mirror of
https://github.com/wassname/simpeg.git
synced 2026-07-29 11:27:23 +08:00
763 lines
211 KiB
Plaintext
763 lines
211 KiB
Plaintext
{
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"metadata": {
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"name": "",
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"signature": "sha256:217ff4129b31cd5fb26fce3dc7aef27cf513d55e30cd6c91c3716bcc65a22097"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"from SimPEG import *\n",
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"import simpegDC as DC\n",
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"from simpegem1d import Utils1D\n",
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"import simpegEM.Utils as EMUtils\n",
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"%pylab inline"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"prompt_number": 1
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"matplotlib.rcParams.update({'font.size': 16, 'text.usetex': True, 'font.family': 'arial'})\n",
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"# matplotlib.rcParams.update({'font.size': 16, 'font.family': 'arial'})"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 2
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},
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{
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"cell_type": "heading",
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"level": 1,
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"metadata": {},
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"source": [
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"1D DC inversion of Schlumberger array"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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",
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"\n",
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"$$\n",
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" \\mathbf{m} = M(\\mathbf{\\sigma})\n",
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"$$\n",
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"\n",
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"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",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "heading",
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"level": 2,
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"metadata": {},
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"source": [
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"Step1: Generate mesh"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"cs = 25.\n",
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"npad = 11\n",
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"hx = [(cs,npad, -1.3),(cs,41),(cs,npad, 1.3)]\n",
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"hy = [(cs,npad, -1.3),(cs,17),(cs,npad, 1.3)]\n",
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"hz = [(cs,npad, -1.3),(cs,20)]"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 3
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 4
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"mesh.plotGrid()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stderr",
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"text": [
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"/usr/local/lib/python2.7/dist-packages/matplotlib/lines.py:503: RuntimeWarning: invalid value encountered in greater_equal\n",
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" return np.alltrue(x[1:] - x[0:-1] >= 0)\n"
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]
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},
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{
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"metadata": {},
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"output_type": "display_data",
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JlWh+PygKGAY8/bQluBMm6CxdKvPooxX87W+FvP9+zmZ9BPsoeXkmwWDNl/ig\nQTobNljnny24AMGgxNSpca67LkZhIfTpE2DKlASqCkuWqNxyi+L0XAiHJSZNqslhtnak+8ILL/DG\nG28AVlMdVVWRJIn169dTVFTExo0bHT+0hmiOH9qtt97KCSecwEUXXcSbb77JCSeckLJ9w4YNzvvB\nEuqSkhJKSkpYunSp87qcFV2b1hRd0zRJJBJEIhEkyeqoVJdLhT1G377WOMXFOkuWyMydq3D44SZH\nHWUydKjBM88oPPOMQteuJnfcoTFwoMlRRxm88ILCzJn5qCo89VSClSslYjHJyfsKBC0hWXABXC64\n7roY772ncMMNMU480ZoVGzJE5667alIJXbqYTJ6sceihBroODz3k4vrrPUSjEpMmpXn1tDITJkxg\nwoQJtRqY33LLLYwbN46TTjqp0WM1xw8tkUjQpUsXysrKePnll1O2l5aWMmHChFqTZoWFhWzdujXl\nuZwV3fSlwC0dK70Rsy22gGMJ1Jhv8QEDTLp1M3n8cSsCCIdh/XqJxYtlfve7GsHetUvikkvsx5aw\nHntsnAcfVFBV+OQThZdfFukFQeuwdq3C2rXWeffGG5YM9O5tcNllqXXvkYg1YfbxxzJXX+0lEDB5\n++0wl13m5ZBDUq+7tnQChhoNaCitUFdQZvud2XW3dfmh2duvu+46zjrrLAzD4M477+TCCy90ttuO\nwqeeemrKPiorKxk5cmTKczkrujZ284uWkCy6ttgahoHf72+02Npj5OVZSyRtKirg8ccVnntO5sYb\nNb7/XqJLF5Of/9xg4UKZf/yj5iNYsMDPggV17yM59wYwcKDOZ5+JSFjQMnr2NPj2W5lt22SuuMLH\njh0xBg3SGTzYYMcOieuv9/Deewo33xzjnHPs6gXarWSssQ3M165dy/Lly1m8eDHl5eX069ePMWPG\nOPnVO+64gzlz5rB48WJKS0vp379/yvLh5O3PP/88VVVV3HfffQAsXLiQd955h759+wJWpNu1a1du\nvPFG5/133nknkiTVsoXPWdFtjUi3uroaXdfx+Xy43e4mnUDpE2l79sDddys88ojCRRfprFsXp0sX\nuO02hXgcRowwGTFCp2tXqKqC11+XOeaYMPffn1fnPpIFF2om3ASCpjJokMZRR2kYhsk991RxzDFd\n+PprlTPOiLJnj8k//+nirbfsLmDWYgm77wK0b8lYepBVV53u0KFDGTp0KNOnT69zrPq22dsvu+wy\nFixYQL+lQP2KAAAgAElEQVR+/VixYgXl5eVIksTWrVt59NFHmTdvHtOnT2fy5MmUlJQ4lQ+SJLFs\n2bKUBRSQw6Jr09Kcrq7rhMNhAFRVJS8vr9mdkqxmHNYSyx49PJxxhs7q1XF69Kh5nd9vRb/Jj9es\nkVizRmbNmjwOPNDKC7/7bsO3aZs3iwk3QfO4554YK1cq/O9/Cn6/H12X6dtX54IL4nTvrjN9eoCB\nAxN89pmL++8vR1FkEomahuNWyVjqmG1ZMpa8n2Aw2Kr+aA8++GDKSjSAyZMn13pdnz59aq0+y8SP\nVnST3YC9Xq/zb3NPGvs47IZIsmyyYoXMCSe4GTrUYMgQk6FDTaqqrFlfgPJyuPVWhfLymn1+953E\nd9+lHkMgYBIKSVRVxbjlFoXFi2W2bBGCK2g+Y8fWKOb8+TXL0f/85wA//CAxY0acX/4yzmGHqbjd\nLmfVpd3nNhzOR1EixOOy4xrRVqSLu91pMFfInSNNo7klY3XZ+Nimei39ppYk6NXL5LXX4hx8MJSW\nSqxdK/HJJxL33quwYoV1cj70UO1crCybrF0boVevOA8/LHHbbfns2SMTClnH1K2bm0hEpBQETefI\nI3VOPFFn7lw3EycmuPXWGMXFPqdszGbTJuvxsmUqGzbIhMMSpaVu+vUzcO/V5kTCRNPA71cwDN3x\nPwOIx+OOCMuy7CzVzybJ12kONknMXdGFpvVeMAyDaDRKLBbL6JmWDcse+/15eVZkKssm/ftbP8XF\noGk6PXq4qay0TpiuXU127ao5IQ1DYt48gyFDFDTNw/Dh8OWXJsXFOnfeqfLggxoXXCD67wqazvr1\nCuvXW4IaDErIMo7gulym0+t59eoQgYDJ+vUyL75oyUNxsY8ffpA4/HCDI4/U6dXLWinpdruAGveH\nUCjkuGune6IlC7EtxtkkV1wjIMdFFxoWy2Sxrc9ZIpuiGwhYs7s2pgnPPScza5biCO5vfqOxdq2c\nIroAa9Z4WbtWTlkgceed1sckBFeQDZYuVTn00JoJ22nT4tx9t4cDDjDIz7f66vbsqdO7t8mHHyqs\nXh2mqgo2blRYtEjlppus1T66jpNOs0UvvdrHtmRPN6hsiTlleqSbS4ILOS66dqSbqXohU3+E5A70\nmcZqqeja+/X7a8rG3nhD4vrrVTQN7r5bw+OBm25SuftunUQiyvLlCS65pBCXC77/Xsblgk8/za2T\nSJC7nHJKgmnT4jz8sJW6si3XgZSlvi4XLFum8OyzKpdfHueVV1SSL6e6rh1JkmrlW9NteGwXCNuy\nJ12I00U1WWiDwSCB9Bm9fZycFl2g1jdeen+EhsQ2eZxs1fvm5Zm8/77MPffIlJZK3HijxuTJBrIM\nH38sEQrVlKd17hygVy8YP94AIlx3nYmiuBg92sXKlWKyTNC6vPyyi1/9SqKiwrqOLMt16zoIh62y\nsOXLFa6+2svQoTrvvx/mhx8k3n038zXV2Jr2umx4bCFOnrRLFuF0U8q6eunuy3QI0bWjXbunraIo\nGfsjNDROtkT3pZcUXnrJmlCbO1fj6KMtwbVM9eIEg3m4XC7y8vIoKpKdiGLPHollyyQmTsxsG9St\nm8n334soWNAyzj8/wX33RZk82cfgwTqGAe++a10rI0cGyM83OfJIg08+kdm5U2b7dpm77ooybpzV\nXH/bNjmjP1pLbvMbMqfUdT0lPSFJEo899hhbt24lFovx7bff0qNHj5xINeR0KJUslNXV1