03PS_filters_OBC_synthetic.ipynb

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    "## Spatial wavefield gradient-based seismic wavefield separation\n",
    "\n",
    "### 03 P/S separation of synthetic OBC data"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P/S separation based on the elastodynamic representation theorem using spatial wavefield gradients can be applied to OBC data. Similar to the land seismic case, we interpret the horizontal particle velocity component of the upgoing S-wave constituents and the vertical particle velocity component of the upgoing P-wave constituents (see Van Renterghem et al. (2018) for details):\n",
    "\n",
    "$$\n",
    "v_x^{S^U} \\approx \\frac{1}{\\rho \\beta}(\\nabla \\times v^U)_y \\approx \\frac{1}{2} (v_x - \\frac{1}{i\\omega} 2\\beta \\frac{\\partial v_z}{\\partial x} + \\frac{1}{i\\omega} \\frac{1}{\\rho} \\frac{\\partial p}{\\partial x})\n",
    "$$\n",
    "\n",
    "$$\n",
    "v_y^{S^U} \\approx \\frac{1}{\\rho \\beta}(\\nabla \\times v^U)_x \\approx \\frac{1}{2} (v_y - \\frac{1}{i\\omega} 2\\beta \\frac{\\partial v_z}{\\partial y} + \\frac{1}{i\\omega} \\frac{1}{\\rho} \\frac{\\partial p}{\\partial y})\n",
    "$$\n",
    "\n",
    "$$\n",
    "v_z^{P^U} \\approx \\frac{1}{\\rho \\alpha}(\\nabla \\cdot v) \\approx \\frac{1}{2} (v_z + \\frac{1}{i\\omega} \\frac{2\\beta^2}{\\alpha} (\\frac{\\partial v_x}{\\partial x} + \\frac{\\partial v_y}{\\partial y}) - \\frac{1}{\\alpha \\rho}p)\n",
    "$$\n",
    "\n",
    "This notebook illustrates the P/S separation method on a synthetic OBC data example. The seismograms (.mat file) were generated from a 2D model consisting of a 100 m thick water layer bounded above by a free-surface. Below the water layer is a homogeneous medium with a P-wave velocity of 1800 m/s, an S-wave velocity of 700 m/s and a density of 1600 kg/m$^{3}$. A point source emitting a 50 Hz Ricker wavelet was placed at 100 m below the seafloor to generate both P- and S-waves."
   ]
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   "source": [
    "# import libraries \n",
    "# ----------------\n",
    "%matplotlib notebook\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('vx', (3000, 600), 'double'),\n",
       " ('vz', (3000, 600), 'double'),\n",
       " ('pressure', (3000, 600), 'double')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check .mat file \n",
    "# ---------------\n",
    "from scipy.io import whosmat\n",
    "whosmat('OBC_synthetic_data_PS_resample.mat') # to list variables inside mat file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load .mat file \n",
    "# --------------\n",
    "from scipy.io import loadmat\n",
    "mat = loadmat('OBC_synthetic_data_PS_resample.mat')  # to load mat file\n",
    "# convert them to numpy array\n",
    "vx = mat['vx']\n",
    "vz = mat['vz']\n",
    "pressure = mat['pressure']\n",
    "mat = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize parameters \n",
    "# ---------------------\n",
    "nx = vx.shape[1] # number of cells in x direction\n",
    "dx = 1           # cell size\n",
    "dt = 1e-4        # time step\n",
    "alpha = 1800     # P-wave velocity\n",
    "beta = 700       # S-wave velocity\n",
    "rho = 1600       # density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate wavefield gradients \n",
    "# -----------------------------\n",
    "from fd_weights import *\n",
    "c = fd_weights(0, [-3/2, -1/2, 1/2, 3/2], 1) # FD coefficients according to Fornberg (1988)\n",
    "\n",
    "# allocate zero gradients\n",
    "gradx_vx       = np.zeros((      vx.shape[0],nx))\n",
    "gradx_vz       = np.zeros((      vz.shape[0],nx))\n",
    "gradx_pressure = np.zeros((pressure.shape[0],nx))\n",
    "\n",
    "for n in range(2,(vx.shape[1]-2)):\n",
    "    # horizontal gradients of vx\n",
    "    gradx_vx[:,n] = ((c[0]*vx[:,n-1]) + (c[1]*vx[:,n])   + (c[2]*vx[:,n+1]) + (c[3]*vx[:,n+2])) / dx\n",
    "    # horizontal gradients of vz\n",
    "    gradx_vz[:,n] = ((c[0]*vz[:,n-2]) + (c[1]*vz[:,n-1]) + (c[2]*vz[:,n])   + (c[3]*vz[:,n+1])) / dx\n",
    "    # horizontal gradient of pressure\n",
    "    gradx_pressure[:,n] = ((c[0]*pressure[:,n-2]) + (c[1]*pressure[:,n-1]) \\\n",
