of the state-of-the-art
                  large scale sparse recovery solver, and as a result
                  limit the number of objective function evaluations
                  by introducing a projected quasi Newton
                  method. Examples showing acceleration on seismic
                  data collection, data processing as well as
                  inversion are included.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/miao2012SINBADasp/miao2012SINBADasp_pres.pdf},
  author = {Lina Miao}
}


@PRESENTATION{ning2012SINBADfim,
  title = {Fast imaging with multiples},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {If correctly used, multiple energy can be mapped to
                  the correct subsurface locations. However, simply
                  applying the cross-correlation imaging condition
                  will introduce non-causal artifacts into the final
                  image. Here we propose an inversion approach to
                  image primaries and multiples simultaneously that
                  yields an artifact-free image. To address the high
                  computational cost associated with inversion, we
                  propose to: i) have the wave-equation solver carry
                  out the multi-dimensional convolutions implicitly,
                  and ii) reduce the number of PDE solves by
                  randomized subsampling. We then propose to improve
                  the overall performance of this algorithm by a
                  process called rerandomization, which helps to
                  cancel the correlation built between model iterate
                  and the subsampling operator. We show the merits of
                  our approach on a number of examples.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/ning2012SINBADfim/ning2012SINBADfim_pres.pdf},
  author = {Ning Tu}
}


@PRESENTATION{oghenekohwo2012SINBADcs,
  title = {Compressed sensing: a tool for eliminating repeatability in acquisition of {4D} (time-lapse) seismic data},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {In 4D (time-lapse) seismic data acquisition, a very
                  significant step is the repeatability of the
                  acquisition process. In other words, the geophones
                  must be placed at the exact location as they were,
                  during baseline survey and acquisition. This
                  condition is required to be able to produce an image
                  of the same location over time and this enhances a
                  proper reservoir characterization. The cost of
                  repeating the seismic acquisition is very expensive,
                  as geophones (receivers) have to be left at the same
                  location over the period for which the data will be
                  acquired. In this talk, we attempt to highlight the
                  effort of Compressed Sensing, to eliminate this
                  condition of repeatability of the acquisition. We
                  show that a random sampling of the shots or random
                  placement of the geophones is able to reproduce the
                  same image over time, hence eliminating any
                  acquisition imprints on the final seismic image.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/oghenekohwo2012SINBADcs/oghenekohwo2012SINBADcs_pres.pdf},
  author = {Felix Oghenekohwo}
}


@PRESENTATION{peters2012SINBADfde,
  title = {Frequency domain {3D} elastic wave propagation in general anisotropic media},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {Elastic wave propagation in 3 spatial dimensions is
                  modeled using a wave equation containing the full
                  stiffness tensor consisting of 21 independent
                  components. This allows modeling in general
                  anisotropic media. The wave equation is discretized
                  on several Cartesian and rotated Cartesian staggered
                  finite-difference grids (using a 2nd order
                  approximation). The grids are linearly combined and,
                  in combination with a antilumped mass strategy,
                  minimize numerical dispersion while requiring a low
                  number of grid points per wavelength. In case not
                  all 21 components need to be modeled, an
                  approximation of the stiffness tensor can be used
                  (e.g., orthorhombic anisotropy, TTI, ...). This
                  results in a linear system of equations, which is
                  solved using an iterative method. The modeling of
                  all 21 components of the stiffness tensor (or an
                  approximation) enables the development of new
                  waveform inversion functionalities.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/peters2012SINBADfde/peters2012SINBADfde_pres.pdf},
  author = {Bas Peters}
}


