sgkang/DC_schumberger_Inv.ipynb

## DC_schumberger_Inv.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:19c3127b59d003240c9fc3ef942fdeb1b3f06fc6f97ca83dc7d01ca16af87352"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from SimPEG import *\n",
      "import simpegDC as DC\n",
      "from simpegem1d import Utils1D\n",
      "import simpegEM.Utils as EMUtils\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "1D DC inversion of Schlumberger array"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is an example for 1D DC Sounding inversion. This 1D inversion usually use analytic foward modeling, which is efficient. However, we choose different approach to show flexibility in geophysical inversion through mapping. Here mapping ($M$)indicates transformation of our model to a different space:\n",
      "\n",
      "$$\n",
      "    \\mathbf{m} = M(\\mathbf{\\sigma})\n",
      "$$\n",
      "\n",
      "Now we consider a transformation, which maps 3D conductivity model to 1D layer model. That is, 3D distribution of conducitivity can be parameterized as 1D model. Once we can compute derivative of this transformation, we can change our model space, based on the transformation. \n",
      "\n",
      "Following example will show you how user can implement this set up with 1D DC inversion example. Note that we have 3D forward modeling mesh."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Step1: Generate mesh"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cs = 25.\n",
      "npad = 11\n",
      "hx = [(cs,npad, -1.3),(cs,41),(cs,npad, 1.3)]\n",
      "hy = [(cs,npad, -1.3),(cs,17),(cs,npad, 1.3)]\n",
      "hz = [(cs,npad, -1.3),(cs,20)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mesh.plotGrid()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nHJ22bQ3uvNPBihUyw4drLF4s89xzB/j3v3P5/POMvwARHGZkZRmUlZV/iffq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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4702410>"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Step2: Generating model and mapping (1D to 3D)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mapping = Maps.ExpMap(mesh)*Maps.Vertical1DMap(mesh)\n",
      "siglay1 = 1./(100.)\n",
      "siglay2 = 1./(500.)\n",
      "sighalf = 1./(100.)\n",
      "sigma = np.ones(mesh.nCz)*siglay1\n",
      "sigma[mesh.vectorCCz<=-100.] = siglay2\n",
      "sigma[mesh.vectorCCz<-150.] = sighalf\n",
      "mtrue = np.log(sigma)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dat = mesh.plotImage(mapping*mtrue)\n",
      "print (mapping*mtrue).min()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.002\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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oKy2tNOn6EbFihiN/o+e7TlX163lNnEj22bOkx8Yas/Ra1TVbr6lTCVyxgh3B\nwcRv21Zle8lHjxK4fHmlE/N3DRmCotGQfPSoaULdwNgZqzDzq52GyOc2fDhjtmxh/8sv8+3bb5sk\nR3UafN+B/motjdawti7Nv4LLKSkoGg323t7sfuopchITsffy4kBYGDmJlW8cqdFq6eDpCYC1jQ3N\n7eyw9/GhtLiYS7/8AsCpTz9l1MaN9J01izO7dnFH27YMWryYy6mppMTENPl8be+6iy5/+QvJ0dFY\n29jgFxKC59ixbHr44Uadrd/zzzP4zTf5fMYMzh0+TEt7ewBKi4spzM4G4OdNm3jglVcYtWkTXy9c\nSAs7O4avXElcRAS55XeeMzVjZwSw9/EBoHm7djSzscHe2xsUxSzDQ8bO51F+vu3w4sX8vGmTfhm1\ntJSCS5eadDa/kBAKs7P5PT6eksJCOvbsyeClS7lw/DgX4+IMqkma/w0c/PwoLSoi88wZrG1t6eDp\nyblDh6osZ+PkxNM//ABcGx927NUL95EjyUlKYoWrKwAnt2yhWatW+D/3HAP/+U+uFhSQEh3Nx8OG\ncfWPP0ya6zpj5lM0Gvo8+yzDV65EVVVSjx1j/YMPmu3I2NBs/jNnomg0PPz++zz8/vv66UkHDvBR\n+Z3krhYU8NHgwTz0zjuEREdTcuUK8Vu38sWcOSbLUx1jZgT0+xiu7eenY2NRVZV/Gnj0aGzGzNf7\nmWdQrKwIePXVSm8+rPgcNiVjZisrKeH+hQtp6+qKRqvlcnIyv3z6aZ0uYZWbuZjZ7XwzCZB8TZ3k\na7rkZi5CCCGqkOYvhBAWqEkN+zSRUoUQolGQYR8hhBCVNKmrfW7XkzJwe2YDydfUSb6mK7SWkRI5\n8hdCCAskzV8IISyQNH8hhLBA0vyFEMICSfMXQggLJM1fCCEskDR/IYSwQNL8hRDCAknzF0IICyTN\nXwghLJA0fyGEsEDS/IUQwgJJ8xdCCAskzV8IISxQrc1/ypQp2Nvb4+XlpZ+WlZXFkCFDuPvuuxk6\ndCg5OTn6eW+88QZubm706NGDvXv36qcfP34cLy8v3NzcmDVrln56UVER48aNw83NjX79+nHu3Dlj\nZRNCCFGDWpv/k08+yZ49eypNW7JkCUOGDOHMmTMMGjSIJUuWABAfH8/mzZuJj49nz549PPPMM/q7\nyEyfPp01a9aQkJBAQkKCfptr1qzBzs6OhIQEZs+ezfz5842dUQghxA1qbf73338/bdu2rTRt586d\nTJ48GYCTLCzgAAAgAElEQVTJkyezY8cOACIjI5kwYQI6nQ4XFxe6detGTEwMaWlp5OXl4e/vD0Bw\ncLB+nYrbGj16NPv27TNeOiGEENWq15h/RkYG9vb2ANjb25ORkQHAhQsXcHZ21i/n7OxMampqlelO\nTk6kpqYCkJqaSufOnQHQarW0bt2arKys+qURQghhkFs+4asoCspteAs0IYS4ndXrHr729vakp6fj\n4OBAWloaHTt2BK4d0ScnJ+uXS0lJwdnZGScnJ1JSUqpMv77O+fPn6dSpEyUlJeTm5tKuXbtqv+/+\nCv93Ae6sT/FCCHGbSgSSyv+vhoXddNl6HfkHBQWxfv16ANavX8+jjz6qnx4REUFxcTGJiYkkJCTg\n7++Pg4MDtra2xMTEoKoqGzZsYMSIEVW2tW3bNgYNGlTj9x1Y4SGNXwghKruTP3tk2K02/wkTJnDf\nffdx+vRpOnfuzLp163jxxRf58ssvufvuu/n666958cUXAfDw8GDs2LF4eHjw0EMPER4erh8SCg8P\nZ+rUqbi5udGtWzcCAwMBCAkJITMzEzc3N95++239lUONwdz0dBx79QLgiYMH6Tl+vH5eBw8PxmzZ\nwrOnT/NKSQmPrF5dZf3uQUFM/Owz5ly4wIL8fKb//DP+zz1nsvprc6v5HHx9mbx/P3PT0lh45Qqz\nkpJ4aMUKrG1tTZahJrearaKW9vbMTUvj1dJSWjk6NmjddXGrGbsGBPBqaWmVh++TT5osQ02Msf8U\njYb+8+cz49QpFl65wtz0dB5etcok9dfmVvONWLeu2n33SkkJze3sDKqh1mGfTz75pNrpX331VbXT\nX3rpJV566aUq0++55x5+/vnnKtOtra3ZsmVLbWWYXFtXV3QtWpAWG4tGp6NT796cP3JEP1/bvDm5\nSUmcjozk3jlz9Je0VtQ1IIDko0c5uGgR+RkZ3DlwIMNXrkR7xx188+9/mzJOFcbIV1JYSOzataTH\nxnIlO5v2PXowfOVKbDt3ZvPIkaaMU4kxsukpCqM2biQlJobujzxiguoNY8yMq/z8yEtL039ddPly\ng9ZeG2NlG/Hhhzj37cuX8+aRfuIE1jY2tL3rLlPFqJEx8kXNnMmX8+bpv1YUhXE7dlCcn8+VzEyD\n6qjXmL8l6NK/P6kxMaCqOPXpQ0FmJpcrnLdIO36ctOPHAfALCal2G3vnzq309YkPP8TBzw/PsWPN\n3vyNke/SqVNcOnVK/3Veairfh4cTEBrasMXXwhjZrgt45RVKCgv59q23GlXzN2bGgkuXKPj99wat\nty6Mkc1lwAB6jh/P+97elZ6jF+PiGrZ4AxgjX3FeHsV5efqv27m54dy3L1sfe8zgOqT532B+djaq\nqqK1tkbRaJiXlYWVToeVtTXzsrJAVXnTwJdV1bmjTRuK8/ONWHHdNGQ+W2dn3MeMISEqyshVG8bY\n2VwGDMBv6lRW+fnRsWfPBqzccA2x/548cgRdixZk/forx1et4qcNGxqo+pszZjb30aPJPnsW16FD\nmbBrF1bNmpEcHc2XL7xQqdGaUkP+7vV++mny09M5Vf7+KUNI87/Be97eKIpCyLff8tm0aaSfOMHo\niAjiNm3iVGTkLW27a0AAPcePN+uQSEPkm3L0KA6+vmjvuINfv/iCnbUcaTYUY2Zr2bEjIzdsYHtw\nsMEvo03BmBnzLlxg97RpXPj+ewDufvhhHvngA9p168YBM7x6M2a2tq6utO7ShZ4TJxI5ZQplV6/y\n4OuvE/z117zXsyelxcUNlKJmDdVbrJo1w2fyZI6vWoVaVmbwetL8b3A5OZmOXl5Y6XSc3rWLZq1a\n4eDrS0RQEAWXLtV7u059+zJu+3YOhIaS8PnnRqy4bhoi39axY2nWsiUdPDx48PXXGbN5M5vLrwAz\nJWNmG7VxIz9+9BFJ+/dXmm7u97QYM2NWQgJZCQn6r9NjY9FYWdFvzhwOLlpUp0ZiDMbMpmg0aK2t\n2TF5MpmnTwOwbdw45qal4TZ8eJ2OkI2loXqLx5gx3NG2LcdruXDhRtL8K5geF0frLl3QaLVY6XS8\nmJurfxLNPHsWgJXu7uSVvzvZUF0DApiwcyeHFy/m6NKlDVG6QRoq3/XlM8+cIS8tjZBvvqF9jx6V\nxlobmrGz3fngg3QNCOC+f/wD+LPpz0pKIvb//o/PnnmmYYLcREPtv4qSo6N5oGVLWnTowB/l79w3\nBWNny09LQ1VVfeOH8nMbly7RukuXBslwMw257+6ZNo3fvviC3PPn67SeNP8KNgYGYtWsGUFr1/Jr\nVBQnt2whIDSU0qIijpRfgppf4aoIQ7gNH86YLVvY//LLfPv22w1RtsEaIt+NNFZW1/7VmvapZexs\n4TeM8Tv5+zNi7Vo+HjqU33/5xai1G8oU+8+xVy+uFhTc0pFofRg727lDh/AJDqadm5v+1U3zdu1o\n0b49OUlJDRHhphpq37V3d6dL//71GkqW5l/B5ZQUFI0Ge29vdj/1FDmJidh7eXEgLIycxMRKy2q0\nWjp4egJgbWNDczs77H18KC0u5lJ5c/AYM4ZRGzdyePFift60iZbln4eklpaa/JcLjJ/PLySEwuxs\nfo+Pp6SwkI49ezJ46VIuHD9u8qsqjJ3t0g0NvmX5u9gvnT5t0iPiioydsd/zz5Nz7hy/x8eDquI6\nbBj3L1zId+++i1pa2qSzxX3yCfcvXEjQmjXsmTWLspISBi9dSmZCglkuSDB2vut6P/00eRcucHrX\nrjrXJM3/Bg5+fpQWFZF55gzWtrZ08PTk3KFDVZazcXLi6R9+AEBVVRx79cJ95EhykpJY4eoKQO9n\nnkGxsiLg1VcJePVV/boVlzE1Y+YrKynh/oULaevqikar5XJyMr98+qnZLmM1Zrbq3PT9ACZizIyK\nlRWDFi/GtnNnyq5eJTMhgT0zZxK7dq1JM11nzGwlhYVsGDyYYW+/zRMHDlBSWEji/v1sGDyYsqtX\nTZrrOmM/P7V33IH3pEkce+cdqMdzU1EbwzPaAIqiEGbuIhpAaPmPf9Ft+uF4kq9pk3xNV6iqoihK\njQctchtHIYSwQNL8hRDCAjWpYZ8mUqoQQjQKMuwjhBCikiZ1tc/telIGbs9sIPmaOsnXdIXWMlIi\nR/5CCGGBpPkLIYQFkuYvhBAWSJq/EEJYIGn+QghhgaT5CyGEBZLmL4QQFkiavxBCWCBp/kIIYYGk\n+QshhAWS5i+EEBZImr8QQlggaf5CCGGBpPkLIYQFkuYvhBAWSJq/EEJYIGn+QghhgaT5CyGEBZLm\nL4QQFkiavxBCWCBp/kIIYYGk+d+iuenpOPbqBcATBw/Sc/z4SvO1zZsz6I03mHn2LAsLC5mdnExA\naKg5Sq2Xm+WbvH8/r5aWVnksyMszV7l1Vtv+83/2WZ45eZIF+fnMSU1lxLp1tOjQwRyl1tnNsilW\nVtz3j38w45dfeKmggGdPn6b39OnmKrWKm9XewcODMVu28Ozp07xSUsIjq1dXu412bm48vmcPC/Lz\neeHiRf4aHo62eXOT1F+bW83X0t6ekR9/zPSff+bl4mL+tndvnWvQ1r980dbVFV2LFqTFxqLR6ejU\nuzfnjxzRz1c0GiZ+9hnNWrVi91NPcen0aVrY2TWZ5lFbvs0jR6LR6fRfKxoNf//uO37bs8cc5dZZ\nbfl6jh/P0GXL2D1tGme/+orWnTvz1/ffZ+RHH7HxoYfMWHntass2cNEiev397+z6+99J//FHOt93\nH4+sXk1pcTGxa9aYsfLaa9c2b05uUhKnIyO5d84cVFWtsg1dy5YE79tH+okTrLn3XlrY2RG0di1B\nbdrw6cSJpoxThTHyaa2tuZKZSfSyZXiMHYvGyqrOddxS83dxccHW1hYrKyt0Oh3Hjh0jKyuLcePG\nce7cOVxcXNiyZQtt2rQB4I033mDt2rVYWVmxYsUKhg4dCsDx48d54oknKCwsZPjw4SxfvvxWyjKZ\nLv37kxoTA6qKU58+FGRmcjklRT/fJzgYx169WOHqypXMTAAuJyebq9w6qy1fYU5OpeXvGjwYWycn\nvn//fVOXWi+15XPq25eMn37ixLp1wLV998Pq1QxYtMhcJRus1ufm5Ml885//cHrnTgByz53Dyd+f\n+xcuNHvzr632tOPHSTt+HAC/kJBqt+E1cSIt7Oz4dOJEivPzAfh8xgwm7t7NvgULyD13ruGD1MAY\n+XLPn2fPrFkAdA0IwMbJqc513FLzVxSFAwcO0K5dO/20JUuWMGTIEObNm8fSpUtZsmQJS5YsIT4+\nns2bNxMfH09qaiqDBw8mISEBRVGYPn06a9aswd/fn+HDh7Nnzx4CAwNvpbQGNT87G1VV0Vpbo2g0\nzMvKwkqnw8ramnlZWaCqvGlnh/vo0aQeO8a9s2fjPWkSpVevkrhvH1+9+CKF2dnmjlEjQ/Pd6J5p\n00j74QfSfvjBDFUbztB8v0ZF4RcSQtcHHuDcoUO0tLfH47HHOLN7t7kj1MjQbFbW1pQWFVVat6Sw\nkDZdu2Lr7FypGTW22g3RuX9/kr/5Rt/4Ac5++SVqWRmd77vPLM3fmPmM4ZaHfW58SbJz504OHjwI\nwOTJkxkwYABLliwhMjKSCRMmoNPpcHFxoVu3bsTExNC1a1fy8vLw9/cHIDg4mB07djTq5v+etzeK\nohDy7bd8Nm0a6SdOMDoigrhNmzgVGalfrq2rK21cXCi7epUtY8bQrFUrhr31FuN37ODDgAAzJrg5\nQ/NV1MrBge6PPMLnM2aYuNq6MzTfb3v38sXzz/O3L75A0WjQaLWc2b2bnVOnmrH6mzM0269RUfjP\nnMnZffv4/eRJnPz98ZsyBVVVsenUySzNvz7Pu5rYODqSn55eaVpZSQlXsrKwcXQ0ZtkGM2Y+Y7il\nE76KojB48GB69+7NBx98AEBGRgb29vYA2Nvbk5GRAcCFCxdwdnbWr+vs7ExqamqV6U5OTqSmpt5K\nWQ3ucnIy1q1bY6XTcXrXLq5kZ+Pg60tcRASXk5P1QzuK5tqPd9v48Vz47juS9u9n55QpdPnLX7D3\n8TFnhJsyNF9FflOmcPXKFX7etMkMFdeNofnufuQRhr31Fl/Mns2qXr3YOHw4be68kxFr15o5Qc0M\nzbZn1iwufP89006c4OXiYsZs3swP//d/KIqCWlbWqGs3RHXj5OZmzHzGcEtH/kePHsXR0ZHff/+d\nIUOG0KNHj0rzFUVBUZRbKrCi/RX+7wLcabQtG256XBytu3RBo9VipdPxYm4uikaD1tqamWfPArDS\n3Z281FTy09Kw0ukornD1y+/x8QC06dqVjB9/NEOCm6tLPj1Fodff/87PGzdytaDATJUbpi757n/p\nJX76+GP9OYzfT56kOD+fJw8dYv+rr5KTmGjOKFXUJVthTg7/Gz+eT62saNmxI/lpafqrfbLLl22s\ntRsiPy0N286dK03TaLU0b9eOvLQ0o9dfG2Pnq0kikFT+fzUs7KbL3lLzdyx/+dShQwdGjhzJsWPH\nsLe3Jz09HQcHB9LS0ujYsSNw7Yg+ucJftpSUFJydnXFyciKlwkvMlJQUnGo4eTHwVoo1ko2BgVg1\na0bQ2rX8GhXFyS1bCAgNpbSoiCNLlgDXnngA5w4d4r5//INmrVrpxx7tuncHICcpySz116Yu+a7r\nFhhI6y5dOL5qlTlKrpM65VMU1NLSSutfPyo25kGNsdRn36mlpfppPSdMIOngQa5kZTWJ2m8m+ehR\nApcvr/S7d9eQISgaDclHjzZIhpsxdr4qyl/p3MmfB8WhYWEsusnFCfUe9ikoKCCv/Ij2jz/+YO/e\nvXh5eREUFMT69esBWL9+PY8++igAQUFBREREUFxcTGJiIgkJCfj7++Pg4ICtrS0xMTGoqsqGDRv0\n6zRGl1NSyElKwt7bm1Pbt5OTmIi9lxdndu8mJzGRnMREfYP4LjyckitXeHT9ejp4eNCpTx8e+eAD\nkg4cIOOnn8ycpHp1yXfdPU8/TeqxY402U0V1yXfq00/xnTIF70mTaOPiQpe//IWH3nmH9B9/NMvR\ncW3qks3xnnvwGDOGtnfdhXO/fjy2dSv23t7smTmz0deu0Wqx9/HB3scHaxsbmtvZYe/jQ3t3d/32\nft60iYJLlxi1aRMdvbxwGTCA4StXEhcRQe75800+H6Bfpnm7djSzscHe27tOw8n1PvLPyMhg5MiR\nAJSUlPD4448zdOhQevfuzdixY1mzZo3+Uk8ADw8Pxo4di4eHB1qtlvDwcP3RU3h4OE888QRXrlxh\n+PDhjfpkL4CDnx+lRUVknjmDta0