cRiMQKB\nQJMFN32sltKjh8mwYQZnnKFz110Khx3mZsAAleJimQcf9PHVVypVVVZ5ms9nEg5LrFkjMXeun4kT\nrbN52bI4PXqYnH66zu9/by0pnjDBcHrvCgRNpXdvKw03cKAlnoYBo0bpTJyo0bu3QSBg8s03QV58\nMUwsBjt3WvKwcmXIEVywV6O1zXloR8Vutxuv14vf70eWZVwuF7169aK6upoNGzYwfPhw9ttvP5Yt\nW9Ymx9UScjrSjcfjBINBTNPE5/Ph8XhaXGfbEuwxzjxTp1cvmDZNIxqNEg5H+fZbL59/7mfFCuuW\navBgN4EAFBWZfPutxLff1txqTZig89//ymzfLjFggITLZR3XOefo/PznBhMnigk1QdPZts0S0X/8\nwyoB++orGZ8P9rYYweczWbdO5g9/8OLxmPzhDzG+/752VBsOt38vXVmWGTduHLqu079/f/785z+z\nc+fOlPxuXYaUixYtorS0lOLiYjp16sT8+fOZPHkyffr0cV7TUsPKeo+9yX/tPobP53PyQy29vcmW\n6AYCUFGhUVFRga7rFBUVcNRRPs4+2+Rvf9Pwek127Ihz7bUaGzfW/gheeEFh7lzr+/DNN2Xuusv6\nffRotxBcQbPo39/g5z+37pgMA+bNc7Ntm8ypp/oZN87Ptm0yu3fLTJzo49e/jvPyyxF69TIzLvUN\nhdqnl24yyQ3M7dVo3bt3Jz8/nxUrVjBnzhzmzZvnuLokU1ZWxsyZM+nXrx99+/alX79+KYJbUlLC\n8OHDmTRpElOmTGHLli0sXry40dsbIqdF1+Px4Ha7s970piVYxeERqqsN8vPzycvLS5kw8PmsyYpT\nTnHx17+qPP641R7vyScTnHpqnHXrKlm4MFHLP00gaAlffSXz6qvWl/ehhxpce22M3r0Nnn8+TPfu\nNef9qlVhfvUrDVm2qhd8vtpjtXekmz6R1im5KQQNG1JKkkRFRQWlpaWUlZWlNLmBlhtWNkROi67N\nvmBOmUgknER/UZEbTfPUyitv2wYXXWQ998YbMvvvb7J6tXXybN4sEQxK9OxpMGGCwc0363i9Jpdd\npnPuucIdWJA93n5b5aSTAmzbJnPGGX527EheJFTzOrt6IZ32jnSTRbeqqqpZ1QsFBQX07t271vMt\nNaxsDDmd090XzCk1TSMcDmMYBoqi7K0JllPaO+7ZA7NnKzzxhMIVV1hiunJlgm+/hbVrrRP+xhut\nj+KUU/IYMUJiyBCTaFTCMEhZQPHXv2rcfbfieK0JBA3RqZNBPC4xcqTOG2+oDBig8+WXmUu+hgwJ\nEAiYDB5s8OqrKsOG6Zx3XoLevU1HkDN1GGvLto7JNMcJGKxo1fY0Ky0tdbqNNcWwMtP2xnwB5LTo\n2rRGI/OGSG6YY0/ihcPhvQ4T1q1ZOAz33qvw978rTJpksGZNnO7drf66Ho/JmDEwZozOgw8qzJmj\nce21MjNmRPj8cz+vvWaJ8fz5NRfHsGEGiQRCcAVNorzcOpfsZuXJgjtxYoIjjjB4/HEXffsaPPdc\nhK+/lli/XuHVV1XWrFE45RQ/waDEoEE6Rx5p8MADbk4+uX3TXy0xpRwzZkxKDvfyyy9n/vz5TJky\npUmGlZm2d3jRtf/jZVlG11t2C96UHg52wxyv1+tYhiSPoeuweLHCBx/IjBpl8OabCQYMqBk7EDD3\nNq6xnrOjBo8Hjj8+wXHH6dx5Z82F0aePydatdvvHDpERErQzRxyhs3GjwgMPRJk3z5qc9futaLZ3\nb5PevTV+9as4xxxjcMEFCfbskfj4Y5kpU6wk7yuv1F5l1pqRrm1IaS/rt6/3PXv2NLnDWbLggpWT\nLSkpYcqUKU02rEzf3hhyWnRt2sqcMhKJ1NvDwR5jyRLr+e++k9izR+KRR2SGDrVqd/v2tfJhyb0Z\n/H6rP0NFhcT993uZO9fNaacZdOlicvLJBk88IdwhBNnD5zOZNy/KT34SwOOpaTWaPmkWCll15ABf\nfSVxww0eRozQiUTg3HNT/dFas9tXsiGlaZpomuZ4FX788cfcfffddO3atVGGlBUVFY7ppB2VFhYW\nOnY7zTGsTN7eGIToNjBG8rLihno42N/EV1+ts22bxPPPJ/jkEytCfeYZmWuuUamuhspKiT/8QeX3\nv9cZNszE44FnnrFW/7z9totXX01w6KEmjzziSRHciy/WuekmjV69Mq9YEwgaw2GHGZxxhqWwY8b4\nWb3aOsfy802iUfDudWCPRCQ0Da66ysNLL6ncfnuMiRM1LrzQm3EirbUiXduQEqy0XiwWc+zPTz75\nZObNm9eopf72Mc6YMSNFIEtLS+nXrx/QPMPK5O2NIafvVVtzIs0W28rKShKJRMbyr7rGsH3S9t8f\nxo0zmTlT5+mnNb76Ks6GDXFk2cTlgsWLZU4+2cWqVTLPPWeNO2lSlOXLZYYPr12P+8gjCldckfo9\n+dvftq0jqyB3mTDBWgXx2GNlvP12OapqMmtWxNm+fr1Cr155jBrlZ+pUL6+8onLZZT50HVatCjFp\nkuWPFgy270Ra+n7qSi9k0oTCwsJajr2LFi1i9uzZzmPbkNKmLsPKurY3RM5Huk3pqdsYkns4APXa\nrteF3286jcfT6doVzjjDYNIk6wfg0EPdnHiiwb//rXD55fXforz0Uqro//OfYrGEoH569NCRJIlE\nwrrc8/NVysuhWzeD4cODnHuuzLvvupkwIc7MmVHeeMPD+edbK7suuijO3LmxlPHC4dqmlG1FYxqY\nN2RIOXXqVObMmUNRURFbtmzh8ssvr9OQsiHDykzbGyLnRReyF+mC1SquqU7A6ceRl5eas03H50vd\n3r27yb//XSOmo0YZ9O1r8uSTtaPqI4/UnWbUYN0qfv55Tt+wCFqZ7dut8+Xbb63z5JlnfOTnm3sr\nbQJomookSXg88PDDbu65x8f06dU895yPCy4IEo+bTocvSZIy1u+2Z1/b9P02ZEhZWFjYKEPKlmyv\nj5wX3WxEunb5F1hNmJvrlZa8DLg+0Q0ErPXuZWVW/e6qVTI9e5rs2AHvvbeH3bsLnPrddI46Ctav\nr3ksBFfQEBdcEOW999yOr96nnyo88YR1hzRypJ/PP7dE+a67/Pzf/2ksWxamf3+TRYsk8vNlTFMj\nFos53b2CQR8eTwJNM1vFBaI+ksVd07RG53L3JTrEFVvjxNs04TUMg1AoRFVVlfNN3tToNtNxuN3W\n+vZ4PPPrgkFrMcRRR7kJhSQmTdK56iqd/fYDr9fkpJMMrrwyyLnnhrn44ig9e9bUIO81LhYIGs2e\nPRJduxoMGGCVWd13X5SlS8MMHarz97+npg5efDHCgAGWsEUiEgUFKh6PB7/fTyAQwOfzEQ5L+HwG\niUSCcDhMOBxG1y2vtGSbntYgfQlwa7oAtxY5H+lC7UbmDZFc/pXsl5ZIJLJi2SNJOCkGd43RKoZh\nVSnYaQOv18QwYMUKGdM0UBSTUMiksrISRVEoLAxgGFYnKNvHar/9mn14gh8pL72UWu2yaJFKWZnE\n2rUKU6ZYpQqBgMmcOdGUZcCRSE3JGNTcVYbDMkVFLnw+lxPs2HeK6c3HW9MbLbnZTS6R85Fu8gKJ\nhgTTtvCpqKjAMAwKCgqc/pz2WK3lk/b22xL/938u5s5VGD9e54ordF57LcHgwQYVFRLPPqvw3Xcy\nJ5zQlWuu6czChYVs3qwQj1v1u7ZxoNttMmiQwahRVvT7pz+JxjiC+pk6NcrQoTWLhx54wM2f/mSJ\n7dy51rauXc0MdbqZJ8ySJ9KSG4+73W58Ph+BQIBAIIDb7UaSJKfMKxQKEQ6HiUajxONxdF1v8vUm\nIt19iPoE0zRN4vF4g64S2ayCCASsCoZNm+C66xQ2bpS5+WbLtufBB2W++ELmmGNMRozQqKiIs3Mn\nvP22l7PPDpKX52flStmxa0/m/PMNNm6UnR6oAkFDzJvnTXn84YcKPp/JccfpjB6tM3OmtTgneXIs\nsbcSMb1wJ5EATbNWTyaTft005I1mpyPsPHF6RFxfGViy6Dan70J7k/OiW1+trm0FHYlEnJna+sq/\nshXpmqZJaanEhReqbN8u8ac/6Tz5ZNw5UX0+CAYty+p4PE4gUIBhuNlvP4mBAzVOO03n3XfNjCvR\nLr5YZdOmmhPS7rUrENTFiBEaH31Uc5507Wqwa5fMihUqp5/ucybSfvhBxjR1JKnuKNd+PlOWoKHU\nQbI3mn0dpptU2t36kk0qkysnkq9PkV5oZ9I/EE3TqK6uJhwO4/P5yM/Pb1S9bTYcgQF0XeKTT6z2\njdu3WxbsX34poesmLlecqiorlCgsLKSw0LV3csJk40YXZ53l4pJLXIwaZXDppTqSZHLyydbt4dq1\nCU47TbR6FDSeZMEdNEhny5YQf/1rlLw8k7feqtk2bZqXgw/O47TTfFx1lZfKSonPP5dJbmtSVy/d\n5mILscvlwuPxOOkJn8/nXK924BQKhRxhfu211/j666/Jy8trYA/7Hh0mTKppNqMTDofRNA2/3+/k\nlZoyRjaOY8wYg/PP1+nWzWTNGpklS2RmzZIoL4eqKuu77uWXVYYNM/D54JtvJN56S+a11wq45ZYE\njz+u8dRTMitXyqhqTdSh6/Dii7lXJiNoX4YM0fnkE4XOnU3Ky+GPf7RSDgsWRLj8ci+dOpk89VSE\n7t1NPv1U5rnnLGk4+2wfP/wgMXCgweDBOn5/jXdaOtmaJEs2qUxOUZimdXcoyzJLlizh7bffZseO\nHTz88MMMHTqUu+66KyecgXNedJM/6FgsRjgcxuv1kpeX1+STIJstIvPzraW+J55o8tOfRgnvrfWK\nRPzce6+Xu+5SWbTI6sfw7bc1xzliRJxJk3Q8Hqtqwe7eb6+Hz7Q8WCCoj5kzI1RWKnzyicLbb6sc\nc4z1DT51apzTT9e44ALo1Mmqpuna1WTMGCtYWLtW4f33w1RWWkuE//MflX/8wyrHMc2aFENbLYyw\n9+F2u/nnP//JrbfeyrHHHkvXrl1Zu3Ztij/avkzOi65hGITDYeLxOKqqZuz+1ViyNZFmL5CoqjKo\nrq5G13VnhVthocTppxu89ZbBwoVW5cG//y1z+eWWmH70kZvRow0SCYndu2tOZDu/m74Y4uSTdV55\nRUS+grq5447UsoTrr4/x5Zcy+fnWxJgkWf8mN7FJturxeuHNNxVeeknlnHMSlJbKGXO6rU36tVld\nXc0BBxzAyJEjOe6445zn58yZA8Dq1as5+uija60ea6npZEtMKaEDiC5YH4Zn7yxVcwUXsnN7ZEfL\nbneCsrI4LperVtRtNzm3Ofxwk5EjrTKwLl0iXH21xK5dVl73jTfq/3uE4ArqoqjIpKKi9jl9110e\ntm61zqvNm2U0TWLHDonkYgM7d7typcKVV3o45BCD998Ps2GDzL33ulPGa+slwJlMKW1mzpyZ0uJx\nxIgRQM2y3ZKSEsaPH+94nM2cOZPFixczadKkrGxvDDk/kaYoCoFAAFVV290nza5cCAaD5OWBrvsy\nLin2+VIb4vj9lgh7vRCJyMRisHChklFwBwxoWfpD8OMhWXDHjrXuqi6+OM6nn4aYODHBpEmJFFPK\nQw4JcPzxfqZN83DbbR7efFPlwgu9XH99nAULohxwgJlxIq2tSBf3dFPKysrKWh3ELrvsMm6//Xbn\ncUtNJ1tqSgkdQHRt2tMnzW4DWVFRgWma+P1+CgtVpzl0OnbvBRu/3zqZFQX+8x8vI0b4efddiYcf\nTjBsmMGwYQZnnWVNIY8Z0z4nvCB3OffcOP37W1/W9tyA2w1jxmhcfHGcgw4ykCST774LctddUb77\nTmbVKusOauXKEBMmaEn+aLVLydqr2U16pLtnzx5KSkrYtm2b81ynTp2cJuMtNZ3MhikldID0Qnub\nU9rrzyVJIj8/n2g06vikffdd5vekO0f4fFBaKnH77dbHoSgmxcUG27ZJfPmlxJFHmvToYR3XjBka\nhx1msGCBwocfyo6Vj0BQF08+WZMOeOEFlYsuShAMWudhNCohSfbdlsT997v56iuZ4uIEXq9Jmrv5\nPhXpJjtIAPTt25c1a9bQO8nld9myZU6D8ZaaTmbDlBI6gOhC9nrqNmWMZBfg5DaQNZ3GTILBzDcS\ndjrBNOF//4Pf/c76GEaONCgq0rj0Uo1PP3Xz9NMy1dUS778v8f771ljPPqsQCtXMHO/a1aI/WdCB\nKSw0qayUOO44jbIyic8/t5aan3uujy1bZF580cWwYTr/+591bo0a5ee88xLcd1+URx918dVXtc/f\ncLi2/Xp7NDC3r9P0/Q4ZMsT5vaKigmeeecaJUFtqOpkNU0oQ6YUmj2EYBsFgkOrqascrLVMtcH3t\nHVUVDEOipETh2GPdDB5s7fOyy3QKC03GjUtw/fU6r7ySoFs3k6FDrf66AHffrXDDDSqrVlkfXTAo\nolxBZiora7zPTjjBSk/95S9R1q4NMXSozsyZsZROeIsXR7j55vjeUsXMEW172K9XVFRQWVlJZWUl\nVVVVzu+aplFVVVXn+8466yxef/11J/JtqelkNkwpQUS6tcaxJ8PST6K6OpPVNUZdjcw1DR55xHrf\n3LkqZ52lO6KrqlZnJ/vv8Put3G///iY9ehj87W8qt96qcdFFolZX0HhWrFBZscL6fdUqhbIyazXk\nF1/IbNhg5W779jUYMiS1hWh6AxzreYnOndsuvZBsSmkvFbYnzbdt20ZJSQlFRUW1TClnzpzJzJkz\nUyLflppOZsOUEjqI6EL9gtmUMdJpijFl8nFksuxZtkyipETFnmB94IEE338v8cwzlghfeKElpn/5\ni8GoUTJHHmkQiViTH2Vl1lgXXeRi9GiNXbtM1q938ctfJnjqKSHCgtpIkolpWpGpy2VSWWmlFF58\n0Tpf1q5VmDUrzL/+5SU/P1VIw2GJAw6oXSkTDELPnm