{
 "metadata": {
  "name": "",
  "signature": "sha256:299623d16f0990fe27d6076bf182db39b4034b2ca232a759f11cfc146986f80e"
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPython notebook to calculate operator for colour inversion using seismic amplitude spectrum and well AI spectrum exported from OpendTect.\n",
      "\n",
      "First sample of the exported seismic amplitude spectrum was edited to be different than zero (0.01Hz), first row of well based AI was deleted as it was 0.00000Hz"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "import numpy as np\n",
      "from scipy.stats import linregress\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "\n",
      "#Input spectrum files\n",
      "seisfile=\"C:\\\\users\\\\zahup\\desktop\\\\F2_01_seismic_amplitude_spectrum.dat\"\n",
      "wellfile=\"C:\\\\users\\\\zahup\\desktop\\\\F2_01_well_AI_amplitude_spectrum.dat\"\n",
      "\n",
      "#Shape parameter for Kaiser window\n",
      "beta=70\n",
      "\n",
      "#Normalized amplitude treshold, smaller amplitudes will be skipped during computation\n",
      "treshold=0.2\n",
      "\n",
      "#Operator phase spectrum depending on polarity of the seismic data\n",
      "#SEG normal(AI increase = through): phase=90; SEG reverse (AI increase = Peak): phase=-90\n",
      "phase=-90\n",
      "\n",
      "#Number of samples of the operator\n",
      "num=100\n",
      "\n",
      "#Ouput operator file\n",
      "operatorfile=\"C:\\\\users\\\\zahup\\desktop\\\\F2_01_colour_operator.dat\"\n",
      "\n",
      "#Load spectrums from input files, and calculate linear regression based on well impedance spectrum on log-log scale\n",
      "\n",
      "#Load exported spectrums from OpendTect\n",
      "freqseis, ampseis=np.loadtxt(seisfile, unpack=True)\n",
      "freqwell, ampwell=np.loadtxt(wellfile, unpack=True)\n",
      "\n",
      "# dB to amplitude conversion\n",
      "ampwell=np.power(10,ampwell/20)\n",
      "ampseis=np.power(10,ampseis/20)\n",
      "\n",
      "#Normalize seismic spectrum\n",
      "normseis = ampseis / np.max(ampseis)\n",
      "\n",
      "#Calculate logarithmic well spectrum\n",
      "logfreq= np.log10(freqwell)\n",
      "logamp=np.log10(ampwell)\n",
      "\n",
      "#Linear regression on logarithmic well spectrum\n",
      "slope, intercept, rvalue, pvalue, stderr = linregress(logfreq,logamp)\n",
      "print ('Regression results:')\n",
      "print (\"Intercept:\", intercept)\n",
      "print (\"Slope    :\", slope)\n",
      "print (\"R-value  :\", rvalue)\n",
      "\n",
      "#Plot well based AI spectrum with regression line\n",
      "lintrend=intercept+slope*logfreq\n",
      "plt.figure(0)\n",
      "plt.title('Well Impedance spectrum')\n",
      "plt.scatter(logfreq,logamp, label=\"AI impedance spectrum\")\n",
      "plt.xlabel(\"log10(frequency)\")\n",
      "plt.ylabel(\"log10(amplitude)\")\n",
      "plt.plot(logfreq,lintrend, label=\"Trend line\", linewidth=3, color='red')\n",
      "plt.xlim(np.min(logfreq),np.max(logfreq))\n",
      "plt.legend()\n",
      "plt.grid()\n",
      "\n",
      "#Calculate raw operator, based on the ratio of trend spectrum from wells and normalized seismic amplitude spectrum\n",
      "\n",
      "#Calculate regression based trend well spectrum\n",
      "WelltrendSpectrum = intercept*np.power(freqseis,slope)\n",
      "\n",
      "#Calculate residual spectrum\n",
      "treshold = 0.2\n",
      "ResidualSpectrum=np.zeros(len(normseis))\n",
      "for i in range(len(normseis)):\n",
      "    if normseis[i]>treshold:\n",
      "        ResidualSpectrum[i]= WelltrendSpectrum[i] / normseis[i]\n",
      "        \n",
      "#Normalize residual spectrum\n",
      "ResidualSpectrum=ResidualSpectrum / np.max(ResidualSpectrum)\n",
      "\n",
      "#Plot normalized seismic spectrum with well trend spectrum\n",
      "plt.figure(1)\n",
      "thold=np.ones(len(freqseis))\n",
      "thold=treshold*thold\n",
      "plt.title('Seismic spectrums')\n",
      "plt.plot(freqseis,normseis, label='Normalized seismic amplitude spectrum')\n",
      "plt.plot(freqseis,WelltrendSpectrum, label='Regression based AI spectrum')\n",
      "plt.plot(freqseis,ResidualSpectrum, label='Frequency domain raw operator', color='red')\n",
      "plt.fill_between(freqseis, ResidualSpectrum,0, ResidualSpectrum > 0.0, interpolate=False, hold=True, color='red', alpha = 0.5)\n",