tHTw9OXfoUJXl/sjIYP2DDzLsv/9l6rFjFGZnk/D553w5b54Z\nqjacofkAbDp1wm34cHY/9ZSJq6w/Q/MdffNNVFXlLwsW0Pq99yjMySHx66/Zt2CBGao2jKHZtNbW\nPPDqq7RzdaW0uJikgwdZe999+mFJczC0dhsnJ54uv6JMVVUce/XCfeRIcpKSWOHqCsDVggI+GjyY\nh955h5DoaEquXCF+61a+mDPHpJkqMmY+QL/M9eWejo1FVVX+qTWsrStqYzwzUg1FUQgzdxENILT8\nx7+oEQ4jGIPka9okX9MVqqrXTuDX0OLl4x2EEMICSfMXQggLJM1fCCEsUJMa828ipQohRKMgY/5C\nCCEqaVIf6Xy7npGH2zMbSL6mTvI1XaG1jJTIkb8QQlggaf5CCGGBpPkLIYQFkuYvhBAWSJq/EEJY\nIGn+QghhgaT5CyGEBZLmL4QQFkiavxBCWCBp/kIIYYGk+QshhAWS5i+EEBZImr8QQlggaf5CCGGB\npPkLIYQFkuYvhBAWSJq/EEJYIGn+QghhgaT5CyGEBZLmL4QQFkiavxBCWCBp/rdobno6jr16AfDE\nwYP0HD++0nwnf3+mHD3KSwUFzElN5cHXXwdFMUep9XKzfB08PBizZQvPnj7NKyUlPLJ6tbnKrLeb\n5fN98kmCv/6aFy5e5MXcXP7+3Xf0nDDBXKXWy83yuQ4dypRvvuGFixd5qaCA5xISGPjaa2i0WnOV\nWye1/e5d197dnQX5+bxcXGzK8m7ZzfL5TJ7Mq6WlVR4uAwcavP2msZcbqbauruhatCAtNhaNTken\n3r05f+SIfr6tszOTvvyS+K1b2RkSgt3ddxO0di2KorDvpZfMWLlhasunbd6c3KQkTkdGcu+cOaiq\nasZq6662fC4DB3Jq+3a+fOEFrmRn4z5qFCM3bKCspIT4rVvNWLlhastXmJvLt2+9xcW4OIrz8nDs\n1YuHV6+mmY0NX8yebcbKa1dbtuu0zZvz2JYtJO7bR7fAQDNUWj+G5CsrLeW/nTpVOpgszM42+HtI\n878FXfr3JzUmBlQVpz59KMjM5HJKin5+7+nTKczJYefUqQBcOnWK/a+8wpA33+Tga69RUlhortIN\nUlu+tOPHSTt+HAC/kBBzlVlvteXbERxcafnoZcvo+sADeI4d2ySaf235UmNirs0vdzklBZcBA+ga\nEGCOcuuktmzXDV+5knOHDpEaE0O3hx4yQ6X1Y2i+gkuX6v09pPnXw/zsbFRVRWttjaLRMC8rCyud\nDitra+ZlZYGq8qadHZ379+e3vXsrrfvbF18w/N13cfDzIyU62kwJbs7QfE3VreS7o00bss+eNXHF\ndVPffHbdu+MaGMipTz81Q9WGqUs270mT6HTPPXzQp0+TGa6rSz6NlRXP/foruubNuXT6NNH/+Q8J\nn39u8PeS5l8P73l7oygKId9+y2fTppF+4gSjIyKI27SJU5GR+uVaOThw/vDhSuvmp6cDYOPoaNKa\n68LQfE1VffN5Pf44Tn37EjVzpgmrrbu65pudnEyL9u2xataME+vW8fXLL5uhasMYmq19jx4M/c9/\n+HDAAEqb0Fi/ofkunTrF9uBgMn76CV3z5vQcP54Ju3axc+pUTqxbZ9D3kuZfD5eTk+no5YWVTsfp\nXbto1qoVDr6+RAQF3dLLsMZC8lXVPSiIR1avZueUKWT8+KOJK66buuZb278/uhYtcOzVi8FLlxK4\nfDl7Zs0yQ+W1MySbVbNmPLZ1K1+//DKXfvnFzBXXjaH77sYhu9Rjx7ijXTv6z58vzb+hTI+Lo3WX\nLmi0Wqx0Ol7MzUXRaNBaWzOzfDhgpbs7eamp5KelVTnCb2lvD0BeWprJazdEXfI1RfXJ5zluHCPW\nrWPX1Kn8vGmTuUo3SH3y5Z4/D1w7miwrLWXUxo3sW7CAqwUFZslQE0OzabRaOnh4MHzlSoavXAmA\noigoGg0vFxez/5VXOLp0qTmjVOtWf/dSoqNrvOKpOtL862hjYCBWzZoRtHYtv0ZFcXLLFgJCQykt\nKuLIkiUA5Jc39uSjR/GeNKnS+t0CAyn+4w/SY2NNXrsh6pKvKaprvl5TpxK4YgU7goOJ37bNXGUb\n7Fb3n8bKCgCl/N/GxOBsikJ4z56V1u3x6KMMWLSI9318+OPiRXOUX6tb3XeOvXrp/5AbQpp/HV1O\nSUHRaLD39mb3U0+Rk5iIvZcXB8LCyElMrLTsd++9R59nn+WRDz7g27feoq2rKwNfe41j77zTaK/0\nqUs+jVZLB09PAKxtbGhuZ4e9jw+lxcWN9uV2XfL1e/55Br/5Jp/PmMG5w4f1r9pKi4vrdEmdKdUl\n371z5vD7L7+QlZCAqqp06t2bwUuXcmrHDorz8syUoGZ1yXbj8y/P37/a6Y1JXfIFhIaSGhNDZkIC\nWmtrPMaMwW/KFKKee87g79domv+ePXt4/vnnKS0tZerUqcyfP9/cJdXIwc+P0qIiMs+cwdrWlg6e\nnpw7dKjKcnmpqXw8dChD//tf/v799xTm5HB81apGfUINDM9n4+TE0z/8AICqqjj26oX7yJHkJCWx\nwtXV1GUbzNB8/jNnomg0PPz++zz8/vv66UkHDvDRoEGmLLlODM2n0WoZ8uabtHFxQS0rIycpiWPv\nvsu3b79thqoNY2i2ajWB96EYms/axobhK1fSysGBq1eucOmXX9j62GOc2rHD4O+lqI3gnTmlpaV0\n796dr776CicnJ/r06cMnn3yCu7u7fhlFUQgzX4kNJrT8x7+owhs1EoE7zVSPsVWX70ZNOa8h+Spq\nalnrmq+ippD1VvLdqLHlDVVVFEWp8c2XjeLjHY4dO0a3bt1wcXFBp9Mxfvx4Im+DSwrrK8ncBZhY\nkrkLMKEkcxdgQknmLsDEksxdQB01iuafmppK586d9V87OzuT2kSvJhFCiKagUYz5Kwa+5Ao1/whV\ng6mYTQ0LIzQszHzFNICb7bvbIa+hz82mmrU+v3tNKasxektTyguA2ghER0erw4YN03+9ePFidcmS\nJZWWcXV1VQF5yEMe8pCHgQ8fH58a+26jOOFbUlJC9+7d2bdvH506dcLf37/KCV8hhBDG0yiGfbRa\nLe+++y7Dhg2jtLSUkJAQafxCCNGAGsWRvxBCCNNqFFf73MyePXvo0aMHbm5uLG2En8dhKBcXF7y9\nvfHz88O//N2GWVlZDBkyhLvvvpuhQ4eSk5OjX/6NN97Azc2NHj16sLfCx0IfP34cLy8v3NzcmNWI\nPnxrypQp2Nvb4+XlpZ9mzHxFRUWMGzcONzc3+vXrx7lz50wTrBrVZQ0LC8PZ2Rk/Pz/8/PyIiorS\nz2vKWZOTkxk4cCCenp707NmTFStWALfvvq0p7225fxvkDK6RlJSUqK6urmpiYqJaXFys+vj4qPHx\n8eYuq15