0X6SabUtotIz0eD+FwmG+++YYHHnig1nsW\nL17MuHHjnCqDtWvXMnTo0BabTmbDlBI6WHohW+PY4h2Px6mqqiIejzfKmDL5/cnphc8+k/jFL1xc\ndZXKTTfpLF2aYMAAg+OOMykpsUwru3UzmTPHKutRVZOHH5Y5/ng3iYTE448rPPaYtd9bbw1y5ZVV\neDzW3xuLifSCIDOmaZ0b4bDEgQdaQtm7t07nzpaYvvlmOV27apimgcejO3dy1nyFiddbO6LdVybS\nKioqMja7Wb58OWVlZQwfPpyKigpKS0tZuHChs72lppMtNaWEDhLpJlcwGIbRIgsPSZLQNI1oNNos\nr7Tk9MK2bRJXXqnywgsyJSU6U6fqTlPzTBUM3bqZDBumUVISwe+XeeUVmYkTU6PYP/85tcHHyy+L\nxRGChtm0yTpPtm1T2LixnDFjCuncWSIeV5Ekmbw8E5fLha7rJBIJqqsNFCVGJBJP6fRVV8lYSxYl\nNYdMCyMqKioYN24cQIoQFhcXO7+31HSypaaU0EFE16Yxjczrw26qHAqF8Pl8eDyeZvVvME2T9esl\nfvhB4qGHFKZP1zjySINYjBTRTV6VZk9ORCISmzYpzJrl4ttvrW3nnRemoEDi/vt9/PvfMVTV4Pzz\nrYRbNCoiXUHjeeKJSrp1M4hGrS/6SMQETDwe0xFPVVWJxVQ6dTJwuUyns5dhGFRXe1DVGLGY5ohx\na1nzpJMs7hUVFbVu9YuKihrVO6U9TSmhg6QXWlqra5om4XDYmQkNBALNNqe0OfFE68N/+OEE4TDM\nmqXSu7ebIUNcXHKJygcfyLz1lkw0ar3e74dduyzBPeOMfE4+OcGKFbvp2VPH7XY53ZYiEZMjjjDo\n21e0dxQ0nsmTrdRVUZF1yUciEpIUIRi0zlPblse+hsJhE7dbd27pbVeIWEyloEBx7ggjkQi6rhOP\nx4nFYk4/3NYQ4uT0Qq720oUOFuk2VXTTJ8kKCwsJ1Wfj24RjyM+3otdzzzU491wAnUQCNm2S+Ogj\niQULFG6+WeWuuxQOPdRk7VqZjz6yLog33thF167g8/nw+62TzDCsvysUMonFIpSW5kZHJcG+waJF\n1qX+9NM+8vJcRKMS++9fQCKhAlYvZ03T0HV9bxohkGLJYzcbD4XA5zMcIZYkiWg0iqIojjVPa3mk\nJV/buWrVAz9S0bUdJcLhMLIsp9j3ZMs9wi73Mgyw010uFwwebDJ4sMlbb+mMH29w5pkG69ZJ/Oxn\nNauGjjmmK4MHmwwbZrB5s0y3bqazjPPxx1X+8peamkCv1xQpBkFGCgpM/vGPOBde6GHYMJ01axQ2\nbZKYMsU61447zseGDdbJuWmTC1nOIxCwxDUalfF6DUeI7WbjwaBVDWGf57bljj2P4nK5cLvdzjWk\n67qTJzYMo5YbRFOFONmqpym1sfsSHUJ0m5JesFeS2T0S0ifJsiW6sozTDzdTc3u705jXa3LUUVFO\nPdVgwgSN3/wmwKef7ubzz/188okKuHjnHYV33rFO6k8/dXH22RoLF1oXixBcQV0cc4yBLFtO0kcd\nZbJmDTz8cJzCQpNevfzs2VPz2jVrFHr18tGnj7UYZ+1alc2bfQwZ4qagwHR8zcJh8Hg0otGE815V\nVVPsdNLzqvY11pAQp1vzpJOcXqiurqZPnz6t8L/W+nQI0bWpTzB13SqJSSQS9U6StYYjcCbR9ftN\nqqsNqqqqkCSJggI/IO3dJjFiRJDhw01ee60zffrobNvm4p13rEqG9J666Xg8piglE6DtNYq2Jsys\n371eeOAB6zy66CKd6dOj3HmnC1U1ufpqjc8+s2ymFixQufFGN9Onw8EHmwwZYjB0qMGOHQqFhR4k\nyZpIc7lcGIbhRLyAI562+0MmIbaF2r5W0s0qMwlxuillp/TGEDlChxDd+iJd61YpSiwWw+v1EggE\n6r2dya4jsFVM3q1b6vO6rqOqOpWVBl6vF5fLtTcqttISuu5BVaN7630lJEnG56s5abdurX+/QnAF\nACtXypx3niW0sZj13OTJHrZts86P666zotVwGPbf33L4HTrUZOhQnRtvNFm9OkKnTlad+YoVCiUl\nVlqisjJCly7eWneJdn27HcnaP0BKBGsLqK6nTgZnEmLDMIjFYo5oh8Nh5s+fz549e9qls1k26BDV\nCzbJgmmaJtFolMrKSkzTpLCwEJ/P1+AHlU1H4EDATGnvaE1EhKiqqiIQkNA0D6qq7nWWsArPPR6T\nsrIobrebvLw88vJkZFll6dIaz+uqKpmJE62r6Kqrqnnttd3NPl5BxyUSkbj0Ug8//CA5nmcHH2zw\n/AWtL7YAACAASURBVPMxjjjCSHpd7SY24bAVNLhcUFoqc++9KlOnhti6dReHHJLZe9AWVJfL5QQ4\n9qIi+/W6rhOLxYjH406Vg72MH6yAxBZbsMTa4/Hg3duTMpFI8M033/DBBx9wyimn0Ldv3yYvTmhv\nOkSkayPLMvF4nHg8TjgcRlGUlEmyxpAN0bXJy7Mi3eQqCbfbTUFBAQUFCl9/XXPb5XZrVFRo+P0q\nspzn1PM+/7yCrteMuWJFlMJCk48/lnn2Wbjnnnz+/vea/EWXLgYLFuxhzRoP116775v0CVqPww83\nGDdO5+9/r1lg89VXMieeaAnYlVe6GTrU4L33FAYN0pzXmKYlxNXVMHWqmw0bJObPL+f441VcrqZV\nzSSbTCbbridHxPZkHaRGxPa1ZKcewCrnnD17NmeddRbvv/8+u3fvZvv27c3/T2oHOoToJn84du1g\nIBBolOV6prGy1TjH74fKSo3KymqnSsJeNef1GoRCirP6zevNJxbz4PdLRCISu3eb3HqrK0VwwbpQ\nrroqwYgRBmecoTFpks6pp+p07myFKnv2yEyb1oUvv0y9ifnrXyu49dYCyspkjj5aY/XqDvHRC+ph\n0yaZTZus8+D778P07u1j9eoor7wic/75HgYNMvjwQ5nPPpP5/e/dPPywytChBocdZmCaEqNGeTn7\n7Aj/+EeEoiJv1ladZRJiqBHX5B87EjZNk48++oj999+fdevWsXHjRnw+H4ceeiiHHnqoM0Z9/miL\nFi2itLSU4uJiOnXqxPz585k8eXLKhFxr+6NBBxFd2yInkUjsnZQqaFHTm2yIrqZpeDwy5eVx/H4/\nqqo6t02SJOH3G1RVWU08LKcJF19/LeFywV//6uL11xWKizW++SbMrl0Sp5/uoaJCont3k6VLFW6/\n3cXWrTLPP69y1VU1M8m9exuYJgwfrvPxxzVLhP/4x5rVO16vsPz5sXDbbTGWLFGdFWhg9WI+/niD\nyy+3ots9eyTOOUejVy+Tl19WmDnTus167LEyjj3Whcvlr2v4rGJPnNlCrGmaY7muqirPPfccr732\nGrt27eLoo4/mz3/+MzfccIMzodaQP1pZWZnTfayoqIiHHnooRXDbwh8NOkhOV5IkPB6PE0m2JMHe\nUtE1DMNZSpyfL2EYfhRFSZk0iEQiyHKMWKzG383vN1myRGHdOpmnn1YZNMiy6dm1S8LrtWp9R4/W\nufRSjUcfjbNhQ5QzztCYMEFL6RC1bZtMaanEz39es7+jj9ZZsiTKqFH2LVqH+NgFjeDaaz2sXKnw\nhz8olJVJbN5s7HWNqDlnwmHo3Nnk888lHnlE4eqrg3z77W7+7/98zbpbbCl2G9VwOIzf7ycQCLB8\n+XLWr1/PI488wo4dO7jpppvo3r07/r3J6Mb4o0mS5DTBKSsrq9UzoS380aCDRLoAHo8HTdPa3Sct\nsrc2JxAIkJcnEwzWlNLYEwhut5v99vMRiVi5q82bJaZNsybK5s6N07evwaefyrz6qsItt7j4+mtL\nJL/5RkaW4eijDQ480OSAA0z22890mlUDrF8fYcOGmtVtAKtXK5xzjuxM6r32Wk0E/NxzUc4809vk\nv1ewb/PTn+p89JHMrbfGKSlxO95op53mY/t26/MvKUkwdKjBW28pfPSRTN++Ov/5TxkjRrRddJuO\nnR6052OqqqqYMWMGsiyzdOlSJ6odM2YMY8aMcd5n+6MVFxc7TcuT/dFsrPmU2nMdbeWPBh1IdCF7\nPXWbupTYXt1mnyihUIhYLIbP5yYYxMnbqqpKXl4esizj98POnRIzZ7p46imVY4/VKSiASy6xbvns\n3g0AW7dKDBpk3Ru+8ILKe+8pyDL88IP1Nx54oPXa44/X6dvXpG9fnV/8QmfrVolnn1Xp1Mlk2DCd\nFSvUvcdc83+zdKl1AfboYbB9u4iAOwqbNslEoxJdusDpp+v87ncG69YZfPhhjDvuUPj3v13k55tc\ncok1MXbggRovvbQHv9/lTHK1dNluU7Crjew6elVVefPNN7nxxhu59tprOeOMM+o9lob80Wzmz5/v\nrGQrLS1NST20hT8adEDRzcYYLfFJ03Udt9uNpmm43QnKy61bJVVVnY73mmZNktkTHXfeGUdR4KWX\nMrdpPOggE0UxufRSjQEDTIYPNygutiLjvDyTfv1MvvsO3n5b4aKL3IwYYTB8uIFdj15eLjmCe+21\nMWbM0CkqsiIZe6GFENyOxZ49dtN76zwpKIAvv5RIJCAQkBg0yOTdd90MG5bg7rsrGTLEBficlWLR\nqFUnrihKyk9rCLEd3cqyTF5eHpFIhJKSEvbs2cPLL79M165dGzVOff5oYEXHyTncyy+/nPnz5zNl\nypQ280eDDiS66QskWnpi1DeGYRhEIhHi8Tg+n89Za65pGpIkoaoqmqbtXXXmwu/3O5UVr79ucP31\n+ZimyfnnR+nf3+Tdd10sWWJ9FBMmeBgxwmDECJ1hwwy6dbNsfCTJupAefNDFAQcY3H57nHPO0Z2+\nDoGAJaKjR+usWSPzn/+4WLs2VcSPOUanUydrss5m5swEb7whevJ2NMaM0XnrLZnJk3WeekplxQqZ\neFyiWzefs3hm1qwqfvObBIFATf16XdUErSHEdkouHo/j9XpRVZVVq1ZxzTXX8Pvf/55zzz3X8UOr\ni7qa3qT7owG1lg2PHTuWkpISpkyZ0mb+aNCBRNcmG3W2dQm3fQsUjUadelsgZZIsOW/bubOb3but\n2dht2yT+/GcXa9fK3HJLjNNOi2MYNaUxV1yhctFFnbj44ghr17q47z4Xa9bI5OebHHmkgaZJTqeo\ntWuj5OfXPvaiIpMLLtA59dQEN92ksHZtqsnVqlUKq1YpPPtsjciOH28l+04/XeO//+1wp8OPluXL\nrc9482aJsWN1LrsswZ/+5CY/32T9eoWSkiBXXw2qmsEIbS/p1QSQPSG2+jiEneg2Ho8za9YsNm/e\nzHPPPUePHj1YvHgxy5Ytq/fvbKw/WkVFheNxZl+3hYWFlJaWAi33T2sKHeYqy6Z7BKS2kcuUt7X3\nY5MpbxsIWHnXG2908fDDKldemeChh+J7S3dce3+s8bt0gcJCGD8+xpgx4b1CLvPQQwFuuCG1ecOJ\nJ3r3RsMGw4frDBpkHWtFhcS8eSa33ebj1FMTnHdegoEDTb79VqJ3b5OdOyVkGSor4YMPUv9/hOB2\nLBTFRNclp2xw2TLb7qmKFSsS+P1eJKnpn3lLhRhIiW5dLheffvopf/zjH7n44ouZM2eO87pJkyY1\nuRyrLn80SZKYMWNGikCWlpbSr18/oO380aADia5NS90jIDVaztSVzF6qaC9rtE8yux7X5rHHVEfc\n/vOfGOPG6Xg8mfeXl2ctivDtLab8/HMoKXHz9dcSCxdW8rOfRXnwQS9b/7+9Lw9r6kzbv8/JCiKL\n2mqrVmXRilaUJCyO1U6LVL/ONVorWDvVr60i+Gur4gbOaKvtpyi4odUKaKed1roAOtqxdYla7QIi\n4NLWjlaCuAHKkhACWc/5/XE8xySEPWHNfV1cmrzJWZKT5zzv8973/RQKMGuWHpcvC3HxogC7dolQ\nVPQ4o4iN7YHPP6/F9Ok01qzhQadjNPU6HbMg9+9/8y0koOXlNejdu31Wqp1wHKxFNQMGmLBggQbR\n0Q1nty1BcwIxwFzvZ86cwbBhw3D48GFcuHABe/fuhbe3d6uOg+2PFhYWxtVgDxw4gDFjxsDDw6MO\npSwjIwMbNmzgHrP9z9hAX19/tPrGmwqCbqteGw4G21KkuroaAoEAIlvRrYlQqVQQi8UwGo116rZs\nsGVLDUajkbtjW0+nzpwhER8vRFAQhdxcEgUFBPz9KchkzEKXVErBx4d+xEQAZDIXXL1ai4QEhtGw\nZIkBMTFGThK8ezcP+fkEtmzRcPLJkhICf/+7B777jikTTJliRH4+CbWagFLJHM+gQRRHO+vZk8bd\nu7WYOlWEs2d5KC6uwVNPPQ66ISEmZGc7a7xdDWVlFRCLm99+yh5gm7xqtVouaZk9ezYuXbqE6upq\nhISEIDg4GOvWrbM4PqVSibS0NHh6eqKgoAAA6pQSWIXY3bt3ERsbW2ffzz33HD744ANUVFSgpqYG\ner2e215QUBAKCgosFGZKpRLe3t5QKBTw8vLC3Llzbe6vvvGmoMtluq2t6bKUM41GA7FY3Gjdli01\n2MKLL1LIydFyjzUa4PJlhkP73Xc8fPyxACoVgcBACsOGUSgrI9C/vwtmzzYhJ6e2jjuZiwug05EQ\niUTQ6Wjs2EEgOVmMN9/UYuJEPV57TYspU5jGa2VlfMyd64kLF/hcwAUAtZpAcjIf+fnMcxIJE6zn\nzTMgNVWAkpLO6dzkhG3k5T2En58YPF77cLEpikLNo2aAbo88Tnfs2AGtVotz586hd+/eyMvLg0Kh\nqPM7SkhIsMhEpVIpxzYA6irEiouLIZPJGlSQNWfcFlrbHw3ogpluTU0NCOLxNL2pMK/b0jQNsVgM\noVBos27L4/EgFotbXTcGmAw3L4/E2bM87NjBTM8GDqS4mq1USmH0aApubsChQzxkZPAwa5YWcXFi\nDB5MISnJgGHDCERHCzF2rAmzZzNijPx84IUXmNW2Y8fKEBhowqpV7jh0SITnnqNw7pzl/bZ3b5qj\nGTnR+eHiQuHOHWW7ZrdsWUEkEkEoFKKwsBALFizAiy++iLi4uEbVbr6+voiPj+eyycjISADAwYMH\nATCLX+Y0rtOnT2PDhg04efKkXcYdhS6T6bamOaV13Vav13P0L7bbaW1trc26bWvx5JPA5MkUJk+m\nkJhoAE0DBQUEcnOZjPjIEQF+/ZXEkCE0fvuNyU6PHHHD3r01mDLl8XmLxYxxeUUFgY8+EuPoUT4C\nA00YNYrGhAkuoCgKAwYAFRUkfv0VGD9eBx4PWLmyFi+95OkMuF0I27cr8frrRvB4fIs6aluBpVQy\n9qaMf/WePXuwf/9+7NixA2PGjGnSduRyuQXlq6CgADNnzgTQegWZPRVmzUWXY8Q3J+hSFIXq6mqo\n1WquVMCutLKBVq1Wo7q6mvN3sJfTUv3HD/j60nj9dRM2bjTg7Fkd7tzRYOvWKkycyJQqBg+mMG+e\nC8LDRYiPFyAzk4f79wns2MGHROICPh/Iz6/FvHlG6PWMAm3fPiFWr2ay/7w8LRYupHHrlgDr1jGK\npI8+qnLoeXU0JCUpG39RJ0RxcTlmzWIcvNg1DrVaDY1Gwym+WH8Qe4Ot3VZXV4PHY3xFSkpKEBER\ngeLiYpw9e7bJAReARcDNz88HSZJYunQpgNYryBobdyS6ZaZrzrcViUTw8PCwcLJnHcFMJhMEAgH3\n2GAwoLa2lhNAmNNhHJFJsBexXq/DmDECHDpkBEky9TGVCsjPZ7Lh9HQevv2W+Sr796fQpw+N3FzG\na+HiRRITJ4pgMADvvWdASQmBHj2ADRsYl7I5c4y4coVGcbF9V7Q7Gt55x4DPPns8nV22zJL+M3Gi\nFqdOdV4Pir//vRrx8TR4POZ7rI9JwJqHA6iX0tUSsNktRVFcdrtv3z6kpaVhy5YtCA0NbdFvRKVS\n4eDBg0hPT0dqair3fGsVZPZUmDUXXSbosrDmz5qDDWLmhhos7YsF20uNvVNb121ZBoM5HYbVqZsH\n4tbKJVlpJEEQNo/Dw4PxZ2A9Gmpr9SgvJ5CXxwTizZsFOH+eec8ffwAbNuhRXMwILHJySBiNDKvh\n+ecprFxJYP/+LncpWOCzzwTYs0eHOXMYVsuzz1L4y19M2LiRCU62Au7x4w8xaVLTJKi28MwzFG7f\ndvxk8v79Sri7C0EQtvdlTemyNhFvbSBmkxGhUAhXV1c8fPgQixcvxoABA3D27FnOCawl8PDwQFRU\nFKKioiCRSBATE2MXBZk9FWbNRZcqL5j3X7KG0WiEWq2GVqvl7OIAWFDAampqLMZtLZSxdV6hUAgX\nFxe4ubnB3d0dLi4uIEmSqw9XVVWhurraYkrXFLCrvTU1NRCJRPUehzVcXIABA2hMmWLCxx8b8N13\nOty/X4OMDC0+/VSHP/4gsHUr86O7fZuEUAgUFZGYMIEJNhMnmhrafKeBWMx8997elp/3K68YuYAL\nAJMnmyCTWb7Gz4/C4sWPvYn/+tc+3P9DQgzYtu2xHHXPHtvS1MWLDeDxmGMICHCsb/HmzVWoqlI/\nahTZvDbmDbXVYeW5arUaVVVVnIGTtYsfe62yvxmRSISjR48iIiIC7777LpKTk1sVcK0VYDExMRwv\ntrUKMnsqzJqLLpfeWJcX2AvDYDBw4gb2Ls9eqLW1tY9Mx0U2ez81ZZ+soY35fm1lEvWVJdgsXKfT\nNUpFayo8PJhFOgCYPduErVsNUKuBa9dI7NrFx61bJMdaOHiwa1wKAgGg1TJ9vVj88IMW770nxAsv\nmPD998wNjKKAGTMsudx//EGiqsqEp56iUFxMomdPoLwc+NOfTAgP1+P0aSH32n/+s24w2blTjStX\nhJww4caNx8cQHGzChQuWN89Bg5jvxpzS11SUllaiR4/mX6v1ob62OvXN6tgZokqlgoeHB9RqNZYt\nWwaxWAy5XF6vJ0JTIZfLER4ebiHbZX/XVVVVrVaQ2VNh1lx0GcoYwExzjEYjVCoVPD09Leq2YrG4\nTitog8EAnU7H3fEducJrfQGzf+zUj/0/a/zRlvj+exIaDYHIyJYLSjo6PvlEh7feMsHNjQmWQ4Yw\nApUNG/R4/30hKiqIOqKQ0FATLlwgMWmSDikpGnh5ieHmxsyQXFxo1NZaXi9hYQaMHm3AqVMCjBhh\nwJw5Wrz0Ut2pao8eNDQaAsuWGVBTA44qyAb7hvDvf1fipZcEDl/QrQ9s7dZoNILP5yMlJQXr1q2D\nWCzG6NGjMWXKFPzP//wP/Pz86ry3oVY67DgrVKipqcF///tffPrpp9z4qFGj0LNnT7z11lsAGDaD\nOa/2z3/+M6RSKYYOHWpzPD4+HkFBQZx5uTUv13rcUeiSQVepVHI1VjaYmgdbVrrLBjl78G1bApb3\nS1EUVxZhfSOs68OOhLnbEyO8EGL/fgFiY4WNv7mdMHgwhVu3mM9l2zY9TpwgcewYHytX6vHwIePG\nxmLQIAoPHhDo1YvmLCzXrNFj0SIj+HxgyRIB+vWjsXo1c75RUQZ88QUfSUlqLFzIZFmurjS8vWn8\n+ivz/ldeMeKf/9SjRw8gKYmP1auF2LdPh7w8kqsTm3sUy2R67NypxMOHAqxY0RNXrvAxdCiFu3cJ\nzlx++XIDEhMFEApp6PWWAV0q1ePUqRqbyse2Als64/P5cHFxQXV1Nf7xj39Ao9EgKioKCoUCubm5\nmDRpEqZOnWrxXlutdGbMmMEFXltCBXPZbmZmJry9vfH1119z4zKZDAqFAt7e3ti+fTskEgk2bdpk\nc9yRCrPmoksFXZbiZTKZ4ObmxrEOWMcwc/4gK91tD5gHOaFQCJHocU3OVjYMoE4gttcPjyWwkyTJ\n1aWtQVFAaSmBuXOF3PS8I2HoUAo3bpDo14/Cjz9q4ev7eOqv0dSApoHPP+fhvfdEFu+5d4/AqFGU\nhfnPrl06eHvrEBcnhlxejb//vSd8fWmMH2/CCy+Iuex2+HAKt28TGDmS4soGly/XwseHxocfCh55\nGJPcQlpoqAlXr5J4+mkKf/zBvP6bb8oxaJAJM2f2QlkZiZAQE44csbwmPT0p7N2rwvjx7Zfd2jIY\n/+mnn7By5UosXrwYM2bMaPB6VKlUSE1Ntchs09LSEBcXZ7F41RmFDi1B1yjkPQLrg6DRaLiaE3sx\nsBdNS+u29oC5SsfcjcwctlaaHcGWMKf4NHYDIkngqadoHDumMzsXICuLxKuvilBd3b7Cijt3CMyd\na8Du3QKEhDCUqTffNOKHH5h+cQsWCFFeTuCHH7R4/nkxpFITzp3T4eZNAmFhlqyFmBgR3NwEqK4m\nceZMD9y5Q+A//+Fh/XoB1qwx4MEDAi4uNOLjjaiqYmTdr7xCgqII/PWvTPPQqirm8wgONuHllw3o\n0QNYu9YAoxH4/XcC8+aJcPUqiZUrPXHjBskF8uefr8b58zxUVjLXxIQJOmRmVrcJP7w+WLfP0Wq1\n+OCDD1BUVIQjR47gqaeeanQbjbXS6cxCh5agS7EXRCIRt6DFEsI1Gg3UajWnjjHPKtsSJpOJWwV2\ndXWFq6trk35I9mZLsFkLS2B3c3NrYat6YOxYCqWltdBoaqDR1KCysgbbtumbva2mYMaMx9vds6cS\nAwaYEBjIMA2ef96I3buZcygrY77br75iPCcCAsQICzPhhx+0CAxkPhOSBP71Lx7CwsT429+MePBA\ng/LyCkyYoMP27RrMncu0TIqOFuLbb/m4d4/EM89Q0GqBc+eY+jfAdGMw76p76JAOffowE8dRoyh4\negJpaQJs3SpAZKQQmzbxUVpKYPJkE95914Cff9bhzp1a+PtT8PensHJlTy7gKhSlyMhgZm1qtRpq\ntRo1NTU2WQSOgHlzSLFYDFdXV+Tn5+OVV15BQEAADh061KSACzTeSqczCx1agi6V6cbExKC4uBiB\ngYFwc3PDL7/8goSEBLi6usJoNMJgMNRhDzg6g6AoCjqdzq5ZdnPYEuZlCTbgssbR9j53oZDp8cb2\neQOAhw8ZIcann7aslNO3L43SUgKlpTx8+aUOs2aJsHatBz75RIvgYD2eesoT58/zsWGDCrNmaVFa\nKsCYMY9/gBRF4LPP+LhyhYRUygTdnBwe9HoC//63FiNHGh41EyVBkjw88wyN/v0pbN0K8HhAWpoO\nMhmF/HwSeXkkLl7k4eJFHo4d40EiYRbj2JLLpElirFplwDvvGLmOHmvXClBUxHRnzssjsWnTY/50\naSkBqZRCUREBjYaAn58RW7ao8Kc/8SAQuFkwW8zLTXq9nqv9O6KVjnX7HKPRiI8//hj5+fnYv3+/\nRfBsKhpqpdOZhQ4tQZcKunv27MHPP/+M999/H3fv3sX48ePx+uuvw8/PDzKZDCEhIZxpsU6n46bp\n7EXL5/PtduGaU8AEAoFDgpw5GipLGI1G6HQ6rrZNEASMRqPDel6Z44kngI0bDdi4kclKdToDrl/X\nY/58T1y+3HAgFouZgAsAU6eaMGsWU5PNydHiwgUS48YxP6asLC38/PhQqVyRksIshj33nAHHj5eB\nIHgoKBAiN1eI2NjHtV6jkUZqKoGAABohIa4YMYIpEZw/z8OmTcxxXbxYC7Y9l5+fCTNmmEAQwJNP\n0ggLY9oipaYKcO0a872SJPDrrwT27mUC8rBhNAwGwMeHwrRpJkybxtwIExL4uHqVxMSJJixfLuQy\n5zNnlPD0FNe5TmzdZM0DMfv9WgdiPp/Pfd9NgXX7HIFAgGvXriE2NhYzZszA2rVr7XINW7fS6cxC\nh5agSwVdgiBQXV2Nt956C/Pnz+e8O69fv46srCykpqbi2rVrEIlECAwMhEwmQ1BQEDw9PW1mEGyG\n2NwLrTE1WVuA5VwajcZHTTKFnGuarR+qo9kS5jVkf38X/PSTAYDh0RigUBB4910hfvzx8Wel1T4O\nFosWMeyAN94QYuFCIc6fJ7Flix5vvy1Cv340jh/nITbWBRMmUHjzTSOGD6fh4eH+6HsFdu8WYsIE\nHfr2NaF3bxqTJ9fi6lURsrNdsXMnDzdvMud87hwPAwZQEAqBPn1QByYTYwrv50fjwAESJSUM9evg\nQR727NEjN5fE6dM8JCYK8PAhAbWaOQc/PxpSKYWBA5mODr16AYcP8zBggAkHDigREtK8tueNzXbY\ntQOgaSoz6/Y5FEVh69atkMvl2LNnD4YNG9bkY2sItlrpdGahQ0vQpdgLTQFN06iurkZubi6ysrJw\n4cIFlJaW4plnnoFUKkVwcDBGjBjBcWebwx6gKKpRY/O2gvkUsSFanKPZEtaij6bW1HU6Zvr96ad8\nbNsmwNixJvz88+NzWLNGjz//mcL48UzNtrCQwLZterzwAoX4eIYCFhVlxEcfCXDwIB/r1ukRGWnA\nRx8RoCgaK1ZoHzUTNeGbb8RYsMADOh2BWbP0SE8XQKsl8MQTNNcSiS0lrF0rQGkpgV9+ITFmDIWN\nGxl5dVSUCBcuaC3Oobyc6UFXUkIgNNSE3FweKOpx3Xnp0mrExmrh7l43u7UXGvp+2T+29MZeszdv\n3sSiRYvw8ssvY+nSpXbjjWdmZsLLy6tOKx2gLvtALpcjKSkJJ06csMt4R0K3C7q2QFEUioqKkJWV\nhezsbFy5cgU0TWPUqFGQSqUICQlB3759LS5gc/YAm1HaooC1x7mwgZ+l9zTnWBoScVhn/41tt6mB\nv6nQ6YCUFD7c3Wnk5vKQnc20sAeA//1fI8aNY6S9aWl8/P4706kjNJRCQoIOnp5M5rd5swf4fB5W\nrjTi3j0CsbECFBQQSE6ugUTC1MLlcj7S0npg61YNLl8W4vJlAfLz+VwtFgDGjzfhgw8MCAigcP06\ngffeE+Gnn7R1jnnJEgF8fGj8v/9nRFERgalTRbhxg0RUlAZJScY2py1al50MBma28eOPP2L//v1w\ndXXFlStXkJaWhuDg4Hq3k5GRgdzc3DqdHABLkQMAREVFQS6Xo7CwEJGRkUhOTkafPn3wzTffYNq0\naYiKirIQKiQlJeHnn39Gv379EBgYWGcc6DhCh5bAGXRtgK1tXbp0CdnZ2cjOzkZRURH69OkDmUyG\n4OBgjB49GkKhEPfv30evXr04lgEblJob7OxxzC3JKJu6besg3FAZxpzX6eiM//ZtAnfuMP7DrNkP\nK6uVSExYvlwHf38NnnyShouLC9atE4MkmQW6jz8WIDragCVLjBa96777jkRKCh8HD1bDaDTCZDLh\nxAkB4uI8QFEEZs7UQ6MhkZ/Pw++/P6Z87dihg0RCYfhwGmxyuGCBgGsc+vHHfMTEaPD++zr07OnS\nbjdm82tFJBJBIBDg8uXL2LRpE8rKylBbW4tr165h/vz5nNiAxenTp5Gfn49Tp07Bx8fHQjEG/RTB\nOQAAGDlJREFU2BY5+Pv7cyoy8y7bERERGDJkiEWw/emnn9C3b1/IZDLMnTu3TjDuaEKHlsAZdJsI\nmqZRWlrKBeHz58/j1q1bEAgEWLZsGcaOHYshQ4Y8mrIaHbpIZw3zGrKLi0ub1JDrm7ayIhRWtdQe\n/NKTJ0kUFhK4f5/pTXf5sgA9ewJSKYXDh5loOGIEhc8/18Hfv+7l/+23PHz2GR8ZGTo8fAgsWyZE\nXh6J5ORa/OlPei5LZL5rPr74ogdWrXLFzJkG5OUx3sYBAUw5Yts2JpOVSg3YtEmJgABhu4lyAMv2\nOS4uTODfu3cvPv/8c2zdupXLbnU6HVQqFZ588kmb24mPj4dSqcSuXbssnu9OIoeWokstpDkSBEGg\nX79+mDp1KgYOHIjdu3djyZIlCAsLQ15eHrZt24YbN26gR48ekEgkCAoKglQqRc+ePe26SGeO9qwh\nW7MlzCXNfD7TsUCtVreoLNFavPiinitrMIHFBIWC8RZmg25BAYG33hI9aolkglRKwd+