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velocity_models.py
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#!/usr/bin/env python3
import numpy as np
import os
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from pdb import set_trace as bp
import sys
from scipy import interpolate
from pyrocko import moment_tensor as mtm
## Local modules
import utils
try:
sys.path.append('/staff/quentin/Documents/Projects/Ridgecrest/')
from extract_velocity_ucvm import get_velocity_ucvm
UCVM_available = True
except:
UCVM_available = False
## display parameters
font = {'size': 14}
matplotlib.rc('font', **font)
## To make sure that there is no bug when saving and closing the figures
## https://stackoverflow.com/questions/27147300/matplotlib-tcl-asyncdelete-async-handler-deleted-by-the-wrong-thread
matplotlib.use('Agg')
def create_velocity_model_(mt, options, add_Yang_layer, tomo_model='cvmsi'):
## Exit message if no UCVM library found
if not UCVM_available:
sys.exit('No UCVM library found. Can not build velocity model')
## Options
offset = 0.1
options_in = {}
options_in['nb_lats'] = 3
options_in['nb_depths'] = 50000
options_in['lats'] = [mt['LAT'], mt['LAT']+offset] #Y
options_in['lons'] = [mt['LON'], mt['LON']+offset] #X
#options_in['lats'] = [mt['balloons']['Tortoise']['balloon']['Lat'], mt['balloons']['Tortoise']['balloon']['Lat']+offset] #Y
#options_in['lons'] = [mt['balloons']['Tortoise']['balloon']['Lon'], mt['balloons']['Tortoise']['balloon']['Lon']+offset] #X
#options_in['depths'] = [0., 50000.] #Z
options_in['depths'] = [0., -1*options['zmax']] #Z
options_in['cvm_model'] = 'cvms5'
options_in['cvm_model'] = 'cvmsi'
options_in['cvm_model'] = 'cvmh'
options_in['cvm_model'] = tomo_model
#options_in['gtl'] = ',elygtl:ely -z 0,350'
options_in['gtl'] = ''
options_in['profiles'] = True
## Auxiliaries
data_vs = get_velocity_ucvm(options_in)
data_vs = data_vs.loc[ data_vs['cr_vs'] > 1 ].iloc[::options_in['nb_lats']]
if add_Yang_layer:
file_yang = '/staff/quentin/Documents/Projects/Ridgecrest/seismic_models/Yang_Vs3km.txt'
vs_yang = pd.read_csv(file_yang, header=None, delim_whitespace=True)
vs_yang = vs_yang.values
zmax = float(vs_yang.shape[0])
z_ = np.linspace(0., zmax, vs_yang.shape[0])
vs_ = np.median(vs_yang, axis=1)
f = interpolate.interp1d(z_, vs_*1000., kind='linear')
idx_z = (data_vs['Z']<=zmax).nonzero()[0]
z_interp = data_vs['Z'].values[idx_z]
vs_interp = f(z_interp)
data_vs.loc[data_vs['Z']<=zmax, 'cr_vs'] = vs_interp
## Empirical relations (Brocher, 2005a)
## eq. (6)
vp_ = 0.9409 + 2.0947 * vs_ \
- 0.8206 * vs_**2 \
+ 0.2683 * vs_**3 \
- 0.0251 * vs_**4
f = interpolate.interp1d(z_, vp_*1000., kind='linear')
vp_interp = f(z_interp)
data_vs.loc[data_vs['Z']<=zmax, 'cr_vp'] = vp_interp
## eq. (1)
rho_ = 1.6612 * vp_ \
- 0.4721 * vp_**2 \
+ 0.0671 * vp_**3 \
- 0.0043 * vp_**4 \
+ 0.000106 * vp_**5
f = interpolate.interp1d(z_, rho_*1000., kind='linear')
rho_interp = f(z_interp)
data_vs.loc[data_vs['Z']<=zmax, 'cr_rho'] = rho_interp
## Save data
mt['vs'] = data_vs['cr_vs'].values
mt['vp'] = data_vs['cr_vp'].values
mt['rho'] = data_vs['cr_rho'].values
mt['z'] = data_vs['Z'].values
return mt
def construct_local_seismic_model(mechanism_data, options):
name_file = '/staff/quentin/Documents/Projects/Ridgecrest/seismic_models/model_temp.txt'
