avo_explorer_v2_mono.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AVO Explorer v2 (mono)\n",
    "\n",
    "Jupyter notebook to explore AVO classes.\n",
    "\n",
    "Monolytic version, made to be run without any Python installed on local pc using [colab.research.google.com](https://colab.research.google.com): no dependency from external libraries, no interactive widgets (ipywidgets are not yet supported by colab).\n",
    "\n",
    "Bookmark the following link to reuse this notebook: <https://colab.research.google.com/github/aadm/geophysical_notes/blob/master/avo_explorer_v2_mono.ipynb>\n",
    "\n",
    "2016-2018, Alessandro Amato del Monte (alessandro.adm@gmail.com)\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below defines the elastic properties for the various AVO classes, taken from Castagna & Swan, \"Principles of AVO crossplotting\" (1997) and Hilterman, \"Seismic Amplitude Interpretation\" (2001)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "shale = np.array([[3094,1515,2.40], [2643,1167,2.29], [2192,818,2.16], [3240,1620,2.34]])\n",
    "ssgas = np.array([[4050,2526,2.21,.2], [2781,1665,2.08,.25], [1542,901,1.88,.33], [1650,1090,2.07,.18]])\n",
    "ssbri = np.array([[4115,2453,2.32,.2], [3048,1595,2.23,.25], [2134,860,2.11,.33], [2590,1060,2.21,.18]])\n",
    "avocl=['class 1','class 2','class 3','class 4']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell define some support functions: `ricker` (to create wavelets), `gassmann` (to perform fluid substitution), `shuey` and `ruger` (reflectivity equations for the isotropic and anisotropic case)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ricker(f, length, dt):\n",
    "    '''\n",
    "    (taken from https://github.com/seg/tutorials-2014/blob/master/1406_Make_a_synthetic/how_to_make_synthetic.ipynb)\n",
    "    (author: Evan Bianco)\n",
    "    '''\n",
    "    t = np.linspace(-length/2, (length-dt)/2, int(length/dt))\n",
    "    y = (1. - 2.*(np.pi**2)*(f**2)*(t**2))*np.exp(-(np.pi**2)*(f**2)*(t**2))\n",
    "    return t, y\n",
    "\n",
    "def gassmann(vp1, vs1, rho1, rho_fl1, k_fl1, rho_fl2, k_fl2, k0, phi):\n",
    "    '''\n",
    "    written by Alessandro Amato del Monte, 2016\n",
    "    '''\n",
    "    vp1 = vp1/1000\n",
    "    vs1 = vs1/1000\n",
    "    rho2 = rho1-phi*rho_fl1+phi*rho_fl2\n",
    "    mu1 = rho1*vs1**2.\n",
    "    k1 = rho1*vp1**2-(4./3.)*mu1\n",
    "    kdry= (k1 * ((phi*k0)/k_fl1+1-phi)-k0) / ((phi*k0)/k_fl1+(k1/k0)-1-phi)\n",
    "    k2 = kdry + (1- (kdry/k0))**2 / ( (phi/k_fl2) + ((1-phi)/k0) - (kdry/k0**2) )\n",
    "    mu2 = mu1\n",
    "    vp2 = np.sqrt(((k2+(4./3)*mu2))/rho2)\n",
    "    vs2 = np.sqrt((mu2/rho2))\n",
    "    return [vp2*1000, vs2*1000, rho2, k2, kdry]\n",
    "\n",
    "def ruger(vp1,vs1,rho1,d1,e1,vp2,vs2,rho2,d2,e2,theta):\n",
    "    '''\n",
    "    written by Alessandro Amato del Monte, 2016\n",
    "    '''\n",
    "    a = np.radians(theta)\n",
    "    vp  = np.mean([vp1,vp2])\n",
    "    vs  = np.mean([vs1,vs2])\n",
    "    rho = np.mean([rho1,rho2])\n",
    "    z   = np.mean([vp1*rho1,vp2*rho2])\n",
    "    g   = np.mean([rho1*vs1**2,rho2*vs2**2])\n",
    "    dvp = vp2-vp1\n",
    "    dvs = vs2-vs1\n",
    "    drho= rho2-rho1\n",
    "    z2, z1 = vp2*rho2, vp1*rho1\n",
    "    dz  = z2-z1\n",
    "    dg  = rho2*vs2**2 - rho1*vs1**2\n",
    "    dd = d2-d1\n",
    "    de = e2-e1\n",
    "    A = 0.5*(dz/z)\n",
    "    B = 0.5*(dvp/vp - (2*vs/vp)**2 * (dg/g) + dd) * np.sin(a)**2\n",
    "    C = 0.5*(dvp/vp + de) * np.sin(a)**2 * np.tan(a)**2\n",
    "    R = A+B+C\n",
    "    return R\n",
    "\n",
    "def shuey(vp1, vs1, rho1, vp2, vs2, rho2, theta, approx=True, terms=False):\n",
    "    '''\n",
    "    shuey (C) aadm 2016\n",
    "    Calculates P-wave reflectivity with Shuey's equation\n",
    "\n",
    "    reference:\n",
    "    Avseth et al. (2005), Quantitative Seismic Interpretation, Cambridge University Press (p.182)\n",