cXFzUzMzMStP+8Y9/qEuXLlVVVVWXLFmizp8/X1VVVT158qTq4+OjFhcXq4mJiaqrq6ta\nVlamqqqq9unTR42JiVFVVVUfeughNSoqyoQpanbo0CH1hx9+UHv27KmfZsx8K1euVKdPn66qqqpG\nRESo48aNM1m2G1WXNSwsTF22bFmVZZt61rS0NDU2NlZVVVXNy8tT7777bjU+Pv623bc15b0d92+j\nPvK/3d78pd4wwrZz504mT54MwOTJk9lR/tbsyMhIJkyYgE6nw8XFhW7duhETE0NaWhp5eXn6Vw7B\nwcH6dczt/vvvp23btpWmGTNfxW2NHj2affv2mSpaFdVlhar7F5p+VgcHB3x9fQFo1aoV7u7upKam\n3rb7tqa8cPvt30bd/G+nN38pisLgwYPp3bs3H3zwAQAZGRnYl39YmL29PRkZGQBcuHABZ2dn/brX\nc9843cnJqVH/PIyZr+JzQavV0rp1a7KyskwVxSDvvPMOPj4+hISE6IdBbqesSUlJxMbG0rdvX4vY\nt9fz9uvXD7j99m+jbv6GvvmrKTh69CixsbFERUWxcuVKDt9why9FUW6rvDe63fNNnz6dxMRETpw4\ngaOjI3PnzjV3SUaVn5/P6NGjWb58OTY2NpXm3Y77Nj8/nzFjxrB8+XJatWp1W+7fRt38nZycSE5O\n1n+dnJxc6a9pU+JYflOXDh06MHLkSI4dO4a9vT3p5bd1TEtLo2PHjkDV3CkpKTg7O+Pk5ERKhZs4\np6Sk4OTkZMIUdWOMfNf3t5OTE+fLP6u8pKSE3Nxc2rVrZ6ooterYsaO+CU6dOpVjx44Bt0fWq1ev\nMnr0aCZNmsSjjz4K3N779nrev/3tb/q8t+P+bdTNv3fv3iQkJJCUlERxcTGbN28mKCjI3GXVWUFB\nAXnln4/+xx9/sHfvXry8vAgKCmL9+vUArF+/Xv9ECwoKIiIiguLiYhITE0lISMDf3x8HBwdsbW2J\niYlBVVU2bNigX6cxMka+ESNGVNnWtm3bGNTIPlI5rcJNNrZv366/EqipZ1VVlZCQEDw8PHj++ef1\n02/XfVtT3tty/5rlNHMdfP755+rdd9+turq6qosXLzZ3OfVy9uxZ1cfHR/Xx8VE9PT31OTIzM9VB\ngwapbm5u6pAhQ9Ts7Gz9Oq+//rrq6uqqdu/eXd2zZ49++vfff6/27NlTdXV1VZ977jmTZ6nJ+PHj\nVUdHR1Wn06nOzs7q2rVrjZqvsLBQfeyxx9Ru3bqpffv2VRMTE00Zr5Ibs65Zs0adNGmS6uXlpXp7\ne5uuNL4AAABwSURBVKsjRoxQ09PT9cs35ayHDx9WFUVRfXx8VF9fX9XX11eNioq6bfdtdXk///zz\n23L/ypu8hBDCAjXqYR8hhBANQ5q/EEJYIGn+QghhgaT5CyGEBZLmL4QQFkiavxBCWCBp/kIIYYGk\n+QshhAX6f5rJVyl8KCVjAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5424110>"
       ]
      }
     ],
     "prompt_number": 139
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1, figsize = (3, 6))\n",
      "Utils1D.plotLayer(1./sigma, mesh.vectorCCz, 'log', ax = ax)\n",
      "ax.set_ylim(-600, 0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 140,
       "text": [
        "(-600, 0)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CwsIgIsjJyUGTJk3w6aefok+fPvUuQG9GDoiRayP9eSS8ffv2RUBAAFasWIEWLVoAAM6e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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4b9a090>"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1, figsize = (7, 5))\n",
      "dat = mesh.plotSlice((mapping*mtrue), normal='Y', ind = 9, ax = ax)\n",
      "cb = plt.colorbar(dat[0], ax =ax)\n",
      "ax.set_title(\"Vertical section\")\n",
      "cb.set_label(\"Conductivity (S/m)\")\n",
      "ax.set_xlim(-1000., 1000.)\n",
      "ax.set_ylim(-500., 0.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 213,
       "text": [
        "(-500.0, 0.0)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZSImISMjtg40CgQCy2Sw8Hg+azSZ0XceXX37JJQKJiGg03D7YKJPJYHl5uePY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\n4P8DMBRqwtvuBuIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6cda950>"
       ]
      }
     ],
     "prompt_number": 213
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Step3: Design survey: Schulumberger array"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<img src=\"http://www.landrinstruments.com/_/rsrc/1271695892678/home/ultra-minires/additional-information-1/schlumberger-soundings/schlum%20array.JPG\"> </img>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "$$ \\rho_a = \\frac{V}{I}\\pi\\frac{b(b+a)}{a}$$"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Let $b=na$, then we rewrite above equation as:"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "$$ \\rho_a = \\frac{V}{I}\\pi na(n+1)$$"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Since AB/2 can be a good measure for depth of investigation, we express "