data = np.c_[abs(mechanism_data['z']), mechanism_data['rho'], mechanism_data['vp'], mechanism_data['vs'], 0.05*mechanism_data['vs'], 0.1*mechanism_data['vs']]
np.savetxt(name_file, data)
return (name_file, 'specfem')
###############################################################
## Create adapted velocity model on both sides of the interface
def create_velocity_model(options):
## Definition
side = {}
unknown_tab = ['rho', 'vs', 'vp', 'Qp', 'Qs']
## Jack's files
options['z'] = np.arange(0.,100000,2800)
if(options['type_model'] == 'specfem'):
zover0, data = utils.read_specfem_files(options)
elif(options['type_model'] == 'specfem2d'):
zover0, data = utils.read_specfem2d_files(options)
else:
sys.exit('Trying to load an external velocity model that is not supported: ' + options['type_model'])
options['z'] = zover0.copy()
z_interp, data_interp = discretize_model_heterogeneous(data, options)
## Back to the right format for earthsr and SWRT
options['z'] = z_interp.copy()/1000.
options['dz'] = np.diff(options['z'])[0]
options['h'] = np.diff(options['z'])
options['nb_layers'] = len(options['z'])
chosen_model = options['chosen_model']
for iunknown in unknown_tab:
side[iunknown] = data_interp[chosen_model][iunknown].copy()
side['z'] = options['z'].copy()
## Add attenuation if needed
side['Qa'] = side['Qp']*0. + 9999. # P-wave Q
side['Qb'] = side['Qs']*0. + 9999. # S-wave Q
if(options['ATTENUATION']):
side['Qa'] = side['Qp']*1000. # P-wave Q
side['Qb'] = side['Qs']*1000. # S-wave Q
return side
def create_velocity_figures(current_struct, options):
nbmodes = np.sum([1 for key in current_struct if key])
fig, axs = plt.subplots(nrows=nbmodes, ncols=1, sharex=True, sharey=True)
if nbmodes == 1:
selected_axs = axs
else:
selected_axs = axs[-1]
selected_axs.set_ylabel('Velocity (km/s)')
selected_axs.set_xlabel('Frequency (Hz)')
fks = current_struct[0]['fks']
selected_axs.set_xlim([fks.min(), fks.max()])
for imode in range(0, nbmodes):
if nbmodes > 1:
selected_axs = axs[imode]
selected_axs.plot(current_struct[imode]['fks'], current_struct[imode]['cphi'], label='$c_\Phi$')
selected_axs.plot(current_struct[imode]['fks'], current_struct[imode]['cg'], label='$c_g$', linestyle='--')
selected_axs.grid()
selected_axs.text(0.5, 1., 'Mode '+str(imode), horizontalalignment='center', verticalalignment='center', bbox=dict(facecolor='w', edgecolor='black', pad=4.0), transform=selected_axs.transAxes)
selected_axs.set_xscale('log')
if nbmodes > 1:
selected_axs = axs[0]
selected_axs.legend()
if(not options['GOOGLE_COLAB']):
plt.savefig(options['global_folder'] + 'cphi.png')
plt.close('all')
#######################################
## Discretize continuous model for SWRT
def discretize_model_heterogeneous(data, options):
## Build interpolated depth model
z_interp = np.linspace(options['z'][0], options['zmax'], options['nb_layers'])
z_interp_interm = np.linspace(options['z'][0], options['z'][-1], 400)
## Loop over models (CVMH/CVMS) and unknowns (rho/vs/vp)
data_interp = {}
for imodel in data:
## Build layered model
data_interp[imodel] = {}
for iunknown in data[imodel]:
data_interp[imodel][iunknown] = []
temp = data[imodel][iunknown][:]
locnan = np.isnan(temp).nonzero()[0]
## Remove nan values
if( locnan.size > 0 ):
iz = locnan[0]
temp[iz:] = temp[iz-1]
#f = interpolate.interp1d(options['z'], temp, kind='previous')
#temp_interm = f(z_interp_interm)/1000.