    "\n",
    "    INPUT\n",
    "    vp1, vs1, rho1: P-, S-wave velocity (m/s) and density (g/cm3) of upper medium\n",
    "    vp2, vs2, rho2: P-, S-wave velocity (m/s) and density (g/cm3) of lower medium\n",
    "    theta: angle of incidence (degree)\n",
    "    approx: returns approximate (2-term) form (default: True)\n",
    "    terms: returns reflectivity, intercept and gradient (default: False)\n",
    "\n",
    "    OUTPUT\n",
    "    reflectivity (and optionally intercept, gradient; see terms option) at angle theta\n",
    "    '''\n",
    "    a = np.radians(theta)\n",
    "    dvp = vp2-vp1\n",
    "    dvs = vs2-vs1\n",
    "    drho = rho2-rho1\n",
    "    vp  = np.mean([vp1,vp2])\n",
    "    vs  = np.mean([vs1,vs2])\n",
    "    rho = np.mean([rho1,rho2])\n",
    "    R0 = 0.5*(dvp/vp + drho/rho)\n",
    "    G  = 0.5*(dvp/vp) - 2*(vs**2/vp**2)*(drho/rho+2*(dvs/vs))\n",
    "    F =  0.5*(dvp/vp)\n",
    "    if approx:\n",
    "        R = R0 + G*np.sin(a)**2\n",
    "    else:\n",
    "        R = R0 + G*np.sin(a)**2 + F*(np.tan(a)**2-np.sin(a)**2)\n",
    "    if terms:\n",
    "        return R,R0,G\n",
    "    else:\n",
    "        return R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The four main functions are `make_avoclasses` (plots a reference chart with the AVO classes), `avomod1` (creation of AVO curves, synthetic gather and Intercept-Gradient crossplot for a half-space model), `avomod2` (like the previous but allows for two different configurations for the lower medium, usually different fluids), `make_avo_explorer` (uses `avomod2` to give ... a simple selection to plot the reference AVO classes given by Hilterman (2001) for shale/brine sand scenarios and performs fluid replacement to make gas and oil sands)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_avoclasses(brine=False):\n",
    "    '''\n",
    "    written by Alessandro Amato del Monte, 2016\n",
    "    '''\n",
    "    ang = np.arange(0,50,1)\n",
    "    cc = ['m','c','r','g']\n",
    "    f,ax=plt.subplots(1,2, figsize=(10, 5))\n",
    "    if brine:\n",
    "        print('In the Intercept-Gradient crossplot, circles are gas sands, squares are brine sands.')\n",
    "    for i, val in enumerate(avocl):\n",
    "        amp0 = shuey(shale[i,0],shale[i,1],shale[i,2],ssbri[i,0],ssbri[i,1],ssbri[i,2],ang)\n",
    "        amp1 = shuey(shale[i,0],shale[i,1],shale[i,2],ssgas[i,0],ssgas[i,1],ssgas[i,2],ang)\n",
    "        tmp0 = shuey(shale[i,0],shale[i,1],shale[i,2],ssbri[i,0],ssbri[i,1],ssbri[i,2],30,terms=True)\n",
    "        tmp1 = shuey(shale[i,0],shale[i,1],shale[i,2],ssgas[i,0],ssgas[i,1],ssgas[i,2],30,terms=True)\n",
    "        Ib, Gb = tmp0[1],tmp0[2]\n",
    "        Ig, Gg = tmp1[1],tmp1[2]\n",
    "        ax[0].plot(ang, amp1, color=cc[i], lw=2, ls='-', label=val+' (gas)')\n",
    "        ax[0].axhline(0, color='k')\n",
    "        ax[0].set_xlabel('angle of incidence'), ax[0].set_ylabel('amplitude')\n",
    "        ax[0].set_xlim(0, 40)\n",
    "        ax[0].text(2,amp1[0]-.02,avocl[i], style='italic', fontsize=10, ha='left', va='top')\n",
    "        ax[1].plot(Ig, Gg, color=cc[i], marker='o', ms=10, label=val+' (gas)')\n",
    "        ax[1].axhline(0, color='k'), ax[1].axvline(0, color='k')\n",
    "        ax[1].set_xlabel('intercept'), ax[1].set_ylabel('gradient')\n",
    "        ax[1].set_xlim(-.5, .5)\n",
    "        if brine:\n",
    "            ax[0].plot(ang, amp0, color=cc[i], lw=2, ls='--', label=val+' (brine)')\n",
    "            ax[1].plot(Ib, Gb, color=cc[i], marker='s', ms=10, label=val+' (brine)')\n",
    "        # draw avo classes areas\n",
    "        cl1_area = patches.Rectangle((0.02,-1),.98,1,edgecolor='None',facecolor='m',alpha=0.2)\n",
    "        cl2_area = patches.Rectangle((-0.02,-1),.04,2,edgecolor='None',facecolor='c',alpha=0.2)\n",
    "        cl3_area = patches.Rectangle((-1,-1),.98,1,edgecolor='None',facecolor='r',alpha=0.2)\n",