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "$$AB/2 = \\frac{(2n+1)a}{2}$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ntx = 16"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 142
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xtemp_txP = np.arange(ntx)*(25.)-500.\n",
      "xtemp_txN = -xtemp_txP\n",
      "ytemp_tx = np.zeros(ntx)\n",
      "xtemp_rxP = -50.\n",
      "xtemp_rxN = 50.\n",
      "ytemp_rx = 0.\n",
      "abhalf = abs(xtemp_txP-xtemp_txN)*0.5\n",
      "a = xtemp_rxN-xtemp_rxP\n",
      "b = ((xtemp_txN-xtemp_txP)-a)*0.5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 143
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print a\n",
      "print b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100.0\n",
        "[ 450.  425.  400.  375.  350.  325.  300.  275.  250.  225.  200.  175.\n",
        "  150.  125.  100.   75.]\n"
       ]
      }
     ],
     "prompt_number": 144
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1, figsize = (12,3))\n",
      "for i in range(ntx):\n",
      "    ax.plot(np.r_[xtemp_txP[i], xtemp_txP[i]], np.r_[0., 0.4-0.01*(i-1)], 'k-', lw = 1)\n",
      "    ax.plot(np.r_[xtemp_txN[i], xtemp_txN[i]], np.r_[0., 0.4-0.01*(i-1)], 'k-', lw = 1)\n",
      "    ax.plot(xtemp_txP[i], ytemp_tx[i], 'bo')\n",
      "    ax.plot(xtemp_txN[i], ytemp_tx[i], 'ro')\n",
      "    ax.plot(np.r_[xtemp_txP[i], xtemp_txN[i]], np.r_[0.4-0.01*(i-1), 0.4-0.01*(i-1)], 'k-', lw = 1)    \n",
      "\n",
      "ax.plot(np.r_[xtemp_rxP, xtemp_rxP], np.r_[0., 0.2], 'k-', lw = 1)\n",
      "ax.plot(np.r_[xtemp_rxN, xtemp_rxN], np.r_[0., 0.2], 'k-', lw = 1)\n",
      "ax.plot(xtemp_rxP, ytemp_rx, 'ko')\n",
      "ax.plot(xtemp_rxN, ytemp_rx, 'go')\n",
      "ax.plot(np.r_[xtemp_rxP, xtemp_rxN], np.r_[0.2, 0.2], 'k-', lw = 1)    \n",
      "\n",
      "ax.grid(True)    \n",
      "ax.set_ylim(-0.2,0.6)\n",
      "# ax.set_xlim(-600,600)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 145,
       "text": [
        "(-0.2, 0.6)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+yMP3v/99dXZ2krsYcrLHniT94Q9/0NatW/Xkk0+GbiN/9vl6znp7e4edOUFs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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4da7b90>"
       ]
      }
     ],
     "prompt_number": 145
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1, figsize = (5,3))\n",
      "mesh.plotSlice(np.log10(mapping*mtrue), grid=True, ax = ax, pcolorOpts={'cmap':'binary'})\n",
      "ax.plot(xtemp_txP, ytemp_tx, 'bo')\n",
      "ax.plot(xtemp_txN, ytemp_tx, 'ro')\n",
      "ax.plot(xtemp_rxP, ytemp_rx, 'ko')\n",
      "ax.plot(xtemp_rxN, ytemp_rx, 'go')\n",
      "ax.set_xlim(-1000, 1000)\n",
      "ax.set_ylim(-200, 200)\n",
      "ax.set_title('Survey geometry (Plan view)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 220,
       "text": [
        "<matplotlib.text.Text at 0xee2d3d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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m7Y9Vt3E/TLOkUqlgcXER1WoVOzs7CAaDE0lAfjPueIxRx2qYZi5Bu5n2Xz6/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\nEzQRkaSYoImIJPX/AVCuKckfwq4DAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x77ef110>"
       ]
      }
     ],
     "prompt_number": 220
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We generate tx and rx lists:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "txlist = []\n",