    "                         + (c[2]*pressure[:,n])   + (c[3]*pressure[:,n+1])) / dx\n",
    "    \n",
    "# P/S separation \n",
    "# --------------\n",
    "# upgoing P-wave on vz\n",
    "vz_P_up = (1/2.) * (vz + ((beta*((2*beta)/alpha)) * (gradx_vx.cumsum(axis=0)*dt)) - ((1/(alpha * rho)) * pressure))\n",
    "# upgoing S-wave on vx\n",
    "vx_S_up = (1/2.) * (vx - ((2*beta) * (gradx_vz.cumsum(axis=0)*dt)) + ((1/rho) * (gradx_pressure.cumsum(axis=0)*dt)))"
   ]
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   "execution_count": 6,
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       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<img 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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting configuration \n",
    "# ----------------------\n",
    "plt.figure(figsize=(9, 6))\n",
    "\n",
    "# recorded vz\n",
    "plt.subplot(2,2,1)\n",
    "vm1 = np.percentile(vz, 99)\n",
    "plt.imshow(-vz, cmap=\"bwr\", vmin=-vm1, vmax=vm1, aspect='auto')\n",
    "plt.xlim(1200/4,1676/4)\n",
    "plt.ylim(59000/20,0)\n",
    "plt.title('Recorded $v_z$',fontweight=\"bold\")\n",
    "plt.xlabel('Incidence angle ($^{\\circ}$)')\n",
    "plt.ylabel('Time (s)')\n",
    "plt.xticks([1200/4,1236/4,1272/4,1308/4,1348/4,1388/4,1432/4,1480/4,1536/4,1600/4,1676/4],['0','','','','','25','','','','','50'])\n",
    "plt.yticks([0,10000/20,20000/20,30000/20,40000/20,50000/20,59000/20],['0.00','','0.10','','0.20','','0.30'])\n",
    "\n",
    "# P/S separated upgoing vz\n",
    "plt.subplot(2,2,3)\n",
    "vm2 = np.percentile(vz_P_up, 99)\n",
    "plt.imshow(-vz_P_up, cmap=\"bwr\", vmin=-vm2, vmax=vm2, aspect='auto')\n",
    "plt.xlim(1200/4,1676/4)\n",
    "plt.ylim(59000/20,0)\n",
    "plt.title('Upgoing P-wave on $v_z$ - filters',fontweight=\"bold\")\n",
    "plt.xlabel('Incidence angle ($^{\\circ}$)')\n",
    "plt.ylabel('Time (s)')\n",
    "plt.xticks([1200/4,1236/4,1272/4,1308/4,1348/4,1388/4,1432/4,1480/4,1536/4,1600/4,1676/4],['0','','','','','25','','','','','50'])\n",
    "plt.yticks([0,10000/20,20000/20,30000/20,40000/20,50000/20,59000/20],['0.00','','0.10','','0.20','','0.30'])\n",
    "\n",
    "# recorded vx\n",
    "plt.subplot(2,2,2)\n",
    "vm3 = np.percentile(vx, 99)\n",
    "plt.imshow(-vx, cmap=\"bwr\", vmin=-vm3, vmax=vm3, aspect='auto')\n",
    "plt.xlim(1200/4,1676/4)\n",
    "plt.ylim(59000/20,0)\n",
    "plt.title('Recorded $v_x$',fontweight=\"bold\")\n",
    "plt.xlabel('Incidence angle ($^{\\circ}$)')\n",
    "plt.ylabel('Time (s)')\n",
    "plt.xticks([1200/4,1236/4,1272/4,1308/4,1348/4,1388/4,1432/4,1480/4,1536/4,1600/4,1676/4],['0','','','','','25','','','','','50'])\n",
    "plt.yticks([0,10000/20,20000/20,30000/20,40000/20,50000/20,59000/20],['0.00','','0.10','','0.20','','0.30'])\n",
    "\n",
    "# P/S separated upgoing vx\n",
    "plt.subplot(2,2,4)\n",
    "vm4 = np.percentile(vx_S_up, 99)\n",
    "plt.imshow(-vx_S_up, cmap=\"bwr\", vmin=-vm4, vmax=vm4, aspect='auto')\n",
    "plt.xlim(1200/4,1676/4)\n",
    "plt.ylim(59000/20,0)\n",
    "plt.title('Upgoing S-wave on $v_x$ - filters',fontweight=\"bold\")\n",
    "plt.xlabel('Incidence angle ($^{\\circ}$)')\n",
    "plt.ylabel('Time (s)')\n",
    "plt.xticks([1200/4,1236/4,1272/4,1308/4,1348/4,1388/4,1432/4,1480/4,1536/4,1600/4,1676/4],['0','','','','','25','','','','','50'])\n",
    "plt.yticks([0,10000/20,20000/20,30000/20,40000/20,50000/20,59000/20],['0.00','','0.10','','0.20','','0.30'])\n",
    "\n",
    "# show result on screen\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figures above show the total horizontal and vertical particle velocity recordings at the seafloor and the P/S separated results. Our technique effectively suppresses the horizontal component of the P-wave and the vertical component of the S-wave up to moderate incidence angles. The sea surface multiple (receiver ghost), which is observed on the vertical component at about 0.21 s, has also been removed by the combined up/down and P/S separation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (Spyder)",
   "language": "python3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
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 },
 "nbformat": 4,
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