    "        cl4_area = patches.Rectangle((-1,0),.98,1,edgecolor='None',facecolor='g',alpha=0.2)\n",
    "        for aa in ax:\n",
    "            aa.grid()\n",
    "            aa.set_ylim(-.5, .5)\n",
    "    background = patches.Polygon([[-1, 1], [1, -1], [1, 1]],facecolor='w')\n",
    "    ax[1].add_patch(cl1_area)\n",
    "    ax[1].add_patch(cl2_area)\n",
    "    ax[1].add_patch(cl3_area)\n",
    "    ax[1].add_patch(cl4_area)\n",
    "    ax[1].add_patch(background)\n",
    "    ax[1].text(.15,-.3,'Class 1',ha='center',va='center',color='m',style='italic')\n",
    "    ax[1].text(0,-.25,'Class 2/2p',ha='center',va='center', color='c',style='italic')\n",
    "    ax[1].text(-.35,-.3,'Class 3',ha='center',va='center', color='r',style='italic')\n",
    "    ax[1].text(-.35,.15,'Class 4',ha='center',va='center', color='g',style='italic')\n",
    "\n",
    "def avomod1(vp1,vs1,rho1,vp2,vs2,rho2,angmin=0,angmax=30,polarity='normal',black=False,mx=0.5):\n",
    "    '''\n",
    "    written by Alessandro Amato del Monte, 2016\n",
    "    '''\n",
    "    n_samples = 500\n",
    "    gain=10\n",
    "    interface=int(n_samples/2)\n",
    "    ang = np.arange(angmin,angmax+1,1)\n",
    "    z = np.arange(n_samples)\n",
    "\n",
    "    # build Ip and Vp/Vs logs\n",
    "    ip, vpvs = (np.zeros(n_samples) for _ in range(2))\n",
    "    ip[:interface] = vp1*rho1\n",
    "    ip[interface:] = vp2*rho2\n",
    "    vpvs[:interface] = vp1/vs1\n",
    "    vpvs[interface:] = vp2/vs2\n",
    "\n",
    "    # calculate avo curve, intercept and gradient\n",
    "    avo = shuey(vp1,vs1,rho1,vp2,vs2,rho2,ang)\n",
    "    _,I,G = shuey(vp1,vs1,rho1,vp2,vs2,rho2,30,terms=True)\n",
    "\n",
    "    # create synthetic gather\n",
    "    _,wavelet = ricker(f=10, length=.250, dt=0.001)\n",
    "    if polarity is not 'normal':\n",
    "        avo *= -1\n",
    "\n",
    "    # builds prestack gather model\n",
    "    rc, syn = (np.zeros((n_samples,ang.size)) for _ in range(2))\n",
    "    rc[interface,:]=  avo\n",
    "    for i in range(ang.size):\n",
    "        syn[:,i] = np.convolve(rc[:,i],wavelet,mode='same')\n",
    "\n",
    "    # do the plot\n",
    "    f=plt.subplots(figsize=(10, 5))\n",
    "    ax0 = plt.subplot2grid((1,7), (0,0), colspan=1)\n",
    "    ax1 = plt.subplot2grid((1,7), (0,1), colspan=1)\n",
    "    ax2 = plt.subplot2grid((1,7), (0,2), colspan=1)\n",
    "    ax3 = plt.subplot2grid((1,7), (0,3), colspan=2)\n",
    "    ax4 = plt.subplot2grid((1,7), (0,5), colspan=2)\n",
    "    ax0.plot(ip, z, '-k', lw=4)\n",
    "    ax0.set_xlabel('AI [m/s*g/cc]')\n",
    "    ax0.margins(x=0.5)\n",
    "    ax1.plot(vpvs, z, '-k', lw=4)\n",
    "    ax1.set_xlabel('Vp/Vs')\n",
    "    ax1.margins(x=0.5)\n",
    "    opz1={'color':'k','linewidth':2}\n",
    "    opz2={'linewidth':0, 'alpha':0.6}\n",
    "    for i in range(0,int(ang.size),10):\n",
    "        trace=gain*syn[:,i] / np.max(np.abs(syn))\n",
    "        ax2.plot(i+trace,z,**opz1)\n",
    "        if black==False:\n",
    "            ax2.fill_betweenx(z,trace+i,i,where=trace+i>i,facecolor=[0.6,0.6,1.0],**opz2)\n",
    "            ax2.fill_betweenx(z,trace+i,i,where=trace+i<i,facecolor=[1.0,0.7,0.7],**opz2)\n",
    "        else:\n",
    "            ax2.fill_betweenx(z,trace+i,i,where=trace+i>i,facecolor='black',**opz2)\n",
    "        ax2.set_xticklabels([])\n",
    "    ax2.margins(x=0.05)\n",
    "    ax3.plot(ang, avo,'-k', lw=4)\n",
    "    ax3.axhline(0, color='k', lw=1)\n",
    "    ax3.set_xlabel('angle of incidence')\n",
    "    ax3.set_ylim(-mx,mx)\n",
    "    ax4.plot(I,G,'ko',ms=10,mfc='none',mew=2)\n",
    "    ax4.axhline(0, color='k', lw=1), ax4.axvline(0, color='k', lw=1)\n",
    "    ax4.set_xlabel('intercept'), ax4.set_ylabel('gradient')\n",
    "    ax4.set_xlim(-mx,mx), ax4.set_ylim(-mx,mx)\n",
    "    ax4.xaxis.set_label_position('top'), ax4.xaxis.tick_top()\n",