      "rx = DC.DipoleRx(np.r_[xtemp_rxP, ytemp_rx, -12.5], np.r_[xtemp_rxN, ytemp_rx, -12.5])\n",
      "for i in range(ntx):    \n",
      "    tx = DC.DipoleTx([xtemp_txP[i], ytemp_tx[i], -12.5],[xtemp_txN[i], ytemp_tx[i], -12.5], [rx])\n",
      "    txlist.append(tx)\n",
      "survey = DC.SurveyDC(txlist)    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 147
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Step4: Set up problem and pair with survey"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "problem = DC.ProblemDC(mesh, mapping=mapping)\n",
      "problem.pair(survey)\n",
      "problem.Solver = SolverWrapD(EMUtils.Solver.Mumps)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 216
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Step5: Run survey.dpred to comnpute syntetic data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = survey.dpred(mtrue)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 149
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$ \\rho_a = \\frac{V}{I}\\pi\\frac{b(b+a)}{a}$$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To make synthetic example you can use survey.makeSyntheticData, which generates related setups"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survey.makeSyntheticData(mtrue,std=0.01,force=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 217
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "appres = data*np.pi*b*(b+a)/a\n",
      "appres_obs = survey.dobs*np.pi*b*(b+a)/a\n",
      "fig, ax = plt.subplots(1,1, figsize = (6, 4))\n",
      "ax.semilogx(abhalf, appres, '.-')\n",
      "ax.semilogx(abhalf, appres_obs, '.-')\n",
      "ax.set_xscale('log')\n",
      "ax.set_ylim(100., 180.)\n",
      "ax.set_xlabel('AB/2')\n",
      "ax.set_ylabel('Apparent resistivity ($\\Omega m$)')\n",
      "ax.grid(True)\n",
      "ax.legend(('Observed', 'Predicted'), loc = 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 214,
       "text": [
        "<matplotlib.legend.Legend at 0xee25050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0+PGPAbO50hESlV4p7puqYnfUaDTYtGkTEokEIpEILBYLVqxYUdpx6Ykq4Hd9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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xe215c90>"
       ]
      }
     ],
     "prompt_number": 214
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Step6: Run inversion"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dmis = DataMisfit.l2_DataMisfit(survey)\n",
      "reg = Regularization.Tikhonov(mesh,mapping=mapping)\n",
      "opt = Optimization.InexactGaussNewton(maxIter=7,tolX=1e-15)\n",
      "opt.remember('xc')\n",
      "invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
      "beta = Directives.BetaEstimate_ByEig(beta0_ratio=1e1)\n",
      "betaSched = Directives.BetaSchedule(coolingFactor=5, coolingRate=2)\n",
      "inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaSched])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 162
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m0 = np.log(np.ones(problem.mapping.nP)*sighalf)\n",
      "mopt = inv.run(m0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SimPEG.InvProblem will set Regularization.mref to m0.\n",
        "SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
        "                    ***Done using same solver as the problem***\n",