@PRESENTATION{petrenko2012SINBADcarp,
  title = {{CARP-CG}: a computational study},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {Forward modelling of the wave equation is a key
                  ingredient in seismic full waveform inversion
                  (FWI). Simulation in the time domain and solution of
                  the wave equation in the frequency domain are two
                  competing approaches to modelling. Frequency domain
                  approaches can further be categorized as using
                  either direct or iterative solvers. For 3D FWI,
                  iterative solvers in the frequency domain are
                  attractive, partly because they require less memory
                  than the other methods. This is due to the fact that
                  there is no need to store the wavefield at each time
                  step, or compute a factorization of the Helmholtz
                  operator that will not be as sparse as the original
                  matrix. One iterative solver that has been applied
                  to the Helmholtz equation is CARP-CG. CARP-CG uses
                  Kaczmarz row projections for each block of a domain
                  decomposition scheme to precondition the Helmholtz
                  system into being symmetric and positive
                  semidefinite. The method of conjugate gradients is
                  then used to solve the preconditioned system. We
                  present a comparison of the performance of CARP-CG
                  implemented in several languages (MATLAB, C,
                  FORTRAN, julia) and in two different hardware
                  environments: the LIMA HPC cluster hosted at UBC,
                  and the Checkers cluster which is part of the
                  Westgrid consortium. Parallelization of the
                  algorithm via domain decomposition implemented with
                  MPI (distributed memory) and OMP (shared memory) is
                  also examined.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/petrenko2012SINBADcarp/petrenko2012SINBADcarp_pres.pdf},
  author = {Art Petrenko}
}


@PRESENTATION{tamalet2012SINBADvpe,
  title = {Variance parameters estimation - application to full waveform inversion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {Many inverse problems include nuisance parameters. While
                  not of direct interest, these parameters are
                  required to recover primary parameters. In order to
                  estimate these nuisance parameters as well as the
                  primary parameters in large-scale inverse problems,
                  a method based on variable projection, which
                  consists in projecting out a subset over the
                  variables, has been developed. We present here the
                  application of this method to the problem of
                  variance parameters estimation in multiple datasets,
                  which is an important problem in many areas
                  including geophysics. More precisely, we apply the
                  method to Full Waveform Inversion and demonstrate
                  the improvement in recovery of the model parameters
                  in the case where the variance of the noise
                  increases with the frequency.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/tamalet2012SINBADvpe/tamalet2012SINBADvpe_pres.pdf},
  author = {Anais Tamalet}
}


@PRESENTATION{warner2012SINBADafw,
  title = {Anisotropic {3D} full-waveform inversion of the {Tommeliten} {Alpha} field},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {We have implemented a robust and practical scheme for
                  anisotropic 3D acoustic full-waveform inversion. We
                  demonstrate this scheme on a field data set,
                  applying it to a four-component ocean-bottom survey
                  over the Tommeliten Alpha field in the North
                  Sea. This shallow-water data set provides good
                  azimuthal coverage to offsets of 7 km, with reduced
                  coverage to a maximum offset of about 11 km. The
                  reservoir lies at the crest of a high-velocity
                  antiformal chalk section, overlain by about 3000 m
                  of clastics within which a low-velocity gas cloud
                  produces a seismic obscured area. We inverted only
                  the hydrophone data, and we retained free-surface
                  multiples and ghosts within the field data. We
                  invert in six narrow frequency bands, in the range 3
                  to 6.5 Hz. At each iteration, we selected only a
                  subset of sources, using a different subset at each
                  iteration; this strategy is more efficient than
                  inverting all the data every iteration. Our starting
                  velocity model was obtained using standard PSDM
                  model building including anisotropic reflection
                  tomography, and contained epsilon values as high as
                  20\%. We have also attempted full-elastic inversion
                  of these data to recover a shallow isotropic model
                  of both p and s-wave velocities. The final FWI
                  velocity model shows a network of shallow
                  high-velocity channels that match similar features
                  in the reflection data. Deeper in the section, the
                  FWI velocity model reveals a sharper and
                  more-intense low-velocity region associated with the
                  gas cloud in which low-velocity fingers match the
                  location of gas-filled faults visible in the
                  reflection data. The resulting velocity model
                  provides a better match to well logs, and better
                  flattens common image gathers, than does the
                  starting model. Reverse-time migration, using the
                  FWI velocity model, provides significant uplift to
                  the migrated image, simplifying the planform of the
                  reservoir section at depth. The workflows, inversion
                  strategy, and algorithms that we have used have
                  broad application to invert a wide-range of
                  analogous field data sets.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/warner2012SINBADafw/warner2012SINBADafw_pres.pdf},
  author = {Mike Warner}
}