    "    ax4.yaxis.set_label_position('right'), ax4.yaxis.tick_right()\n",
    "    for aa in [ax0, ax1, ax2]:\n",
    "        aa.invert_yaxis()\n",
    "        aa.xaxis.tick_top()\n",
    "        plt.setp(aa.xaxis.get_majorticklabels(), rotation=90, fontsize=8)\n",
    "        aa.set_yticklabels([])\n",
    "    plt.tight_layout()\n",
    "\n",
    "def avomod2(vp1,vs1,rho1,vp2A,vs2A,rho2A,vp2B,vs2B,rho2B,angmin=0,angmax=30):\n",
    "    '''\n",
    "    written by Alessandro Amato del Monte, 2016\n",
    "    '''\n",
    "    n_samples = 500\n",
    "    interface = int(n_samples/2)\n",
    "    ang = np.arange(angmin,angmax+1,1)\n",
    "    z = np.arange(n_samples)\n",
    "\n",
    "    # builds Ip and Vp/Vs logs\n",
    "    ipA,ipB,vpvsA,vpvsB = (np.zeros(n_samples) for _ in range(4))\n",
    "    ipA[:interface] = vp1*rho1\n",
    "    ipA[interface:] = vp2A*rho2A\n",
    "    ipB[:interface] = vp1*rho1\n",
    "    ipB[interface:] = vp2B*rho2B\n",
    "    vpvsA[:interface] = vp1/vs1\n",
    "    vpvsA[interface:] = vp2A/vs2A\n",
    "    vpvsB[:interface] = vp1/vs1\n",
    "    vpvsB[interface:] = vp2B/vs2B\n",
    "\n",
    "    # calculates avo curve, intercept and gradient\n",
    "    avoA = shuey(vp1,vs1,rho1,vp2A,vs2A,rho2A,ang)\n",
    "    avoB = shuey(vp1,vs1,rho1,vp2B,vs2B,rho2B,ang)\n",
    "    tmp0= shuey(vp1,vs1,rho1,vp2A,vs2A,rho2A,30,terms=True)\n",
    "    IA, GA = tmp0[1],tmp0[2]\n",
    "    tmp0= shuey(vp1,vs1,rho1,vp2B,vs2B,rho2B,30,terms=True)\n",
    "    IB, GB = tmp0[1],tmp0[2]\n",
    "\n",
    "    # do the plot\n",
    "    f=plt.subplots(figsize=(10, 5))\n",
    "    ax0 = plt.subplot2grid((1,6), (0,0), colspan=1)\n",
    "    ax1 = plt.subplot2grid((1,6), (0,1), colspan=1)\n",
    "    ax2 = plt.subplot2grid((1,6), (0,2), colspan=2)\n",
    "    ax3 = plt.subplot2grid((1,6), (0,4), colspan=2)\n",
    "    ax0.plot(ipB, z, '-r', lw=4)\n",
    "    ax0.plot(ipA, z, '-k', lw=4)\n",
    "    ax0.set_xlabel('AI [m/s*g/cc]')\n",
    "    ax0.margins(x=0.5)\n",
    "    ax1.plot(vpvsB, z, '-r', lw=4)\n",
    "    ax1.plot(vpvsA, z, '-k', lw=4)\n",
    "    ax1.set_xlabel('Vp/Vs')\n",
    "    ax1.margins(x=0.5)\n",
    "    ax2.plot(ang, avoB,'-r', lw=4)\n",
    "    ax2.plot(ang, avoA,'-k', lw=4)\n",
    "    ax2.axhline(0, color='k', lw=1)\n",
    "    ax2.set_xlabel('angle of incidence')\n",
    "    ax2.margins(y=0.5)\n",
    "    ax3.plot(IB,GB,'ro',ms=15,mfc='r',mew=1)\n",
    "    ax3.plot(IA,GA,'ko',ms=15,mfc='k',mew=1)\n",
    "    ax3.axhline(0, color='k', lw=1), ax3.axvline(0, color='k', lw=1)\n",
    "    ax3.set_xlabel('intercept'), ax3.set_ylabel('gradient')\n",
    "    ax3.margins(0.5)\n",
    "    ax3.xaxis.set_label_position('top'), ax3.xaxis.tick_top()\n",
    "    ax3.yaxis.set_label_position('right'), ax3.yaxis.tick_right()\n",
    "    for aa in [ax0, ax1]:\n",
    "        aa.invert_yaxis()\n",
    "        aa.xaxis.tick_top()\n",
    "        plt.setp(aa.xaxis.get_majorticklabels(), rotation=90, fontsize=8)\n",
    "        aa.set_yticklabels([])\n",
    "    plt.tight_layout()\n",
    "\n",
    "def make_avo_explorer(avoclass=3, fluid='gas', phimod=0.0):\n",
    "    '''\n",
    "    written by Alessandro Amato del Monte, 2016\n",
    "    '''\n",
    "    shale = np.array([[3094,1515,2.40], [2643,1167,2.29], [2192,818,2.16], [3240,1620,2.34]])\n",
    "    ssbri = np.array([[4115,2453,2.32,.2], [3048,1595,2.23,.25], [2134,860,2.11,.33], [2590,1060,2.21,.18]])\n",
    "    vp1,vs1,rho1 = shale[avoclass-1,0],shale[avoclass-1,1],shale[avoclass-1,2]\n",
    "    vp2,vs2,rho2 = ssbri[avoclass-1,0],ssbri[avoclass-1,1],ssbri[avoclass-1,2]\n",
    "    phi2 = ssbri[avoclass-1,3]+phimod\n",
    "\n",
    "    # elastic parameters for toy-fluid replacement\n",
    "    k0 = 37.00\n",
    "    rhob, kb = 1.09,  2.20\n",
    "    if fluid is 'gas':\n",
    "        rhof_new, kf_new = 0.40,  0.02 # gas density & bulk modulus\n",
