02PS_filters_land_synthetic.ipynb

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    "## Spatial wavefield gradient-based seismic wavefield separation\n",
    "\n",
    "### 02 P/S separation of synthetic land data"
   ]
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   "source": [
    "Robertsson & Curtis (2002) derived expressions to estimate the upgoing P- and S-wave potentials using spatially compact receiver groups. We work with particle velocities instead of wavefield potentials to obtain the horizontal component of the upgoing S-wave constituents and the vertical component of the upgoing P-wave constituents (see Van Renterghem et al. (2018) for details):\n",
    "\n",
    "$$\n",
    "v_x^{S^U} \\approx \\frac{\\beta}{i\\omega}(\\nabla \\times v^U)_y \\approx \\frac{1}{2}(v_x - \\frac{1}{i\\omega} 2\\beta \\frac{\\partial v_z}{\\partial x})\n",
    "$$\n",
    "\n",
    "$$\n",
    "v_y^{S^U} \\approx \\frac{\\beta}{i\\omega}(\\nabla \\times v^U)_x \\approx \\frac{1}{2}(v_y - \\frac{1}{i\\omega} 2\\beta \\frac{\\partial v_z}{\\partial y})\n",
    "$$\n",
    "\n",
    "$$\n",
    "v_z^{P^U} \\approx \\frac{\\alpha}{i\\omega}(\\nabla \\cdot v^U) \\approx \\frac{1}{2}(v_z + \\frac{1}{i\\omega} \\frac{2\\beta^2}{\\alpha} (\\frac{\\partial v_x}{\\partial x}+\\frac{\\partial v_y}{\\partial y}))\n",
    "$$\n",
    "\n",
    "The above equations separate the recorded particle velocity components into their P- and S-wave constituents and additionally remove the free-surface effect.\n",
    "\n",
    "We test our P/S separation filters on synthetic data generated on a 2D model. A force source emitting a 50 Hz Ricker wavelet was placed at 100 m depth in a homogeneous medium with P- and S-wave velocities of 1800 and 900 m/s, respectively. The incident P- and S-wave arrivals were recorded at the free-surface."
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    "# 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",
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    {
     "data": {
      "text/plain": [
       "[('vx', (3001, 500), 'double'), ('vz', (3001, 500), 'double')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check .mat file \n",
    "# ---------------\n",
    "from scipy.io import whosmat\n",
    "whosmat('PS_filters_land_synthetic_resample.mat') # to list variables inside mat file"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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   "source": [
    "# load .mat file \n",
    "# --------------\n",
    "from scipy.io import loadmat\n",
    "mat = loadmat('PS_filters_land_synthetic_resample.mat')  # to load mat file\n",
    "# convert them to numpy array\n",
    "vx = mat['vx']\n",
    "vz = mat['vz']\n",
    "mat = 0"
   ]
  },
  {
   "cell_type": "code",
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    "# initialize parameter \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 = 900       # S-wave velocity\n",
    "rho = 1600       # density"
   ]
  },
  {
   "cell_type": "code",
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    "# 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((vx.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-2]) + (c[1]*vx[:,n-1]) + (c[2]*vx[:,n])   + (c[3]*vx[:,n+1])) / dx\n",
    "    # horizontal gradients of vz\n",
    "    gradx_vz[:,n] = ((c[0]*vz[:,n-1]) + (c[1]*vz[:,n])   + (c[2]*vz[:,n+1]) + (c[3]*vz[:,n+2])) / 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))) \n",
    "# upgoing S-wave on vx\n",
    "vx_S_up = (1/2.) *(vx - ((2*beta) * (gradx_vz.cumsum(axis=0)*dt))) "
   ]
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       "\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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\" 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(1000/4,1476/4)\n",
    "plt.ylim(60000/20,0)\n",
    "plt.title('Recorded $v_z$',fontweight=\"bold\")\n",
    "plt.xlabel('Incidence angle ($^{\\circ}$)')\n",
    "plt.ylabel('Time (s)')\n",
    "plt.xticks([1000/4,1036/4,1072/4,1108/4,1144/4,1188/4,1232/4,1280/4,1336/4,1400/4,1476/4],['0','','','','','25','','','','','50'])\n",
    "plt.yticks([0,10000/20,20000/20,30000/20,40000/20,50000/20,60000/20],['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(1000/4,1476/4)\n",
    "plt.ylim(60000/20,0)\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([1000/4,1036/4,1072/4,1108/4,1144/4,1188/4,1232/4,1280/4,1336/4,1400/4,1476/4],['0','','','','','25','','','','','50'])\n",
    "plt.yticks([0,10000/20,20000/20,30000/20,40000/20,50000/20,60000/20],['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(1000/4,1476/4)\n",
    "plt.ylim(60000/20,0)\n",
    "plt.title('Recorded $v_x$',fontweight=\"bold\")\n",
    "plt.xlabel('Incidence angle ($^{\\circ}$)')\n",
    "plt.ylabel('Time (s)')\n",
    "plt.xticks([1000/4,1036/4,1072/4,1108/4,1144/4,1188/4,1232/4,1280/4,1336/4,1400/4,1476/4],['0','','','','','25','','','','','50'])\n",
    "plt.yticks([0,10000/20,20000/20,30000/20,40000/20,50000/20,60000/20],['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(1000/4,1476/4)\n",
    "plt.ylim(60000/20,0)\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([1000/4,1036/4,1072/4,1108/4,1144/4,1188/4,1232/4,1280/4,1336/4,1400/4,1476/4],['0','','','','','25','','','','','50'])\n",
    "plt.yticks([0,10000/20,20000/20,30000/20,40000/20,50000/20,60000/20],['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 shows the total horizontal and vertical particle velocity recordings and the P/S separated results using our equations above. The horizontal component P-wave is effectively suppressed, whereas the vertical component S-wave is only suppressed up to the critical angle."
   ]
  },
  {
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}