@PRESENTATION{wason2012SINBADobs,
  title = {Ocean bottom seismic acquisition via jittered sampling},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {We present a pragmatic marine acquisition scheme
                  where a single (or multiple) vessel sails across an
                  ocean-bottom array firing airguns at ? optimally
                  jittered source locations and instances in
                  time. Following the principles of compressive
                  sensing, we can significantly impact the
                  reconstruction quality of conventional seismic data
                  (from jittered data) and demonstrate successful
                  recovery by sparsity promotion. In contrast to
                  random (under)sampling, acquisition via jittered
                  (under)sampling helps in controlling the maximum gap
                  size, which is a practical requirement of wavefield
                  reconstruction with localized sparsifying
                  transforms. Results are illustrated with simulations
                  of optimally jittered marine acquisition, and
                  periodic time-dithering marine acquisition.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/wason2012SINBADobs/wason2012SINBADobs_pres.pdf},
  author = {Haneet Wason}
}


@PRESENTATION{xiang2012SINBADfgn,
  title = {Fast {Gauss-Newton} full-waveform inversion with sparsity regularization},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {Full-waveform inversion (FWI) can be considered as a
                  controlled data fitting process, in which we
                  approximately fit observed data by iteratively
                  updating the initial velocity model, we expect the
                  final model can reveal subsurface structure till the
                  wavefield misfit can converge to designed tolerance.
                  The conventional FWI approach is expensive since it
                  requires the inversion of a linear system, which
                  involves extremely large multi-experiment data
                  volumes. To overcome this issue we percent a
                  curvetlet based sparsity-promoting Gauss-Newton
                  inversion method. In this presentation we invert for
                  the model updates by replacing the normal
                  Gauss-Newton linearized subproblem for subsampled
                  FWI with a sparsity promoting FWI formulation. We
                  speed up the algorithm and avoid over fitting the
                  data by solving the problem approximately. Aside
                  from this, we control wavefield dispersion by
                  gradually increasing grid size as we move to higher
                  frequencies. Our approach is successful because it
                  reduces the size of seismic data volumes without
                  loss of information. With this reduction, we can
                  compute a Newton-like update with the reduced data
                  volume at the cost of roughly one gradient update
                  for the fully sampled wavefield.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/xiang2012SINBADfgn/xiang2012SINBADfgn_pres.pdf},
  author = {Xiang Li}
}


@PRESENTATION{xiang2012SINBADweb,
  title = {Wave-equation based inversion with joint sparsity promotion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/xiang2012SINBADweb/xiang2012SINBADweb_pres.pdf},
  author = {Xiang Li}
}


@PRESENTATION{yilmaz2012SINBADwms,
  title = {Weighted methods in sparse recovery},
  booktitle = {SINBAD Fall consortium talks},
  year = {2012},
  organization = {SINBAD},
  abstract = {In the recent years we have successfully employed
                  "weighted" algorithms to recover sparse signals from
                  few linear, non-adaptive measurements. The general
                  principle here is to use prior knowledge about the
                  signal to be recovered, e.g., approximate locations
                  of large-in-magnitude transform coefficients, if
                  such information is available. An example for this
                  is the use of weighted 1-norm minimization to
                  improve wavefield reconstruction from randomized
                  (sub)sampling. We will review these results and
                  outline some new directions we have explored during
                  the last year, such as weighted non-convex sparse
                  recovery (see Ghadermarzy's talk), weighted
                  analysis-based recovery (see Hargreaves's talk), and
                  a weighted randomized Kaczmarz algorithm for solving
                  large overdetermined systems of equations that are
                  known to admit a (nearly) sparse solution. Various
                  examples in seismic will be shown.},
  keywords = {presentation, SINBAD, SINBADFALL2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/yilmaz2012SINBADwms/yilmaz2012SINBADwms_pres.pdf},
  author = {Ozgur Yilmaz}
}