    "    else:\n",
    "        rhof_new, kf_new = 0.80,  1.02 # oil density & bulk modulus\n",
    "\n",
    "    vp2B,vs2B,rho2B,_,_ = gassmann(vp2, vs2, rho2, rhob, kb, rhof_new, kf_new, k0, phi2)\n",
    "\n",
    "    print('Shale:  Vp={:.0f}, Vs={:.0f}, rho={:.2f}'.format(vp1,vs1,rho1))\n",
    "    print('Sand (brine): Vp={:.0f}, Vs={:.0f}, rho={:.2f}, porosity={:.2f}'.format(vp2,vs2,rho2,phi2))\n",
    "    print('Sand ({:s}): Vp={:.0f}, Vs={:.0f}, rho={:.2f}'.format(fluid,vp2B,vs2B,rho2B))\n",
    "\n",
    "    avomod2(vp1,vs1,rho1,vp2,vs2,rho2,vp2B,vs2B,rho2B,angmin=0,angmax=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AVO classes\n",
    "\n",
    "The function `make_avoclasses` simply plots all the AVO classes in the amplitude vs angle domain and intercept vs gradient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 316,
       "width": 612
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_avoclasses()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AVO explorer\n",
    "\n",
    "`avomod1` is the main function that allows the user to explore AVO classes by studying the reflectivity (amplitude) variations with angle of incidence at an interface between two half-spaces.\n",
    "\n",
    "The syntax is:\n",
    "\n",
    "```\n",
    "avomod1(vp1, vs1, rho1, vp2, vs2, rho2, angmin=0, angmax=30, polarity='normal', black=False, mx=0.5)\n",
    "```\n",
    "\n",
    "The upper medium is characterized by the elastic properties `vp1`, `vs1`, `rho1` (P and S-wave velocity in m/s, density in g/cc) and the lower medium by `vp2`, `vs2`, `rho2`.\n",
    "\n",
    "The maximum angle of incidence in degrees is `angmax`.\n",
    "\n",
    "Optional parameters are `polarity` that when set to `normal` means SEG-normal, i.e. increase in acoustic impedance is a peak or positive number; `black` is a boolean flag to draw the synthetic seismogram in black and white when set to `True`, otherwise peaks are blue and troughs are red. Finally, `mx` controls the scale of the intercept and gradient crossplot as well as the vertical scale (i.e., reflection magnitude) of the AVO plot.\n",
    "\n",
    "We can use the elastic properties from the above array defining the various classes or input our own values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 354,
       "width": 713
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avomod1(shale[2,0],shale[2,1],shale[2,2],ssgas[2,0],ssgas[2,1],ssgas[2,2],angmin=0,angmax=30,polarity='normal',black=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make it easier to investigate the predefined AVO classes and how they change according to fluid content and porosity variations, use `make_avo_explorer`, that also allows the user to change the saturating fluid in the sand (lower half-space) and its porosity.\n",
    "\n",
    "In the plot below, *black* is always brine, *red* is either gas or oil according to the selection in the input parameters.\n",
    "\n",
    "Porosity variations should stay within a range of 10% positive or negative variations, i.e. from -0.1 to +0.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shale:  Vp=2192, Vs=818, rho=2.16\n",
      "Sand (brine): Vp=2134, Vs=860, rho=2.11, porosity=0.33\n",
      "Sand (gas): Vp=1521, Vs=911, rho=1.88\n"
     ]
    },
    {
     "data": {
      "image/png": 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44AHOP/30MmuLi4u54IILmDZtGvvn5PD2qFEcctBBlX9TIiIiySvdzFoAbmaN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\ngP79+5OZmVlh/cyZr7J9+xa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' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        this.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.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) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.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\nmpl.