#f = interpolate.interp1d(z_interp_interm, temp_interm, kind='previous')
#data_interp[imodel][iunknown] = f(z_interp)
f = interpolate.interp1d(options['z'], temp, kind='next')
temp_interm = f(z_interp)/1000.
data_interp[imodel][iunknown] = temp_interm
return z_interp, data_interp
def generate_default_atmos():
param_atmos = {}
param_atmos['isothermal'] = False
param_atmos['subsampling'] = True
param_atmos['subsampling_layers'] = 120
param_atmos['file'] = './atmospheric_model.dat'
param_atmos['cpa'] = 3.2e2 # m/s
param_atmos['H'] = np.inf # m
param_atmos['Nsq'] = 1e-10
param_atmos['wind_x'] = 0.
param_atmos['wind_y'] = 0.
param_atmos['bulk'] = 1e-3
param_atmos['shear'] = 1e-3
param_atmos['kappa'] = 0.
param_atmos['gamma'] = 1.4
param_atmos['rho'] = 1.2
param_atmos['cp'] = 3000.
return param_atmos
def read_csv_seismic(model, dimension, loc_source = 50000.):
temp = pd.read_csv( model, delim_whitespace=True, header=None )
if(dimension == 1):
temp.columns = ['z', 'rho', 'vp', 'vs', 'Qs', 'Qp']
else:
temp.columns = ['x', 'z', 'rho', 'vp', 'vs', 'Qs', 'Qp']
x = temp['x'].unique()
ix = np.argmin( abs(x - loc_source) )
x_chosen = x[ix]
temp = temp.loc[ temp['x'] == x_chosen, temp.columns != 'x' ].copy()
if(temp['z'].iloc[0] > 0):
temp_add = temp.loc[ temp['z'] == temp['z'].min() ].copy()
temp_add.loc[0, 'z'] = 0.
temp = pd.concat([temp_add, temp]).reset_index()
temp_add = temp.loc[ temp['z'] == temp['z'].max() ].copy()
temp_add['z'].iloc[0] = 1.e7
temp = pd.concat([temp, temp_add]).reset_index()
return temp
def plot_atmosphere_and_seismic(global_folder, seismic, z_atmos, rho, cpa, winds, H, isothermal, dimension, google_colab):
import matplotlib.colors as mcolors
nb_cols = 3
fig, axs = plt.subplots(nrows=2, ncols=nb_cols)
colors = [icolor for icolor in mcolors.TABLEAU_COLORS]
iax = 0
iax_row = 1
z = seismic['z'].values/1000.