%----- 2012 (SPRING) -----%

@PRESENTATION{aravkin2012SINBADipu,
  title = {Inverse problems using {Student's} t},
  booktitle = {SINBAD Spring consortium talks},
  year = {2012},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADSPRING2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/aravkin2012SINBADipu/aravkin2012SINBADipu_pres.pdf},
  author = {Aleksandr Y. Aravkin}
}


@PRESENTATION{dasilva2012SINBADrdp,
  title = {Recent developments in preconditioning the wave-equation {Hessian}},
  booktitle = {SINBAD Spring consortium talks},
  year = {2012},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADSPRING2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/dasilva2012SINBADrdp/dasilva2012SINBADrdp_pres.pdf},
  author = {Curt Da Silva}
}


@PRESENTATION{herrmann2012SINBADlds,
  title = {Latest developments in seismic-data recovery},
  booktitle = {SINBAD Spring consortium talks},
  year = {2012},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADSPRING2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/herrmann2012SINBADlds/herrmann2012SINBADlds_pres.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{herrmann2012SINBADsls,
  title = {Supercool(ed) least-squares imaging: latest insights in sparsity-promoting migration},
  booktitle = {SINBAD Spring consortium talks},
  year = {2012},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADSPRING2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/herrmann2012SINBADsls/herrmann2012SINBADsls_pres.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{ning2012SINBADSPRINGfim,
  title = {Fast imaging with multiples by sparse inversion},
  booktitle = {SINBAD Spring consortium talks},
  year = {2012},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADSPRING2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/ning2012SINBADSPRINGfim/ning2012SINBADSPRINGfim_pres.pdf},
  author = {Ning Tu}
}


@PRESENTATION{vanleeuwen2012SINBADoof,
  title = {An object-oriented framework for frequency-domain {FWI}},
  booktitle = {SINBAD Spring consortium talks},
  year = {2012},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADSPRING2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/vanleeuwen2012SINBADoof/vanleeuwen2012SINBADoof_pres.pdf},
  author = {Tristan van Leeuwen}
}


@PRESENTATION{wason2012SINBADrma,
  title = {Randomized marine acquisition for ocean-bottom surveys},
  booktitle = {SINBAD Spring consortium talks},
  year = {2012},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADSPRING2012, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/wason2012SINBADrma/wason2012SINBADrma_pres.pdf},
  author = {Haneet Wason}
}


%----- 2011 (FALL) -----%

@PRESENTATION{aravkin2011SINBADesp,
  title = {Extensions to sparsity promotion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/aravkin2011SINBADesp/aravkin2011SINBADesp_pres.pdf},
  author = {Aleksandr Y. Aravkin}
}


@PRESENTATION{aravkin2011SINBADrfwi,
  title = {Robust {FWI} using {Student's} t & robust source estimation},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/aravkin2011SINBADrfwi/aravkin2011SINBADrfwi_pres.pdf},
  author = {Aleksandr Y. Aravkin}
}


@PRESENTATION{aravkin2011SINBADrsp,
  title = {A randomized, sparsity promoting, Gauss-Newton algorithm for seismic waveform inversion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/aravkin2011SINBADrsp/aravkin2011SINBADrsp_pres.pdf},
  url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/aravkin2011SINBADrsp/li2011SINBADrsp_pres.pdf},
  author = {Aleksandr Y. Aravkin and Xiang Li}
}


@PRESENTATION{dasilva2011SINBADrdp,
  title = {Recent developments in preconditioning the {FWI} {Hessian}},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/dasilva2011SINBADrdp/dasilva2011SINBADrdp_pres.pdf},
  author = {Curt Da Silva}
}