fyVBNJoAk\naRw4wEN8vBAzZxqxc6cWrq4EgMcpMXvjCQgwIjRUj82bmWBRXc3H5csixMT04F577FgVevRwbdfs\n1rp9TmlpKWJjY+Ht7Y0zZ85YtLgSiUT1Btz60N1EDi2FM+i2AGPGjMEvv/zCkcNlMhliYmJA0zRU\nKhVycnKQlZWF3bt3o6KigptChYSEYNiwYSAIoknOY/XBmo5mD0eylsLat8Gch9wYrckeNx7rY6mv\nrOHjQ8PHx4TXX2eyPL0e+O03Ahcv8pCdzfSnu3OHyVAZv2GGPnbsGFMysAX2xkOSJIRCRrFFURRu\n36aRmCiGn58RR48q4e1NgST50Ov1DjW9rw/W7XNIksThw4exbds2JCYmYsKECXY5HkeLHDoiE6El\ncAbdFoAkSZtqHIIg4OnpifDwcISHhwNgLviCggJkZWXhq6++wi+//AIej4eAgACuPtynTx+uM4XJ\nZGqQ9M5mlADajY7GwppE31R+KVt+qU/E0ZKgxBpp1yevtoZQCIwZQ2PMGCPmzWOeY7txLFwoREEB\nAZEImDbNMhuWSChYUz+NRjzKkAls3SpCcjIfy5apMWeOES4urtzNh11sdaSwwRzm2S1b56+srMSS\nJUvg4eEBuVxu10DW3UQOLYUz6DoYJEnCz88Pfn5+mD17NmeWnpeXh+zsbKxYsQL37t1Dv379ON7w\nqFGjQBCEBdeSDSImk4nrVNwZGRIEQUAgENTpYsAGYnNviaaUJayPpTX1UrYbx9Wrj1kI9+8zZQm2\nG8elSySefJKGREJBJmO6NWs0BP77XwIvvCCCu7sJx4+XY9gwEfj8x9N1VsrNnrP5DECrZehr9lRL\nWrfPIUkSJ06cQEJCAtasWYPJkyfb/frpbiKHlsIZdNsYrGBi/PjxGD9+PADmR3j37l1kZ2fju+++\nw9q1a6HX6zFy5EgEBgZCo9FAr9fj7bffBo/Hg1ar5aaqbVkrNc+c7FXWYBVTbEBi99NYWYKl6Tm6\nxPL000w3jilTmIzcZAJu3GACcV4eib17hbh8mQmOixerMHs2k902dCwtlXE3dQZg3T5HrVZjxYoV\nMBgMOHHihMMCWHcTObQUzqDbAUAQBAYOHIiBAwciIiICAKDX65Geno6VK1fCaDRi5MiROHfuHCQS\nCYKDgyGRSCAUCttMWcb2jgMcX9ZorCzBSrgBcIGILcs4+sbD4wHDh9MYPtyE2bNNoCgdFAodcnN5\nmD6dtMhum4PmuMtZlybMbUG1Wi1MJhNcXV3B4/Hwww8/YNWqVVi+fDmmT5/u0M+nM3dzaEt06KCr\nVCqRlpYGT09PFBQUAEAdMrYtIrY9x9sLQqEQ169fxz/+8Q+88847IAgC5eXluHDhArKysvDJJ5+g\nqqqK85UIDg6Gr68vALRqkc4aDS2UtSXYQExRFCiK4mTNbFBig01bsSXMs/4BA4Tw8bEvc4TlfZvf\n3KyFDeb1YdZPQ6/Xw8vLC3q9HqtXr8b9+/fxn//8B3379rXbsbHHYgvz5s1DZmYmJ1KQy+VcXzN7\njHcFdGieblxcHDZs2MA9lkqliI6O5gKjLSK2uSqlteMdHea+EtnZ2fX6SlAUBaPR2GxDlKYYnLcV\nzDPt+rjIbFBiM+L6RBzNMYGxBfN6KZtRthdY3i2b6f/f//0f/vWvf3HUxbfffhvjxo3DE0+0vKux\nORYtWoSKigocP34cNTU1+PDDDxEWFsbJeYHGRQpdReTQUnTooOvr64v4+HjuQ4+MjAQAHDx4EICT\niG2N+nwlBg4cyAXhkSNH2vSVsGUBaY/FKXuck7XzVXNlzeZBuL5zbso2bbEB2msxE6jbPkev1yMh\nIQHXr1/H1KlTcevWLeTk5GD69OmYM2dOq/fX2ZOUjoIOHXRv3bpl4d0pkUgwc+ZMLF26FPn5+QgL\nC7MImvn5+ZBKpaAoqtXjXQUN+UpIJBKEhISgX79+Fhki61AmFAodqqRrDOZdC8Ri+5jCWLMlrL0l\n6lMPmnNd20r119A5mLfPEQgEuHr1KhYvXoy//e1vmD9/vkNmJa1NUhITE5GbmwuFQgEAWLFiRbcM\n2B26pmsecPPz80GSJJYuXQrAScRuKkiSxJAhQzBkyBC88cYbdXwlVq9ejaKiIgiFQpSXl2PUqFHY\nvHkzVy81rxu2VbNMtmuBIzLt+tgS5jcday4tm+GKRKIOkd2at88xGo1ISkrC+fPn8cUXX9jswmsP\ntFYtFh0dDZlMxs1SMzMzERERgfT09G4XeDt00AWYpnYHDx5Eeno6UlNTueedROyWgSAIiMVihIaG\nIjQ0FACwZs0abN++HTNnzoSrqytmzZqFmpoaPPvss9wiHesrodfrYTQauYWt1izSWYPNQGtra9tU\naVffohWr+mNNWtgFyuaWJewBW9nt9evXsWjRIvzlL3/ByZMnHZp9tzZJSUtLQ2JiIvf4tddeg4eH\nBxISEpxB19FQKpUNXqgeHh51HkdFRSEqKgoSiQQxMTGIioqyOxE7KSkJ5eXloGkaX375JVatWlVn\nvCuyJABg7NixiImJsVjhNhqN+O2335CVlVXHV0Imk0Emk0EkEoGiKJsqq+Z2LbBenLKXh2tLYO3C\nZS5qYLNha7aEI02NrJV/NE1j586dOHLkCD799FOMHDnSrvuzhdYkKSzXViKR4ObNm9zzKpUKhYWF\nDjjajo02vbIzMzNx6tSpBl/j6enJ0cKUSqVFcIyJieHYC/YkYsfHx2P9+vXIz89HYmIijhw5ArFY\nzLWMtrWAYE5rae14e8MWD5LP5yMgIAABAQE2fSX27Nlj4SsRHByMZ599FiRJ2uxaUF9maG1J6era\nfqYwQMN85OaWJVpy8zGHrUXEoqIiLFiwAOPGjcOZM2fabJGzviSGpmkMGTIEBEEgIiIC69evx/Tp\n03Hp0iVIJBJcvHgRnp6eCAsLQ2VlJfdetq4rlUrb5Pg7FOgOilOnTtEEQdAqlYp7LiUlxeI5Ly+v\nOu8JDw/nHjdlXKlU0omJiRbjqampFu+13o5cLqcnTpxot/HOCpPJRN+4cYP+4osv6Pnz59Pjxo2j\nJ0yYQC9YsID+8ssv6Rs3btCVlZV0WVkZXVpaSt+/f58uKSmhHz58SFdWVtJKpZIuKSmhS0tL6aqq\nKlqj0bTbX3V1NV1RUUEXFxfTFRUVdHV1dYu3o1Kp6PLycvrBgwd0cXExXVxcTD948IAuLy+nVSpV\nk7ZdVVVFl5SU0A8ePKDVajWtVqvpHTt20KGhoXROTk6bf9d5eXk0QRA2n0tMTKQJgqDT0tJomqbp\nDRs20DExMQ1uLywsjCZJkj59+rTDjrmjosPWdGUyGaKjoy2mLadOnUJERAT3nD2I2Hv37kVcXBwi\nIiK4cYqiuCzYaVdXPxrzlYiPj8f9+/fRr18/SKVSBAUFISAgADRNo6CgAE8//TQAJhtm28Y7epHO\nFtjsliCIVjcQtVbT0Y/YEuY+Cw2VJWgb2W1JSQkWLlyI4cOH48yZMxCLxfY69SajIbXYsmXLcOrU\nKcybNw+enp6Qy+UNMhpSU1Nx+vRpZGRkcLO/7oQOTRm7dOkSF5zKy8tBEAQSEhIsXmMPIjZ7sbPj\n0dHRuHXrFk6cOAG5XI6YmBiLWpRCoYCvry+USiVycnKaNZ6RkQG5XI7U1FQolUqLm0pXrBvTZr4S\n2dnZOHv2LO7cuQM/Pz/MnTsXEokEgwYNspimNyR1tfextYYD3Jr92hJxkCTJBeiKigoMHjwYhw4d\nws6dO7Fx40aMGzeuXUsvDbXEUalUGDx4MFQqVZ3r2hwsLTMjI6NDttJpC3TYTBdgfGvNlS62wNZd\n7TWuVCqRnp7OZaj2YkmYtzkZMGAAN85enF21bmzuK8Hj8bBv3z5s2bIFQ4cORU5ODpKSklBQUAAP\nDw8uG5ZKpTYpa/aok7Kw7n7bltm1NVuCDf46nQ58Ph/FxcWYNGkSDAYD3N3dMXv2bK5G3lw0t5dZ\nQ+Pr169HUlISMjMzoVAocPPmTchkMqSlpQFgZqdyuRzr1q2zuT+FQoGwsDDI5XLuOk1MTMTy5ctb\ndG6dFR06020NmsuSYBEeHo7ExESuRbRcLkdkZKRFYLXOZJszHh8fj9u3b2P//v0WGUF3UNdVV1dD\nr9fXoR7RNG3hK3Hx4kXOV4Lt0Dx06FAL9zGgZQ5c7ZXd1gfr9jkkSeLYsWNITEzE4sWLIRAIkJOT\nA4VCgczMzCZvtyW9zFojoQ8JCcGYMWNA0zRSU1ORl5dnkTAplUpIpVKkpqZalBR8fX0tZondAV0y\n6DaXJcEiPj4e4eHhFheFLZWatbKtOePx8fG4efMmDh06xD3nVNfVRVN8Jby8vOqoyqwFHOYB1Zx6\n1d5eErSN9jlVVVWIi4sDACQnJ8PLy6vV+2mLXmYZGRlISEhA7969cfLkSXh5eaF3797Iy8vjkpuI\niAgUFhZCIpFw21QoFCgsLOx2QbdDlxdaitdee63Z0+rMzEyLgHvp0iWMGTPGIXZ1Wq3WgqblVNfV\nBY/Hg7+/P/z9/TFnzhyu51pubi6ys7Px9ddfo6SkBM8884yFrwTrtmVuDE6SJFdDZYUF7Z3dWrfP\n+f7777F69WqsWLECr776qkOPz56Lw9HR0UhLS0P//v1x6dIlZGRkgCAIFBYWQiqVYteuXaisrERm\nZiYIgrB4L0EQCAwMtPfpdXh0yaDbXMjlclRUVCAsLIyrwR44cICbHtnbru7evXsW4gunuq5xEAQB\nd3d3vPjii9yN0dxX4tChQ/jwww9B0zSee+45rixRWVkJrVaLESNGAGC63BqNRod3aLYF8+yWNcyp\nqanBqlWrUF5ejm+//dZubmANwZ43+ZSUFKSkpHAltfDw8DrXIoBOO+NyBLp90FUqlVw/M/NAyZqJ\nA3UXEHx9fTFt2jSubrxixQokJydDo9Hg1q1bePrpp/HSSy/ZfP+FCxfg7u5usXLblm1OOiLDoaVo\nyFfi3LlzmDJlCh48eICXX34ZI0aMgEwmQ2BgIHg8nsM6NNcHW+1z2HZNCxcuxBtvvNFmwd95k29f\ndPugy/rNNgZrloOtuvH9+/e5f+Pi4izqxuz7L168WEcp11ZtTjoqw8FeMPeVSElJQWhoKLZs2QK9\nXo/s7GycP38emzdvtvCVCAoKgre3N6eMs1ejTHNYt8/R6XRYu3Ytbty4gcOHD6N///5N2k5LF4et\n4exl1r7o9kG3pWhJ3bg+tFWbk7S0NAtT+IkTJ2LDhg1dJuiaY9euXRYigldffRWvvvoqAEtfie3b\nt+PGjRtwdXWFRCJBUFAQZDIZ3N3d67TJaWiRzhas2+fw+XxcvnwZS5Yswdtvv42kpKQmZ9UtXRy2\nBWcvs/aFM+i2Meojizi6zcnRo0dRXV2NyMhIzl6vqyjjbKEh1VZzfSWCgoIwfPhwrhmmta+ELbtL\nNrsVCARwc3OD0WhEQkICsrOz8dVXX8HHx6dZ59MZb/JO2IYz6LYRWHVdZmYmKisr4ePjY9HmpL66\nMYuWjrOKOrbHnHl21tkZDvYCQRDw9PREeHg4V9+nKAo3b95EVlYWvv76a1y9ehU8Hg+jR4/mDH6e\neOIJUBTFNcpklXSswkwoFMLFxQW///47Fi1ahGnTpuH48eNtaoDeXjd5J+pHl+TpOlEXGRkZiIiI\nQExMDEeUVyqV6NWrFxQKhYVhfEPbsJe6qTNImM1h7Stx4cIF3Lt3D/369eOsLk0mE0pLSzFp0iRO\nDODn54eysjIsW7YM06dP5/wmHA32Jp+SkoLKykrEx8c7e5l1EDiDbjeBXC7HxIkTLYKuuXKuoUy3\nrdVNnaX3Fusr8f3332Pz5s0oKCjA+PHj0b9/fwwaNAhyuRz+/v544okncPHiReTl5UGhUMDFpelt\n2pOSkgAwC7AymazOgm5Xv9l1STjSwswJx2H58uU0QRA0QRB0ZGQkrVAo6MDAQJogCFoqldZ5fV5e\nHg3AwnLPll1fQ4iLi6Ojo6PrPN/drS8/+OADetasWXRFRQWt0+nonJwc+v3336ePHj1q8TqKopq1\n3bi4OIvHEomEsyGlaeYaMLdGjIuLozMyMuw27oRj4Ay6nRjN9TG1DrrW/sKNwVbQzcvLqxM0zYN5\na8c7A4xGo923ae7zzMLp89w14FxI68Roro8p8NixH7DP4odTwgyHLIyVl5dzPs9svd3Ly8vp89wF\n4Ay6nRzp6ekYPHgwIiMj63AnrUEQBKqqquplQLQETnWTY+Dt7Y38/HyLBc5Tp05xtCznza7zwhl0\nOzk8PDwa9TE1x+jRo+26QOUIddP69etB0zRiY2MxduzYbrt4xNqLAo7zea5v3Bl0HYf287Zzwi6I\ni4tDZGQk5s2bh8TERFy6dKlN929vdVN8fDwiIyNBEAQOHz6MAwcOcCv4AHO+EokEr732GqKiolBQ\nUGDhM9vacUdDqVRCpVLV+1cfIiMjcebMGS7zdUp5OzHau6jsRMuRnp5usfDh6elJ+/j40Eql0ubr\nCYKgIyIiWry/prIXWtIglKYfLx6Zj3elxaOMjAw6Ojq6wT9rxgJNM5+7dQPHhhpF2mPcCcfBGXQ7\nKebNm0cTBEH7+vrScrmcTk9Pp728vGiSJGkfHx/uR5qenk5LJBJujB2PjIxs9j6XL19uM+haU43i\n4uLozMzMZo8XFBTQBEHQMTEx3Hh6errdmBCdkSmRkZFhEXDz8/O5/9vrZlffuBOOgbOm20nB+pia\nY/r06XVeN336dJvPNwdtJWEGgIULF2LEiBHceHdePGprn2enlLdt4FSkOdFhoVQqLVbxMzIyMG/e\nPIsFIHMpc25ubqvGmyKFbiuwx2WNiIgIHDhwgHvslPJ2PjgzXSc6LBy9eKRUKjmmRHJyMkQiUR32\nR2uZEB999BHXA+zhw4cICAho0j7MfZ6tx82xbNkyi/G0tDSLY2htt2wnHIB2Lm844YRNtMXi0fLl\nyy2ek0gkdGpqqsV4a2W2M2bMsBj39va2+z6cUt7OBWfQdaLDoa0Wj3x8fOjY2FhuPCIiwoLdYQ+m\nhI+PDyfTlsvldN++fe2+j4bGneh4cPJ0nehQYBePJBIJlEolFAqFRQ2TXfwxf72txaGmjMvlcgiF\nQm68oKAAQUFBAOwns5XL5Vyd1MvLC6WlpXbfR33jTnRMOIOuEx0GbJPQ6OhoeHl5oVevXvD19UVh\nYSH3mvXr10OhUCAzMxNJSUk2mRBNHU9PT+fG8/PzQZIkli5dCsB+TAnzxbm7d+8CYAK/PfdR37gT\nHRPOhTQnOgxa2iS0NeMqlQppaWlIT09Hamoq97w9ZbYqlQoHDx7Evn37uHF3d3enlLebwpnpOtGt\n4eHhgaioKJw8eRJz585FWloagIaZEiRJQiAQcL3V2L+ioiLQNF2HKcHuY/fu3QCYfnWN7cMp5e26\ncGa6TnRbKJVKi8AVExOD6OhoREVF1esZQdM01q1bh7KyMiiVSsTFxXHjZWVlAIB169Zxrm/m+2C3\nt2jRIixYsMDZlbebwhl0neiWkMvlCA8Pt2hVRD/SCVVVVdXb8TY8PBy7du0CwGST7P/ZbarVao6H\n26NHDy4wuru7o6KiAv7+/vj9998b3IezK2/XBm/16tWr2/sgnHCirdG7d28olUoLm8v169dj5MiR\nePPNNwEAlZWVUKlU8Pf3BwCkpqZiypQpGD58eJPGHz58CHd3d469kJqaitLSUoSGhtptH42NO9Hx\n4JQBO9FtwXpKAEynBoIgkJCQYPGa1spoFy1ahIqKCpSXl4OiKIwePdru+3BKeTsX/j8/NaGxVF7D\nnwAAAABJRU5ErkJggg==\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x2825f10>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 5