      "plt.plot(freqseis,thold, label='Amplitude treshold', color='grey')\n",
      "plt.xlabel('Frequency [Hz]')\n",
      "plt.ylabel('Normalized Amplitude')\n",
      "plt.ylim(0,1.5)\n",
      "plt.xlim(0,100)\n",
      "plt.legend()\n",
      "plt.grid()\n",
      "\n",
      "#Precalculation for inverse dft, to transform raw operator to time domain.\n",
      "#First setup a complex amplitude spectrum with the given phase, then calculate ifft and reorder the result.\n",
      "\n",
      "#Calculate dt\n",
      "dt=1/(2*np.max(freqseis))\n",
      "df=freqseis[2]-freqseis[1]\n",
      "\n",
      "#Setup complex amplitude spectrum for ifft with phase assumption\n",
      "phase=np.radians(phase)\n",
      "cspectrum_poz=ResidualSpectrum*(np.cos(phase)+1j*np.sin(phase))\n",
      "cspectrum_neg=ResidualSpectrum*(np.cos(-1*phase)+1j*np.sin(-1*phase))\n",
      "rev_cspectrum_neg=np.fliplr([cspectrum_neg])[0]\n",
      "input_cspectrum=np.append(cspectrum_poz,rev_cspectrum_neg)\n",
      "\n",
      "#Calculate ifft and reorder arrays\n",
      "t_op=np.fft.ifft(input_cspectrum)\n",
      "start_t=(-1/2)*dt*(len(input_cspectrum))\n",
      "t_shift=np.linspace(start_t,-1*start_t,len(t_op))\n",
      "t_op_shift=np.fft.ifftshift(t_op)\n",
      "\n",
      "#Tapering of the time domain operator using a Kaiser window, and calculation of the operator triming indexes, and plot the final operator\n",
      "\n",
      "#Tapering\n",
      "window=np.kaiser(len(t_shift),beta)\n",
      "t_op_final=t_op_shift*window\n",
      "\n",
      "#Operator trimming indexes\n",
      "start_i=(int(len(t_shift)/2))-int(num/2)\n",
      "stop_i=(int(len(t_shift)/2))+int(num/2)\n",
      "\n",
      "#Plot final time domain operator\n",
      "plt.figure(2)\n",
      "plt.title('Colour inversion operator')\n",
      "plt.plot(t_shift,t_op_final, label='Time domain operator')\n",
      "#plt.fill_between(t_shift, t_op_final,0, t_op_final > 0.0, interpolate=False, hold=True, color='blue', alpha = 0.5)\n",
      "plt.xlim(t_shift[start_i],t_shift[stop_i])\n",
      "plt.ylim(-0.07,0.09)\n",
      "plt.xlabel('Time [s]')\n",
      "plt.ylabel('Amplitude')\n",
      "plt.legend()\n",
      "plt.grid()\n",
      "\n",
      "#Save final operator\n",
      "np.savetxt(operatorfile,t_op_final[start_i:stop_i].real)\n",
      "\n",
      "#QC operator\n",
      "bt_op=np.fft.fft(t_op_final)\n",
      "plt.figure(3)\n",
      "backfreq=np.fft.fftfreq(len(t_op_final),dt)\n",
      "plt.title('QC tapering')\n",
      "\n",
      "plt.plot(backfreq,abs(bt_op), label='Tapered backtransformed operator', color='red')\n",
      "plt.plot(freqseis,ResidualSpectrum, label='Frequency domain raw operator')\n",
      "plt.ylabel('Normalized amplitude')\n",
      "plt.xlabel('Frequency (Hz)')\n",
      "plt.xlim(0,100)\n",
      "plt.legend()\n",
      "plt.grid()\n",
      "plt.show()\n",
      "\n",
      "print('Operator is saved to:',operatorfile)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Regression results:\n",
        "Intercept: 8.5916801723\n",
        "Slope    : -0.77710664924\n",
        "R-value  : -0.774860056446\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EUI2h1rkCuNSU5C8opa4Frgb2A3uA0SKyyCEfiWU964wIfPaZ4Tto+nRYv945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\n+opvw1fdBf+zprpnhLmE/R6GkWkqMDdE2mnAZeb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       "text": [
        "<matplotlib.figure.Figure at 0x89ca0f0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pKSl274eTJ08SFBSEv79/udc3YynP6Sj25GerWjbzWsNwDldI/yb9WX9kvbvN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       "text": [
        "<matplotlib.figure.Figure at 0xbf3ab70>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Af/dO5QE3qOoRQB/g6shrg0IQfK1B0AjB05moLsXhDBgAn3wC+/eX/9ogPM8g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\nuLVisxQb6YBfRmUgMNnbnwwMjpKnF7B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       "text": [
        "<matplotlib.figure.Figure at 0x9f87f98>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rR9M/1rja63oOtYfWhYnWhYnWhTX4U8+hr1JqjVKqQClVppSyK6X8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\nDAaDoQj/D/hcSEQY07PyAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x88dc828>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Operator is saved to: C:\\users\\zahup\\desktop\\F2_01_colour_operator.dat\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}