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 */\nfunction 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\nmpl.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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.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\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.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\nmpl.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\nmpl.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; 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xCuhmZh8ABlxuZjcDX7j76zHWuQd41sx6EDmL0D+UTkVEJNmq1DFAAVkkg1WnIRbuvgu4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\nmzYJ1x555JFxaxcvjlzXuHX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          "\u001b[1;32m~\\GoogleDrive\\PYTHON\\geophysical_notes\\avo_explorer_library.py\u001b[0m in \u001b[0;36mmake_avo_explorer\u001b[1;34m(avoclass, fluid, phimod)\u001b[0m\n\u001b[0;32m    344\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    345\u001b[0m                 \u001b[0mrhof_new\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkf_new\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.80\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;36m1.02\u001b[0m \u001b[1;31m# oil density & bulk modulus\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 346\u001b[1;33m         \u001b[0mvp2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvs2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrho2B\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrockphysics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mavseth_fluidsub\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvp2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvs2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrho2\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1e3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mphi2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrhob\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1e3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrhof_new\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1e3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk0\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1e9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkb\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1e9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkf_new\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1e9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    347\u001b[0m         \u001b[0mrho2B\u001b[0m \u001b[1;33m/=\u001b[0m \u001b[1;36m1e3\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    348\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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xM0Zedx0jfvpTMho23GX9li1buO6663jiiScAuObss/n7rbdSr5Ir5m3bto1b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\n9SCLSGjcfQtwzi5ev2UXr12ejDaJiEh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        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Shale:  Vp=2192, Vs=818, rho=2.16\nSand (brine): Vp=2134, Vs=860, rho=2.11, porosity=0.33\nSand (gas): Vp=1521, Vs=911, rho=1.88\n"
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j7C0tz1dFRJxHsQ3kFuCMzPxq68PrOW2br0GTmdsi4i4azw1l+YOZ+esm55vObUQcRLF9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Sh/keoH2ccSxJyp16KadQL+Osgv8oLC9MhSe5sPwGkIBPteUkhT80fw4sAJ4t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\nDAAIGP369VOTJk0cY2+NGaPXR41yLdMYo08++US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582vWrFlq2rRptjMkacaM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PAAAg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\n7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      "state": {
       "layout": "IPY_MODEL_02479e00172749b9a3dfb015f4d23832",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Shale:  Vp=2192, Vs=818, rho=2.16\nSand (brine): Vp=2134, Vs=860, rho=2.11, porosity=0.33\nSand (gas): Vp=1521, Vs=911, rho=1.88\n"