|
|
},
|
|
{
|
|
"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": 6
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"fig, ax = plt.subplots(1,1, figsize = (7.5, 7))\n",
|
|
"Utils1D.plotLayer(np.log(sigma), mesh.vectorCCz, 'linear', showlayers=True, ax = ax)\n",
|
|
"ax.invert_xaxis()\n",
|
|
"ax.set_ylim(-500, 0)\n",
|
|
"ax.set_xlim(-7, -4)\n",
|
|
"ax.set_xlabel('$log(\\sigma)$', fontsize = 20)\n",
|
|
"ax.set_ylabel('Depth (m)', fontsize = 22)\n",
|
|
"ax.text(-7., 10., '(a)', fontsize = 30)\n",
|
|
"fig.savefig('logcond1d.png', dpi=200)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": 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zPP3Xf/3XjSFAkvb390O/7k2nE/rNY7C1taWvX78GRwjan+6vunrtwKD2Xq4GAQAAonb9\nQ+cvv/wS2bpCB4Hnz5/r27dvI+1EsVhUqVS6sSYWi3XcKSC1TlPs7u52HCGIx+PB9QJt7cfT09MD\n2wEAMEnouwZc19XCwoKy2azev3/f981zamoqkrsG2orFomzbDm4BrNVqWlxclNR9V4DjODo8PNSX\nL1+Gar+KK18BIDz2naMV5XiGPpGfSCT09u1b7e/vy7Ztzc7O6ocffuhY4vF4ZHcNSK037kajoeXl\nZXmeJ9d1dXx8HLRvb2+rWCx21GcymaHbAQAwRegjAvl8fqg3Tcuy9Oeff966Y/14ntfzXP7m5mZH\nGGjPHOi6rmzb1uvXrzvqB7W3kWoBIDz2naM1VlMMLywsdHwboW3bXTWu62prayvSUwP3hY0ZAMJj\n3zlaYxUEpqamtL29rQ8fPtxYZ9u2Li4u7tS5ccDGDADhse8crbG6RmBxcbHnUYDr/vjjj9v0BwAA\n3KPQtw++f/9e29vbymQyHbP7XZdIJHR+fn6Xvo2tSqW1XJdMthbqqaeeetPr+5mU/o9bfZRudbHg\n0dGRarWaXr58qdXV1a5Jei4uLpTNZrlGAAAMxb5ztMbuGoGhXjiiuwbuGxszAITHvnO0xu7bBxOJ\nhJaWlvp2ynVd/etf/7pTxwAAQPRudUSgXq8P/K6BqGcWvC+kWgAIj33naI3VXQNv374dGAIk6dOn\nT7fqEAAAuD99jwg0m82+3/gX1ihf676RagEgPPado/UgRwSOj4/16tWrO69ga2uLowMAAIypvkFg\ne3tb379/1+rqqr5+/Rr6hcvlshYWFhSPx5VOp+/USQAAEI2BFwtmMhnl83ktLy9rbW1Nq6urWlpa\nUjweD76C+PLyUufn56pWq/r27ZsKhYJc1x1qKuJxx+EtAAiPfedoPfg8AoVCQel0Ws1mM+hQL+2X\nisViyufz2tjYGGFXHwYbMwCEx75ztB48CLQVCgUdHR2pXC53tc3MzGh1dVWZTOZRBIA2NmYACI99\n52iNTRC4yvM8NRoNSVI8Hu+aZvixYGMGgPDYd47WWAYBU7AxA0B47DtHa6wmFAIAAI8HQQAAAIMR\nBAAAMBhBAAAAgxEEAAAwWGRB4IcffojqpQEAwIg8ucsPn52dBbMNXlWv11Wv1+/y0mOtUmkt1yWT\nrYV66qmn3vT6fial/+NWH6VbzSOws7OjfD7ft933fVmWpT///PNOnRsH3AsLAOGx7xytKMcz9BGB\n3d1d5XI5Sa1phePxeFdNo9HQ5eXl3XsHAAAiFfqIQPuNv1wua3FxsW/d1NSUvn//frfejQFSLQCE\nx75ztMbqiIDneTo4OLgxBEjS3t7erTsFAADuR+i7Bp4/fz7UhYCZTOZWHQIAAPcn9KkB13W1srKi\nQqGgZ8+e9a2bnZ3V+fn5nTv40Di8BQDhse8crQc5NZDP52VZVs+27e1tra2taXNzU6urq11fQXxx\ncaGLi4vR9hQAAIxc3yMCU1N3m2uI2wcBwFzsO0frwS4WTCQSmpubC/2ijUZD//rXv27dKQAAcD9u\nPCJQr9dvFQTaP8/tgwBgJvadoxXlePY9/r+xsaHZ2dlbv/CnT59u/bMAAOB+3GqK4V6azaYsy9L0\n9PQoXm5skGoBIDz2naP1IEcE+vntt996Pn98fKynT58qHo/3rQEAAOMl9BGBQef+XdfV+vq6Njc3\n9euvv965gw+NVAsA4bHvHK2xOiIwSCKR0Pb2dvDFRAAAYHzdePtgs9nU2dlZ8LidRvrdGthoNFSv\n15XL5UiCAABMgBuDQKlUUjabDcJAe6bB5eXlnvVX3/y3t7dH1UcAABCRoa8RyOVy2tnZkaS+cwvE\nYjHF43EtLy8/mm8f5DwXAITHvnO0ohzPUBcLOo6j9fX1RzFR0LDYmAEgPPado/VgUwxft7a2pnQ6\nHUlHJkml0lquSyZbC/XUU0+96fX9TEr/x60+SiObUOixItUCQHjsO0drbI4IXFcsFvXt2zednZ0p\nkUhodXVVP/3006j6BgAAInarIFAsFpVOp+V5Xlebbdv6/Pmznj17dufOAQCAaIU+NVCr1YLbB+fm\n5pRIJBSLxeR5nk5OToLvHDg9PdWPP/4YSafvE4e3ACA89p2jNTZ3DUjS+vq6Tk5OVC6Xtbi42NXu\nOI42Nzf117/+VV++fBlZRx8KGzMAhMe+c7TGKghMTU3p8+fP2tjY6FvTnnPgMdxmyMYMAOGx7xyt\nsfqugbm5OV1cXNxYMzs7q1gs1vV8v6mJAQDAwwgdBDKZjPb393V5edmzvdlsKpvN6v37911tz58/\nD99DAAAQmdB3Ddi2Ld/3Zdu2Xr58qdXV1eBiwX/+858qFotaXFyUbdv6+PFj8HMXFxcDjyQAAID7\ndatrBG69MsvSn3/+eeuffwic5wKA8Nh3jtbYTSiUSCS0tLQUqlOu63KNAAAAY+ZWRwTq9XrfbyAc\n9LOTdicBqRYAwmPfOVpjddfAhw8fbhUCJOnTp0+3+jkAABCNkXzp0OXlpaanp0fRn7FDqgWA8Nh3\njtZYHRFoK5fLWllZ0dTUlGzbliRVq1UtLCxwLQAAABPiVkcEtra2VCgUOp5rn/t3HEdbW1uqVqt6\n+vTpSDr5kEi1ABAe+87RGqsjAoeHhyoUCnr37p1KpVLXef+1tTWl02lls9mRdRIAAEQj9O2Dx8fH\nKpVKN84SmEqltLW1daeOAQCA6IUOAtVqdeBUwa7ryvO8W3dq3FUqreW6ZLK1UE899dSbXt/PpPR/\n3OqjFPoagVQqpVevXun169fBc9fnB1hfX1ej0dDJycnoevpAOM8FAOGx7xytsbpGYHNzU9vb23rx\n4oX+8Y9/qFarSWrdQlgul7W+vi7HcfTq1auRdxYAAIzWre4ayGQyyufzrRf4/ynFsixJku/7Wlpa\nehRHAyRSLQDcBvvO0RqrIwKSdHR0pE+fPmlubi7omO/78n1f7969ezQhAACAx24kMwu6rqt4PK5Y\nLDaKPg3keZ7y+bxisZjq9bokaW9vr6Pm8PBQiURCjUZDkpROp0O1t5FqASA89p2jFeV4jiQI3Lds\nNqv9/f3g8crKijKZTPBmns1m9eLFCz179kyStLu7q9XVVW1sbAzVfhUbMwCEx75ztMbi1EC5XNbO\nzo4WFhY0Ozur//iP/9Ds7KxWV1f197//XV+/fo2kg70Ui0V9/PgxeJxIJFQqlYLH+Xw+eJOXWnc6\nHB0dDd0OAIApBs4jUKvVtLm5Kdd1u9ouLi50enqq09NT5XI5LS0t6fPnz5FPLew4Tsc66vW6/va3\nv0lqzXNwnW3bchxnqHYAAExy4xGBcrms5eXlniFgZmam47Hv+zo9PdX8/HzkXzp0NQRUq1VNTU3p\nzZs3kqRGo6F4PN5R37524fLycmA7AAAmufGIwObmpiRpaWlJmUxGa2trsm2766JA13XlOI6Ojo5U\nq9X0/PlznZ+fR9drSc1mU58+fdLnz5+Vy+WC5z3PCy4AbGu/8TcajYHtvb5O+eeffw7+nUwmlew1\nHRQAACNSqVRUua8pBv0+crmcb1mWf3Bw0K+kp8+fP/uWZfn5fD7Uz11cXPie5/VdbrK0tOTncjnf\n932/VCr5tm13tNfrdd+yLL/ZbA5sv+6GIQIA9MG+c7SiHM++dw2sr68rFot1fbvgMDKZjP744w99\n+fJlqPpisdhxsV8vsVgsuEXQ87yOoxL5fF6ZTEbfv39XtVrVyspKx5THV58b1H4dV74CQHjsO0cr\nyvHse2rAdd1bX0mfyWRCffvgxsZGz1v3enEcR+vr6/I8LziM3x6cy8tLLS0tdZ26aDQaSqVSkjSw\nHQAAk/S9WLDRaGh+fv5WL5pIJHpeYDgKq6urymQyHefyS6WSNjc3g+e2t7dVLBaDdsdxlMlkgseD\n2gEAMEXfUwNTU1Mdn7rD8DxP8Xi856H2UajVasHtfufn57IsS7/++mtHTXvmQNd1Zdt2x7clDtPe\nxuEtAAiPfedoPcjMgncJAmdnZ5qfn48sCNwnNmYACI9952g9yDUCUuv2wducO//999+DbyMEAADj\n68YjAnd6YcvSn3/+eafXGAekWgAIj33naI3Fdw0AAIDH58ZTA6VSSc+fPw/9ooVCIdTtg5OmUmkt\n1yWTrYV66qmn3vT6fial/+NWH6VILhZ0XVcLCwtcLAgAhmLfOVoPcmrg06dPtwoBUmsegdvMSAgA\nAO5X3yMCaCHVAkB47DtHi4sFAQBAJAgCAAAYjCAAAIDBCAIAABiMIAAAgMEIAgAAGIwgAACAwQgC\nAAAYjCAAAIDBCAIAABiMIAAAgMEIAgAAGIwgAACAwQgCAAAYjCAAAIDBnjx0ByZRpdJarksmWwv1\n1FNPven1/UxK/8etPkqW7/v+/a5ysliWJYYIAMJh3zlaUY4npwYAADAYQQAAAIMRBAAAMBhBAAAA\ngxEEAAAwGEEAAACDEQQAADAYQQAAAIMRBAAAMBhBAAAAgxEEAAAwGEEAAACDEQQAADAYQQAAAIMR\nBAAAMBhBAAAAgxEEAAAwGEEAAACDPXnoDkyiSqW1XJdMthbqqaeeetPr+5mU/o9bfZQs3/f9+13l\nZLEsSwwRAITDvnO0ohxPTg0AAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAA\nAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgsCcP3YFJ\nVKm0luuSydZCPfXUU296fT+T0v9xq4+S5fu+f7+rnCyWZYkhAoBw2HeOVpTjyakBAAAMRhAAAMBg\nj+IagZ2dHX348KHjucPDQyUSCTUaDUlSOp0O1Q4AgAkm/ohANpvVyclJ13PLy8va2NhQOp1WvV5X\nsVgcuh0AAFNMdBBwXVeWZXU9n8/n9ezZs+BxKpXS0dHR0O0AAJhiooNAuVxWKpXqeK5arXbV2bYt\nx3GGagcAwCQTGwTK5bK2tra6bqdoNBqKx+Mdz8ViMUnS5eXlwHYAAEwysUHA8zzNzMz0fL59AWBb\n+42/0WgMbAcAwCRjc9eA53k9z/e3XX3TLxaL2tjY6FnX/nR/VfsNPh6PD2zv5eeffw7+nUwmlew1\nHRQAACNSqVRUuacpBsciCBSLRZVKpRtrYrGY9vb25Lpuzzfztng8Ls/zOp5rP56enh7Y3svVIAAA\nQNSuf+j85ZdfIlvXWASBjY2Nvp/wr6vVanJdN7jo79u3b/I8T7/99ps2Nja0tLTUFRQajUZwUeGg\ndgAATDIWQSCM64Ehl8vJdV29efMmeG57e7vj9IHjOMpkMkO3AwBgion+0qF8Pq/Pnz/r9PRU79+/\nVzqdDq4laM8c6LqubNvW69evO352UHsbX5wBAOGx7xytKMdzooPAfWBjBoDw2HeOFt8+CAAAIkEQ\nAADAYAQBAAAMRhAAAMBgE3f74DioVFrLdclka6GeeuqpN72+n0np/7jVR4m7BgbgylcACI9952hx\n1wAAAIgEQQAAAIMRBAAAMBhBAAAAgxEEAAAwGEEAAACDEQQAADAYQQAAAIMRBAAAMBhBAAAAgxEE\nAAAwGEEAAACDEQQAADAYQQAAAIMRBAAAMBhBAAAAgxEEAAAw2JOH7sAkqlRay3XJZGuhnnrqqTe9\nvp9J6f+41UfJ8n3fv99VThbLssQQAUA47DtHK8rx5NQAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAA\nBiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAAAYDCCAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYj\nCAAAYDCCAAAABiMIAABgsCcP3YFJVKm0luuSydZCPfXUU296fT+T0v9xq4+S5fu+f7+rnCyWZYkh\nAoBw2HeOVpTjyakBAAAMRhAAAMBgBAEAAAxGEAAAwGAEAQAADEYQAADAYAQBAAAMRhAAAMBgBAEA\nAAxGEAAAwGAEAQAADEYQAADAYAQBAAAMRhAAAMBgBAEAAAxGEAAAwGAEAQAADPbkoTswiSqV1nJd\nMtlaqKeeeupNr+9nUvo/bvVRsnzf9+93lZPFsiwxRAAQDvvO0YpyPDk1AACAwQgCAAAYjCAAAIDB\nCAIAABg8eXVQAAAK9klEQVRsIoNAoVDQwcGBzs7O5HmeDg8PdXZ21lFzeHioYrGofD6vfD7f9RqD\n2nGzyn1f1johGJfeGJdujElvjMv9m8gg0Gg0tLu7q/n5eSUSCc3Pz2tubi5oz2azWl5e1sbGhtLp\ntOr1uorF4tDtGIz/rL0xLr0xLt0Yk94Yl/s3kUHAsix5nifXddVoNPTTTz91tOfzeT179ix4nEql\ndHR0NHQ7AACmmNgJhaanpzU9Pd31fLVa7XrOtm05jjNUOwAAJpnICYXa5/Tj8bgkyXVdvX37VpLk\nOI52dnb073//O6h3XVcLCwvyPE///Oc/b2y/Hi4WFhZUr9ej/pUAAOhrfn6+431rlCbyiMDa2lrH\nNQE7OzvK5/NKp9PyPE+NRqOjvh0YGo3GwPbrQSCqgQcAYByMzTUCnuep2Wz2Xa66GgKk1jn+/f19\nSVIsFut67fYbfzweH9gOAIBJxuKIQLFYVKlUurEmFotpb29PnucpHo93HMafmZmR67qSFLRd1X48\nPT09sB0AAJOMRRDY2NjQxsbGULWWZendu3cdb9qu62p+fl6StLS01PWpv9FoKJVKDdUOAIBJxubU\nwLBmZmY0Ozvb8VyhUAhODUjS9vZ2x7wAjuMok8kM3Q4AgCkm8q6BZrOpXC6nWCymer2uv/71r11z\nCRweHiqRSMh1Xdm2rdevX4dqBwDcv52dHX348KFve6FQkOu62tzclG3byufzevnyZde1Y4/NoHGR\n/u99rX3dWzqdHuq1JzIIRCmTyWh3dzfURnXbwZ80nudpb29Pq6urajQaWllZ0eLiYs9ak/6zhhkX\nyYztJezf34Tt5Ta/ownbylXZbFblclknJyd9a3K5nHZ2diS1rh37+PFj1wfBx2aYcclms3rx4kUw\nWd7u7q5WV1eHO+3uo0MikfAty+pa8vl8z/p379755XI5eJzNZv1CoXBf3b03FxcX/vLycvD44ODA\n39zc7Ft/dHQUjJ1t236xWLyPbt67sONiyvYS9u9vwvYS9nc0ZVtpq9frfjab7fj/1Esul/ObzaZ/\ndnZ2Px17YMOOi23bHY8dx/FTqdRQ6yAIXJPJZPxareafnZ35Z2dnvuu6/u7ubt/6uwz+JNne3u4K\nQ57n9a035T9r2HExZXsJ+/c3YXsJ+zuasq205XI533GcoYKASYYZl9PT067t5fT01Lcsa6h1jMVd\nA+Oi2Wwqm812HKrL5/N6//59z3qTpivuNQ4zMzM3/ky/aaAfkzDjYtL2IoX/+5uwvQz7O5q2rZTL\nZW1tbenbt29D1efz+Z4zyz42w45Lo9HomgenfXfc5eXlwG2OIHDFzMxMx068Wq0qkUj0HcS7Dv6k\naM/RUK/XdXp6GszQOOg/32P/zxp2XEzZXtrC/v0f+/YiDf87mrateJ438INF200zyz42w45L2Blz\nr5u42wfvUy6X0/Pnz/u2Dxr8x6L9hmdZVvDVzVLrYpR+1tbWlE6ngzki6vV68B0Rj0XYcTFle5HC\n//1N2F7C/I6Tvq2EmSm2WCwOPY+MdPPMsuMuqnG564y5RhwR8DxPlmX1be+VuBzHCSYp6mfSpyse\ndlzav8vKykrQ9vz5c62srGhvb6/nz/b6z5rNZicitUc1LqZsL1L4v/+kbi9RjckkbythZop1Xbfn\n79rPoJllx1mU43LXGXMffRAIM/hXHR0d6W9/+9uNPzfJ0xWHGZf2Bnn1d7rpMKUp/1nDjosp20vY\nv/+kbi9RjskkbythZoqt1WpyXTe4JuLbt2/yPE+//fabNjY2usLToJllx1mU43LnGXPvdDnjI2ZZ\nll+r1QbWXb9Ss1Qq+evr61F168HYtu27rhs8vumKVM/z/Gw22/Hc0dGRv7CwEGkfH0KYcWnXX/UY\nt5ewf38Ttpfb/I4mbCvXHR0ddV0dX6/XO26bPDg46GhPpVKP8nbTq4YZl+u3l2az2aHHhWsEemgn\n716HZlzX7Zie2JTpit+/f99xxfLx8bEODg6Cx1fHZZhpoB+LMOMimbG9DPP3N217CTsmkhnbylX5\nfF6FQkFnZ2f67bffgnPm5XJZuVwuqNve3tbh4aHy+bx2d3e1s7PzqCcUGnZc2qcUisWiDg8PtbCw\nMPS4MLNgD57naXV1Vaenp12H4dp/lC9fvgTPmTJd8eHhYfBvy7L05s2b4PH1cRlmGujHIsy4tOsf\n+/Yy6O9v4vYSdkwkM7YVPDyCAAAABuPUAAAABiMIAABgMIIAAAAGIwgAAGAwggAAAAYjCAB4ENfn\nVo/65wD0RhAAcO9c1731V+p++vRJZ2dnI+4RYC6CAABls1nZtq2pqfvZJRwcHIT6xrmr0un0o5p1\nEHhoTCgEQFLr65PL5bK+ffsW6XoODg60vr6uH3/88davUavV5DiO3r59O8KeAWbiiAAASVKpVNLa\n2lrk63Ec504hQJIWFxcHfvMfgOEQBABIan3KHvprS2/JcRytr6+P5LVSqVTHl/IAuB2CAIDgwr2V\nlZVI11MoFPTy5cuRvNbLly91fHw8ktcCTPbkoTsA4OFVq1UtLy93fdum1Po2zt3dXcViMc3Ozur8\n/Fx7e3tddblcTpZlyfM8LS4uan9/XwcHB1pcXAxqHMfRhw8fbuxHNptVuVzuarNtW+fn58Hjubk5\nVavVsL8qgGs4IgCg7/UBjuNobW1NBwcH2tvbCy7O29nZ6ajb3NzU5eWl0um03r59q2q1qnK5rPn5\n+Y66RqPRtw+O42hra0t///vf5bqujo6OJLXCged5HSFgmNcDMByCAACVy+Wu6wM8z9PW1pY+fvzY\ncaRgbW1NuVwueJzL5VSr1fTmzZvguUQiofn5+Y6f8zxPiUSi5/o9z9P6+roKhYJ++uknPX36VOl0\nWmtrayqVSj2PVLTXwwRDwN0QBADDtQ+vP3v2rOP5dDqt+fn5riv8XdfteJzNZruOEPQ6wuC6ruLx\neM8+pNNpZTKZnncT3PSpPx6Pd/UHQDgEAcBwjuNoeXm56/lyuaxXr151PX96ehoc8q9Wq2o2m10X\nAPZ6zVgs1rcPxWJRmUym63nXdbW6unpj/23bvrEdwM0IAoDh+n169zxPS0tLXfWfPn0K3vhPTk4k\nSU+fPg3aPc/T2dlZ12v2+/Tefq7XkQfXdW+c2+CmowwAhkMQAAx39fqAfD4vScG5/OtvsoVCQVNT\nU/r111+D566f93ccR7FYrCMcSK0jAmEu7tvf31c2m+17fYDUOm1wUzuAwQgCADQ3N6dardYxj8DL\nly87pht2XVd7e3sdt/atra11vLm7rqtsNtt3YqJen94TiYSWlpY6vkioUCjojz/+6AgcvfS7+BDA\n8JhHADDc/v6+jo6OtLCwoNevXwfP5/N5ZbNZSVK9Xpckff36teMTeCKRUD6f1+7urmZnZxWLxWTb\ndt8gsLa2plqt1jG3gNQ6KpHNZrW8vCzP82Tbtr58+XJjv6vVqv7zP//zVr8zgP/Dlw4BGBnP8xSP\nx1WtVnveAVCr1XR8fNxzQqKwdnZ2tLu723UKAkA4nBoAcGue53U8dhyn5y2HbYuLiyObDdB1XUIA\nMAIEAQC3cnBw0HHOvz0VcXtGwH6y2WxwUeJt5XI57e7u3uk1ALRwjQCAW1leXlYmkwne1E9PT1Uo\nFAZ+xfDz589VKpXUbDY1MzMTer2e56nZbHZNgATgdrhGAMCDyOfzSqfT9/ZzAHojCAAAYDCuEQAA\nwGAEAQAADEYQAADAYAQBAAAMRhAAAMBg/w8o0AMw+wzGGQAAAABJRU5ErkJggg==\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x6591a50>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 46
|
|
},
|
|
{
|
|
"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\", fontsize = 16)\n",
|
|
"cb.set_label(\"Conductivity (S/m)\", fontsize = 16)\n",
|
|
"ax.set_xlabel('Easting (m)', fontsize = 16)\n",
|
|
"ax.set_ylabel('Depth (m)', fontsize = 16)\n",
|
|
"ax.set_xlim(-1000., 1000.)\n",
|
|
"ax.set_ylim(-500., 0.)\n",
|
|
"ax.text(-1000., 20., '(b)', fontsize = 20)\n",
|
|
"fig.savefig('cond3d.png', dpi=200)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
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"png": 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sy4jFYtjc3EQ2m8XZ2ZllJwu7dCIimoJHuxotojsdfBOJBB4/foxoNDpyIe5arYb19XXz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\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x5637310>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 44
|
|
},
|
|
{
|
|
"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": 9
|
|
},
|
|
{
|
|
"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": 10
|
|
},
|
|
{
|
|
"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": 11
|
|
},
|
|
{
|
|
"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": 12,
|
|
"text": [
|
|
"(-0.2, 0.6)"
|
|
]
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": 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|
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"text": [
|
|
"<matplotlib.figure.Figure at 0x43f1b10>"
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]
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}
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],
|
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"prompt_number": 12
|
|
},
|
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{
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"cell_type": "code",
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"collapsed": false,
|
|
"input": [
|
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"fig, ax = plt.subplots(1,1, figsize = (6,4))\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.legend(('A (C+)', 'B (C-)', 'M (P+)', 'N (C-)'), fontsize = 14)\n",