zi = np.linspace(z[0], z[-1], 10000)
f = interpolate.interp1d(z, seismic['rho'].values/1000., kind='previous')
unknown = f(zi)
axs[iax_row, iax].plot(unknown, zi, color=colors[iax+iax_row*nb_cols])
axs[iax_row, iax].grid()
axs[iax_row, iax].set_xlim([unknown.min(), unknown.max()])
axs[iax_row, iax].set_yscale('log')
axs[iax_row, iax].set_ylabel('Depth (km)')
axs[iax_row, iax].set_xlabel('Density (g/cm$^3$)')
axs[iax_row, iax].text(-0.8, 0.5, 'Seismic', horizontalalignment='center', verticalalignment='center', bbox=dict(facecolor='w', edgecolor='black', pad=2.0), transform=axs[iax_row, iax].transAxes, rotation=90)
axs[iax_row, iax].invert_yaxis()
iax += 1
f = interpolate.interp1d(z, seismic['vp'].values/1000., kind='previous')
unknown = f(zi)
axs[iax_row, iax].plot(unknown, zi, color=colors[iax+iax_row*nb_cols])
axs[iax_row, iax].grid()
axs[iax_row, iax].set_xlim([unknown.min(), unknown.max()])
axs[iax_row, iax].tick_params(axis='both', which='both', labelleft=False)
axs[iax_row, iax].set_yscale('log')
axs[iax_row, iax].invert_yaxis()
axs[iax_row, iax].set_xlabel('$v_p$ (km/s)')
iax += 1
f = interpolate.interp1d(z, seismic['vs'].values/1000., kind='previous')
unknown = f(zi)
axs[iax_row, iax].plot(unknown, zi, color=colors[iax+iax_row*nb_cols])
axs[iax_row, iax].grid()
axs[iax_row, iax].set_xlim([unknown.min(), unknown.max()])
axs[iax_row, iax].tick_params(axis='both', which='both', labelleft=False)
axs[iax_row, iax].set_yscale('log')
axs[iax_row, iax].invert_yaxis()
axs[iax_row, iax].set_xlabel('$v_s$ (km/s)')
axs[iax_row, iax].get_shared_y_axes().join(axs[iax_row, 0], axs[iax_row, 1], axs[iax_row, 2])
## Create a profile from a few altitude points if isothermal model
rho = rho
cpa = cpa
wx = winds[0]
wy = winds[1]
z = z_atmos
if(isothermal):
z = np.linspace(0, 50, 100)
rho = rho[0]*np.exp(-z/(H[0]/1000.))+z*0
cpa = cpa[0]+z*0
wx = winds[0][0]+z*0
wy = winds[1][0]+z*0
else:
z = z/1000.
iax = 0
iax_row = 0
unknown = rho/1000.
try:
axs[iax_row, iax].plot(unknown, z, color=colors[iax+iax_row*nb_cols])
except:
bp()
axs[iax_row, iax].grid()
if(unknown.min() < 0.5*unknown.max()):
axs[iax_row, iax].set_xlim([unknown.min(), unknown.max()])
axs[iax_row, iax].set_ylim([z.min(), z.max()])
axs[iax_row, iax].set_ylabel('Altitude (km)')
axs[iax_row, iax].text(-0.8, 0.5, 'Atmosphere', horizontalalignment='center', verticalalignment='center', bbox=dict(facecolor='w', edgecolor='black', pad=2.0), transform=axs[iax_row, iax].transAxes, rotation=90)
axs[iax_row, iax].set_title('Density (g/cm$^3$)')
axs[iax_row, iax].set_xscale('log')
iax += 1
unknown = cpa/1000.
axs[iax_row, iax].plot(unknown, z, color=colors[iax+iax_row*nb_cols])
axs[iax_row, iax].grid()
if(not isothermal):
axs[iax_row, iax].set_xlim([unknown.min(), unknown.max()])
axs[iax_row, iax].set_ylim([z.min(), z.max()])
axs[iax_row, iax].tick_params(axis='both', which='both', labelleft=False)
axs[iax_row, iax].set_title('$c_p$ (km/s)')
iax += 1
unknown = wx/1000.
axs[iax_row, iax].plot(unknown, z, color=colors[iax+iax_row*nb_cols])
if(not isothermal):
axs[iax_row, iax].set_xlim([unknown.min(), unknown.max()])
axs[iax_row, iax].set_ylim([z.min(), z.max()])
if(dimension > 2):
unknown_ = wy/1000.
axs[iax_row, iax].plot(unknown_, z)
if(not isothermal):
axs[iax_row, iax].set_xlim([min(unknown.min(), unknown_.min()), max(unknown.max(), unknown_.max())])
axs[iax_row, iax].grid()
axs[iax_row, iax].tick_params(axis='both', which='both', labelleft=False)
axs[iax_row, iax].set_title('winds (km/s)')
fig.subplots_adjust(hspace=0.3, right=0.95, left=0.2, top=0.9, bottom=0.15)
if(not google_colab):
plt.savefig(global_folder + 'seismic_and_atmos_profiles.png')
plt.close('all')