        },
        {
         "ename": "TypeError",
         "evalue": "avomod2() got an unexpected keyword argument 'method'",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
          "\u001b[1;32m~\\AppData\\Local\\Continuum\\Miniconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    248\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 250\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    251\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m~\\GoogleDrive\\PYTHON\\geophysical_notes\\avo_explorer_library.py\u001b[0m in \u001b[0;36mmake_avo_explorer\u001b[1;34m(avoclass, fluid, phimod)\u001b[0m\n\u001b[0;32m    356\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Sand ({:s}): Vp={:.0f}, Vs={:.0f}, rho={:.2f}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfluid\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvp2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvs2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrho2B\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    357\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 358\u001b[1;33m         \u001b[0mavomod2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvp1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvs1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrho1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvp2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvs2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrho2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvp2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvs2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrho2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mangmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mangmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'shuey'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
          "\u001b[1;31mTypeError\u001b[0m: avomod2() got an unexpected keyword argument 'method'"
         ]
        }
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        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Shale:  Vp=3094, Vs=1515, rho=2.40\nSand (brine): Vp=4115, Vs=2453, rho=2.32, porosity=0.20\nSand (gas): Vp=4099, Vs=2529, rho=2.18\n"
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giZlxxRVXMGvWLA444ICEvo0bN3L66adz0003sXXr1ogSiojULSqQRaTmSkoK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\n+AGtrYVJ5iZZK8mTSaYn2abFcDtwYq/NQ1v53Uk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          "text/plain": "<matplotlib.figure.Figure at 0xa1f7e10>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "e55e846fbf0241428ebd78f2bd06fcc1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "rho1",
       "layout": "IPY_MODEL_94c8b767f2fd44d8ab5f236daf648101",
       "max": 3,
       "min": 1.5,
       "step": 0.1,
       "style": "IPY_MODEL_c843f9aac8dd41478b79ce5e64f2d9f9",
       "value": 2.16
      }
     },
     "e5761ed97ed04db19a0ab9db8d85c201": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_27b832e8781c45b7b24535443d54ed0a",
       "outputs": [
        {
         "data": {
          "image/png": 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OcAzAQOqXAHfYVv72y/M+Az6Y5N1JVid5b5KfJ/mLJJ/ZhWO9PslpM1caAADQ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\n+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       "style": "IPY_MODEL_f7cb3a118ca44926b68bbdd1c486468b"
      }
     },
     "f0c295ebabe540838051be3b5898cd96": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "vs1",
       "layout": "IPY_MODEL_6038535946014ed7a9ab98b06a6a8ca5",
       "max": 3000,
       "min": 1000,
       "step": 100,
       "style": "IPY_MODEL_d111ab33f4894f45bff1e586ecabcb94",
       "value": 1000
      }
     },
     "f0d49c87eb134479af1b263b2a7b3b38": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "CheckboxModel",
      "state": {
       "description": "black",
       "disabled": false,