@PRESENTATION{friedlander2011SINBADrir,
  title = {Robust inversion, data-fitting, and inexact gradient methods},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/friedlander2011SINBADrir/friedlander2011SINBADrir_pres.pdf},
  author = {Michael P. Friedlander}
}


@PRESENTATION{herrmann2011SINBADcoc,
  title = {Challenges and opportunities for compressive sensing in seismic acquisition},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/herrmann2011SINBADcoc/herrmann2011SINBADcoc_pres.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{herrmann2011SINBADcos,
  title = {Challenges and opportunities in sparse wavefield inversion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/herrmann2011SINBADcos/herrmann2011SINBADcos_pres.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{herrmann2011SINBADoverview,
  title = {Welcome and overview of {SINBAD} & {DNOISE}},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/herrmann2011SINBADoverview/herrmann2011SINBADoverview_pres.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{herrmann2011SINBADrnr,
  title = {To redraw or not to redraw: recent insights in randomized dimensionality reduction for inversion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/herrmann2011SINBADrnr/herrmann2011SINBADrnr_pres.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{jumah2011SINBADdrEPSI,
  title = {Dimensionality-reduced estimation of primaries by sparse inversion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/jumah2011SINBADdrEPSI/jumah2011SINBADdrEPSI_pres.pdf},
  author = {Bander Jumah}
}

@PRESENTATION{li2011SINBADels,
  title = {Efficient least-squares imaging with sparsity promotion and compressive sensing},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/li2011SINBADels/li2011SINBADels_pres.pdf},
  author = {Xiang Li}
}

@PRESENTATION{lin2011SINBADirEPSI,
  title = {Inside the robust {EPSI} formulation},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/lin2011SINBADirEPSI/lin2011SINBADirEPSI_pres.pdf},
  author = {Tim T.Y. Lin}
}


@PRESENTATION{lin2011SINBADslim,
  title = {{SLIM's} software design principles},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/lin2011SINBADslim/lin2011SINBADslim_pres.pdf},
  author = {Tim T.Y. Lin}
}


@PRESENTATION{mansour2011SINBADwcw,
  title = {Why do curvelets work?},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/mansour2011SINBADwcw/mansour2011SINBADwcw_pres.pdf},
  author = {Hassan Mansour}
}


@PRESENTATION{min2011SINBADpss,
  title = {Parameter-selection strategy for density in frequency-domain elastic waveform inversion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/min2011SINBADpss/min2011SINBADpss_pres.pdf},
  author = {Dong-Joo Min}
}


@PRESENTATION{ning2011SINBADmsr,
  title = {Migration from surface-related multiples},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/ning2011SINBADmsr/ning2011SINBADmsr_pres.pdf},
  author = {Ning Tu}
}


@PRESENTATION{vanderneut2011SINBADirs,
  title = {Interferometric redatuming by sparse inversion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/vanderneut2011SINBADirs/vanderneut2011SINBADirs_pres.pdf},
  author = {Joost van der Neut}
}


@PRESENTATION{vanleeuwen2011SINBADfwi,
  title = {Fast waveform inversion without source encoding},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/vanleeuwen2011SINBADfwi/vanleeuwen2011SINBADfwi_pres.pdf},
  author = {Tristan van Leeuwen}
}


@PRESENTATION{vanleeuwen2011SINBADoofwi,
  title = {Wavefield modelling and inversion in {Matlab}},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/vanleeuwen2011SINBADoofwi/vanleeuwen2011SINBADoofwi_pres.pdf},
  author = {Tristan van Leeuwen}
}


@PRESENTATION{vanleeuwen2011SINBADtem,
  title = {Towards extended modelling for velocity inversion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/vanleeuwen2011SINBADtem/vanleeuwen2011SINBADtem_pres.pdf},
  author = {Tristan van Leeuwen}
}


@PRESENTATION{wason2011SINBADode,
  title = {Only dither: efficient marine acquisition ``without" simultaneous sourcing},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/wason2011SINBADode/wason2011SINBADode_pres.pdf},
  author = {Haneet Wason}
}