        "SimPEG.l2_DataMisfit is creating default weightings for Wd."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "============================ Inexact Gauss Newton ============================"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   \n",
        "-----------------------------------------------------------------------------\n",
        "   0  9.67e-02  6.33e+03  2.24e+04  8.50e+03    9.06e+03      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   1  9.67e-02  4.67e+02  7.43e+03  1.19e+03    2.80e+03      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   2  1.93e-02  1.95e+02  4.57e+03  2.84e+02    4.36e+02      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   3  1.93e-02  1.02e+02  7.15e+03  2.40e+02    1.26e+02      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   4  3.87e-03  1.11e+02  6.52e+03  1.37e+02    2.18e+02      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   5  3.87e-03  3.10e+01  1.73e+04  9.80e+01    1.06e+02      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   6  7.73e-04  3.94e+01  1.45e+04  5.07e+01    1.01e+02      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   7  7.73e-04  1.04e+01  3.03e+04  3.38e+01    3.64e+01      0              "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "------------------------- STOP! -------------------------\n",
        "1 : |fc-fOld| = 1.6880e+01 <= tolF*(1+|f0|) = 8.5000e+02\n",
        "0 : |xc-x_last| = 1.3315e+00 <= tolX*(1+|x0|) = 2.6641e-14\n",
        "0 : |proj(x-g)-x|    = 3.6437e+01 <= tolG          = 1.0000e-01\n",
        "0 : |proj(x-g)-x|    = 3.6437e+01 <= 1e3*eps       = 1.0000e-02\n",
        "1 : maxIter   =       7    <= iter          =      7\n",
        "------------------------- DONE! -------------------------\n"
       ]
      }
     ],
     "prompt_number": 163
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 207
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "appres = data*np.pi*b*(b+a)/a\n",
      "appres_obs = survey.dobs*np.pi*b*(b+a)/a\n",
      "appres_pred = invProb.dpred*np.pi*b*(b+a)/a\n",
      "fig, ax = plt.subplots(1,1, figsize = (6, 4))\n",
      "ax.plot(abhalf, appres_obs, 'k.-')\n",
      "ax.plot(abhalf, appres_pred, 'r.-')\n",
      "ax.set_xscale('log')\n",
      "ax.set_ylim(100., 180.)\n",
      "ax.set_xlabel('AB/2')\n",
      "ax.set_ylabel('Apparent resistivity ($\\Omega m$)')\n",
      "ax.grid(True)\n",
      "ax.legend(('Observed', 'Predicted'), loc = 1)\n",
      "fig.savefig('obspred_dc1d_dat.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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U0Vzy3nvv4bLLLsMbb7yBl156CZ/85CfH7rR2LfD004nRbdetA554AvjSl/If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\nVmP16tVj1o/uwU6zjxACX6+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       "text": [
        "<matplotlib.figure.Figure at 0x6cbdf90>"
       ]
      }
     ],
     "prompt_number": 218
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1, figsize = (3, 6))\n",
      "ax.plot(1., 1., 'k', lw = 2)\n",
      "ax.plot(1., 1., 'r', lw = 2)\n",
      "ax.legend(('True', 'Recovered'), loc = 4, fontsize = 14)\n",
      "Utils1D.plotLayer(1./(np.exp(mopt)), mesh.vectorCCz, 'log', ax = ax, **{'lw':2, 'color':'r'})\n",
      "Utils1D.plotLayer(1./(np.exp(mtrue)), mesh.vectorCCz, 'log', ax = ax, **{'lw':2})\n",
      "ax.set_ylim(-600, 0)\n",
      "fig.savefig('obspred_dc1d_mod.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xdbb1050>"
       ]
      }
     ],
     "prompt_number": 219
    }
   ],
   "metadata": {}
  }
 ]
}