       "layout": "IPY_MODEL_cf1e7df697224301bf1961640efb9528",
       "style": "IPY_MODEL_bddf589f92714f88bb2b008fc57e3591",
       "value": false
      }
     },
     "f0ecf998d69a42cfb54356502389debe": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "CheckboxModel",
      "state": {
       "description": "black",
       "disabled": false,
       "layout": "IPY_MODEL_05f5a57e9a274efe9b7b83015579139a",
       "style": "IPY_MODEL_4d06fcae9cb54077abdf3f965d373347",
       "value": false
      }
     },
     "f14cccc5011148cb859c714279e2658f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_82aedb43e02d42879570c17cfbf55c5b",
       "outputs": [
        {
         "data": {
          "image/png": 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44AHOP/30MmuLi4u54IILmDZtGvvn5PD2qFEcctBBlX9TIiIiySvdzFoAbmaN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\n",
          "text/plain": "<matplotlib.figure.Figure at 0xa253898>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "text/plain": "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]),\n array([-0.24338299, -0.24341179, -0.24349814, -0.24364195, -0.24384303,\n        -0.24410115, -0.24441598, -0.24478715, -0.24521421, -0.24569662,\n        -0.24623381, -0.24682511, -0.24746982, -0.24816714, -0.24891623,\n        -0.24971616, -0.25056598, -0.25146463, -0.25241104, -0.25340403,\n        -0.25444241, -0.25552491, -0.25665021, -0.25781694, -0.25902367,\n        -0.26026894, -0.26155124, -0.26286899, -0.26422059, -0.2656044 ,\n        -0.26701872]),\n -0.24338298853988244,\n -0.094542937487458734)"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "f153db1168ab407797290934e3f740ea": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f2b61954ba1d4f8094ba4cf2a2b8b2a2": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_73941b473daa4ada9578893a20b30c71",
       "outputs": [
        {
         "ename": "ValueError",
         "evalue": "too many values to unpack (expected 3)",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
          "\u001b[1;32m~\\AppData\\Local\\Continuum\\Miniconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    248\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 250\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    251\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m~\\GoogleDrive\\PYTHON\\geophysical_notes\\avo_explorer_library.py\u001b[0m in \u001b[0;36mmake_avo_explorer\u001b[1;34m(avoclass, fluid, phimod)\u001b[0m\n\u001b[0;32m    347\u001b[0m                 \u001b[0mrhof_new\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkf_new\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.80\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;36m1.02\u001b[0m \u001b[1;31m# oil density & bulk modulus\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    348\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 349\u001b[1;33m         \u001b[0mvp2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvs2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrho2B\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgassmann\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvp2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvs2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrho2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrhob\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrhof_new\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkf_new\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mphi2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    350\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    351\u001b[0m         \u001b[1;31m# vp2B,vs2B,rho2B=b.rockphysics.avseth_fluidsub(vp2,vs2,rho2*1e3,phi2,rhob*1e3,rhof_new*1e3,k0*1e9,kb*1e9,kf_new*1e9)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;31mValueError\u001b[0m: too many values to unpack (expected 3)"
         ]
        }
       ]
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