@PRESENTATION{yilmaz2011SINBADcsp,
  title = {Compressed sensing with prior information},
  booktitle = {SINBAD Fall consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/yilmaz2011SINBADcsp/yilmaz2011SINBADcsp_pres.pdf},
  author = {Ozgur Yilmaz}
}


%----- 2011 (SPRING) -----%

@PRESENTATION{aravkin2011SINBADSPRINGrfwi,
  title = {Robust {FWI} using {Student's} t-distribution},
  booktitle = {SINBAD Spring consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADSPRING2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/aravkin2011SINBADSPRINGrfwi/aravkin2011SINBADSPRINGrfwi_pres.pdf},
  author = {Aleksandr Y. Aravkin}
}


@PRESENTATION{herrmann2011SINBADdre,
  title = {Dimensionality-reduced estimation of primaries by sparse inversion},
  booktitle = {SINBAD Spring consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBASPRING2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/herrmann2011SINBADdre/herrmann2011SINBADdre_pres.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{lin2011SINBADrep,
  title = {Robust {EPSI} in a curvelet-like representation domain},
  booktitle = {SINBAD Spring consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADSPRING2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/lin2011SINBADrep/lin2011SINBADrep_pres.pdf},
  author = {Tim T.Y. Lin}
}


@PRESENTATION{vanleeuwen2011SINBADfwiso,
  title = {A hybrid stochastic-deterministic method for waveform inversion},
  booktitle = {SINBAD Spring consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADSPRING2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/vanleeuwen2011SINBADfwiso/vanleeuwen2011SINBADfwiso_pres.pdf},
  author = {Tristan van Leeuwen}
}


@PRESENTATION{vanleeuwen2011SINBADpei,
  title = {Probing the extended image volume},
  booktitle = {SINBAD Spring consortium talks},
  year = {2011},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADSPRING2011, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/vanleeuwen2011SINBADpei/vanleeuwen2011SINBADpei_pres.pdf},
  author = {Tristan van Leeuwen}
}


%----- 2010 (FALL) -----%

@PRESENTATION{almatar2010SINBADesf,
  title = {Estimation of surface-free data by curvelet-domain matched filtering and sparse inversion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/almatar2010SINBADesf/almatar2010SINBADesf_pres.pdf},
  author = {Mufeed H. AlMatar}
}


@PRESENTATION{aravkin2010SINBADesf,
  title = {Exploiting sparsity in full-waveform inversion: nonlinear basis pursuit denoise algorithm},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/aravkin2010SINBADesf/aravkin2010SINBADesf_pres.pdf},
  author = {Aleksandr Y. Aravkin}
}


@PRESENTATION{aravkin2010SINBADicc,
  title = {Introduction to convex composite optimization},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/aravkin2010SINBADicc/aravkin2010SINBADicc_pres.pdf},
  author = {Aleksandr Y. Aravkin}
}


@PRESENTATION{friedlander2010SINBADaso,
  title = {Algorithms for sparse optimization},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/friedlander2010SINBADaso/friedlander2010SINBADaso_pres.pdf},
  author = {Michael P. Friedlander}
}


@PRESENTATION{friedlander2010SINBADisl,
  title = {Introduction to {Spot}: a linear-operator toolbox},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/friedlander2010SINBADisl/friedlander2010SINBADisl_pres.pdf},
  author = {Michael P. Friedlander}
}


@PRESENTATION{herrmann2010SINBADcss,
  title = {Compressive sensing and sparse recovery in exploration seismology},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/herrmann2010SINBADcss/herrmann2010SINBADcss_pres.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{herrmann2010SINBADdrf,
  title = {Dimensionality reduction for full-waveform inversion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/herrmann2010SINBADdrf/herrmann2010SINBADdrf_pres.pdf},
  author = {Felix J. Herrmann}
}


@PRESENTATION{kumar2010SINBADpoe,
  title = {Parallelizing operations with ease using Parallel {SPOT}},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/kumar2010SINBADpoe/kumar2010SINBADpoe_pres.pdf},
  author = {Nameet Kumar}
}


@PRESENTATION{li2010SINBADci,
  title = {Compressive imaging},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/li2010SINBADci/li2010SINBADci_pres.pdf},
  author = {Xiang Li}
}


@PRESENTATION{li2010SINBADfwi,
  title = {Full-waveform inversion with randomized {L1} recovery for the model updates},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/li2010SINBADfwi/li2010SINBADfwi_pres.pdf},
  author = {Xiang Li}
}


@PRESENTATION{lin2010SINBADlib,
  title = {Leveraging informed blind deconvolution techniques for the estimation of primaries by sparse inversion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/lin2010SINBADlib/lin2010SINBADlib_pres.pdf},
  author = {Tim T.Y. Lin}
}


@PRESENTATION{lin2010SINBADsol,
  title = {Sparse optimization and the {L1} norm},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/lin2010SINBADsol/lin2010SINBADsol_pres.pdf},
  author = {Tim T.Y. Lin}
}


@PRESENTATION{lin2010SINBADsre,
  title = {Software release: estimation of primaries by {L1} inversion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  author = {Tim T.Y. Lin}
}


@PRESENTATION{mansour2010SINBADrcs,
  title = {Recovering compressively sampled signals using partial support information},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/mansour2010SINBADrcs/mansour2010SINBADrcs_pres.pdf},
  author = {Hassan Mansour}
}


@PRESENTATION{modzelewski2010SINBADsra,
  title = {Software releases and architecture},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/modzelewski2010SINBADsra/modzelewski2010SINBADsra_pres.pdf},
  author = {Henryk Modzelewski}
}


@PRESENTATION{moghaddam2010SINBADrfwi,
  title = {Randomized full-waveform inversion},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/moghaddam2010SINBADrfwi/moghaddam2010SINBADrfwi_pres.pdf},
  author = {Peyman P. Moghaddam}
}


@PRESENTATION{pacteau2010SINBADkpo,
  title = {Kronecker product optimization in {SPOT}},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/pacteau2010SINBADkpo/pacteau2010SINBADkpo_pres.pdf},
  author = {Sebastien Pacteau}
}


@PRESENTATION{saab2010SINBADcsk,
  title = {Compressed sensing using {Kronecker} products},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/saab2010SINBADcsk/saab2010SINBADcsk_pres.pdf},
  author = {Rayan Saab}
}


@PRESENTATION{schmidt2010SINBADhsd,
  title = {Hybrid stochastic-deterministic methods},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/schmidt2010SINBADhsd/schmidt2010SINBADhsd_pres.pdf},
  author = {Mark Schmidt}
}


@PRESENTATION{tu2010SINBADspm,
  title = {Sparsity promoting migration with surface-related multiples},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/tu2010SINBADspm/tu2010SINBADspm_pres.pdf},
  author = {Ning Tu}
}


@PRESENTATION{vanleeuwen2010SINBADwis,
  title = {Waveform inversion by stochastic optimization},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/vanleeuwen2010SINBADwis/vanleeuwen2010SINBADwis_pres.pdf},
  author = {Tristan van Leeuwen}
}


@PRESENTATION{wason2010SINBADssd,
  title = {Sequential source data recovery from simultaneous acquisition through transform-domain sparsity promotion - curvelet versus shearlet transform},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/wason2010SINBADssd/wason2010SINBADssd_pres.pdf},
  author = {Haneet Wason}
}


@PRESENTATION{yilmaz2010SINBADsac,
  title = {Sparse approximations and compressive sensing: an overview},
  booktitle = {SINBAD Fall consortium talks},
  year = {2010},
  organization = {SINBAD},
  keywords = {presentation, SINBAD, SINBADFALL2010, SLIM},
  url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/yilmaz2010SINBADsac/yilmaz2010SINBADsac_pres.pdf},
  author = {Ozgur Yilmaz}
}


%----- 2010 (SPRING) -----%