|
|
"mesh.plotSlice(np.log10(mapping*mtrue), grid=True, ax = ax, pcolorOpts={'cmap':'binary'})\n",
|
|
"ax.set_xlim(-600, 600)\n",
|
|
"ax.set_ylim(-200, 200)\n",
|
|
"ax.set_title('Survey geometry (Plan view)')\n",
|
|
"ax.set_xlabel('Easting (m)')\n",
|
|
"ax.set_ylabel('Northing (m)')\n",
|
|
"ax.text(-600, 210, '(a)', fontsize = 16)\n",
|
|
"fig.savefig('DCsurvey.png', dpi = 200)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": 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H0dERjo6OUK1WkclkJjYyq6uryOVyZig2L0CDRAghM0RVVfR6PZyenk5kPFRVRbvdNmN5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\nPL/9AAAAAElFTkSuQmCC\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x6585890>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 42
|
|
},
|
|
{
|
|
"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": 14
|
|
},
|
|
{
|
|
"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 = SolverLU\n",
|
|
"problem.Solver = SolverWrapD(EMUtils.Solver.Mumps)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 15
|
|
},
|
|
{
|
|
"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": [
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stderr",
|
|
"text": [
|
|
"/usr/local/lib/python2.7/dist-packages/mumps/__init__.py:204: RuntimeWarning: undestroyed DMumpsContext\n",
|
|
" RuntimeWarning)\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 16
|
|
},
|
|
{
|
|
"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": 17
|
|
},
|
|
{
|
|
"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(1,2, figsize = (14, 4))\n",
|
|
"ax[1].semilogx(abhalf, appres, 'k.-')\n",
|
|
"ax[1].set_xscale('log')\n",
|
|
"ax[1].set_ylim(100., 180.)\n",
|
|
"ax[1].set_xlabel('AB/2')\n",
|
|
"ax[1].set_ylabel('Apparent resistivity ($\\Omega m$)')\n",
|
|
"ax[1].grid(True)\n",
|
|
"ax[1].text(100, 183, '(c)', fontsize = 16)\n",
|
|
"# ax[1].legend(('Observed', 'Predicted'), loc = 1)\n",
|
|
"\n",
|
|
"dat = mesh.plotSlice((mapping*mtrue), normal='Y', ind = 9, ax = ax[0])\n",
|
|
"cb = plt.colorbar(dat[0], ax =ax[0])\n",
|
|
"ax[0].set_title(\"Vertical section\", fontsize = 16)\n",
|
|
"cb.set_label(\"Conductivity (S/m)\", fontsize = 14)\n",
|
|
"ax[0].set_xlabel('Easting (m)', fontsize = 16)\n",
|
|
"ax[0].set_ylabel('Depth (m)', fontsize = 16)\n",
|
|
"ax[0].set_xlim(-1000., 1000.)\n",
|
|
"ax[0].set_ylim(-500., 0.)\n",
|
|
"ax[0].text(-1000, 20, '(b)', fontsize = 16)\n",
|
|
"fig.savefig('DCfwd.png', dpi=200)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": 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nHxsbw+joaMH7/f39NR/b7XYbtjOZDPx+P2w2G+LxOMxmMxYXF2s+DxERkYbp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\nniOsAAAAAElFTkSuQmCC\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0xd821fd0>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 43
|
|
},
|
|
{
|
|
"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": 21
|
|
},
|
|
{
|
|
"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 4.75e-01 6.33e+03 2.24e+04 1.70e+04 1.16e+04 0 "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
" 1 4.75e-01 1.14e+03 5.22e+03 3.61e+03 4.96e+03 0 "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
" 2 9.49e-02 2.92e+02 4.20e+03 6.90e+02 1.07e+03 0 Skip BFGS "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
" 3 9.49e-02 2.21e+02 4.47e+03 6.45e+02 1.21e+02 0 Skip BFGS "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
" 4 1.90e-02 2.01e+02 4.51e+03 2.87e+02 3.76e+02 0 "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
" 5 1.90e-02 1.20e+02 7.00e+03 2.53e+02 1.17e+02 0 "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
" 6 3.80e-03 1.28e+02 6.48e+03 1.52e+02 2.18e+02 0 "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
" 7 3.80e-03 4.19e+01 1.76e+04 1.09e+02 1.41e+02 0 "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
"------------------------- STOP! -------------------------\n",
|
|
"1 : |fc-fOld| = 4.3690e+01 <= tolF*(1+|f0|) = 1.6953e+03\n",
|
|
"0 : |xc-x_last| = 1.4581e+00 <= tolX*(1+|x0|) = 2.6641e-14\n",
|
|
"0 : |proj(x-g)-x| = 1.4105e+02 <= tolG = 1.0000e-01\n",
|
|
"0 : |proj(x-g)-x| = 1.4105e+02 <= 1e3*eps = 1.0000e-02\n",
|
|
"1 : maxIter = 7 <= iter = 7\n",
|
|
"------------------------- DONE! -------------------------\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 22
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"# matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 23
|
|
},
|
|
{
|
|
"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, fontsize=14)\n",
|
|
"ax.text(100., 185, '(a)', fontsize = 20)\n",
|
|
"fig.savefig('obspred_dc1d_dat.png', dpi=200)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": 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zkEkx+SY+ZvT29hZyqZIr8NEIKRlBENjatWvZF77wBfbMM8+U7kaDg4xt386Y\nTsfYokUfT168+ur85yeTjFkss57A+MEHH7CGhgZ2+umns4suuoi9/fbbRRSeVAu53p1FZUXmeR4G\ngwEbNmyA2WwuPtIRMo8cP34cXV1daGpqwuWXXw6e53HZZZfJe5OhIaCrC9DrgUsvBf761/TERaMx\nfdxgSOfxyiczz2SWTXGnnHIKzj33XBw7dgx//vOf8dnPfhYulytvxnOy8BQUXJxOZ9a2TqdDJBJB\na2vrpOu8ELIQ/dd//RdWrFiB3//+9/jTn/6Ezs5OnHLKKfJcfLKA8uabwM6dwP/+38CePel+lAKb\nuwp15plnAgAMBgP27duHgwcPQqPRoL29HW+88UbJ7kuq37Q9calUKqsNLl+SyFQqhWg0Kn/pCKkC\n69atw6uvvoqTTjoJV111FU6cOIF3330X7777Lt577z3p/zNfb731FsbGxvCFL3wBjz76aE4f5awM\nD6fTpdx7b7of5ZxzgP/4j3S/yUkn5Z5fptnvu3fvzhqM8OUvfxl//etf8cADD+Diiy/G2WefjVNP\nPRV1dXV45JFHUF9fTyPMFogpO/TD4TCcTueM0tq3tLQUNaFLbtShT+Rw8OBBGAwGaR7K5z//edx0\n000488wzsXjx4rxf1113Hf7whz8AKHKi4+go0NcH/N//m/7vunXA888Df/4zPrp4VadPSSaT0Ov1\niMfjANKpZxYtWoSlS5di6dKl+Nvf/oYPPvgAF154Ifbs2VOakXKkYGWfRGm32xEOh8FxXNaN1Wo1\nlEoljJk23ipBwYXIYcOGDTh48CBefvnlGU9cLHqi4yuvpAPKI48AS5cCN92UHv2lVBY1wqsSJn4v\nTj75ZPztb3/DG2+8gW9/+9t4+eWXARSfbYDIpyKjxTiOk2UUQTkU+GiE5HjxxRfZJz/5Sfb6668z\ni8XCkjMcVZVMJgs6nzHGWCrFmNfL2KWXMrZkCWN33MHY88/nu3hRI7zKbarvxdq1axkAZjAYCvte\nkZKS691ZUPqXWCwGhUIBpVKJZcuWwev1IhgMYuXKldi6dWvxkU5GVHMhxfrmN7+J5cuX48477yzd\nTWIxYMMGQBCAs84C7r8/3dw1PrPwPFV0tgFSEhXJLWYwGMDzvDRqjOM42Gw2aDQaxONx7Ny5s+gC\nyYWCCymGIAhoamrC0NCQ/C++EyeAX/0KePBB4PDhdCD5qF+i2vtRyPwn17uzoLwNGo0Gvb29qK+v\nh8FgkNL+QOHhAAAgAElEQVR+A+nAQ8h84XQ6sWXLFnkDy/Aw4PUCDz0EfPazwHe+A3zta8BVV6WD\ni8GQzuFFyDxQUHARRVFaLZLnedjGrXtNq0GS+eKNN95Ab28vXnnlFXkueOgQ8JOfAH5/OpD86lfp\ndeUzaEVGMg8VFFwSiQRGRkYQDAYBQFqoKxAIQDf+l4WQOWzHjh248cYbcfbZZ8/+IqOjwP/7f+mm\nr1deAbZsAV5+GfjUp3LPpRUZyTxUUHDp6uqCTqeDIAgwGo0wGo0wmUwIh8NVNxSZkNl4++238cgj\nj+D555+f3QWOHUuvg8LzwGmnpWfO33ADINfsfELmiKIXC+N5Pn0hhUL2pY+LQR36ZDYcDgeOHDmC\n//iP/yjsg+++C3R3Az/8IXD8eLp/BaAOejLnyPXuLCi3WD46nQ46nU6ahTsTfr8fDodj2vPa2tpy\n9rndbgQCAXi9Xni93oLKSshUEokEvF4vOjo6Zv4hUQR+8ANAo0mnuN+3D1i5Mn2MOujJAjZls9jE\nlSjD4XDevECMMXAcN+0yx+FwGDzPIxgMQqvVTnkux3GIRCI5+9asWYPVq1cDSP+VGQgEsH79+imv\nRchM/PjHP8bXvvY1fPazn53+5H/8A3jggXRt5corgd//HrjggvQx6qAnpDIrUTocDoiiKA1jnkgQ\nBHg8HoRCoawAo1arkUgkpO1M7rO+vr685aFmMTJTIyMj0Gq1+MMf/oDlmcW08vmf/0k3fT38cLrJ\ni+PStRZC5omyzHMJh8NZ4/yVSuWUK1HKJRwOw2QyZSXMzPTtjKdSqWaUVJOQ6ezcuRMmk2nywPLa\na4DbDTz6KHD99enhxeedV95CEjKHTBlcJg4vtlqtk67b0tXVJUuBwuEwrFYrBgYGsvYnEomcuTSZ\nwDcyMoLa2lpZ7k8WnqNHj+L+++/P/4fK668DJhMwOAh85jPAc88B559f/kISMscU1KFvt9tx4MAB\nHD58GADg9XphtVqxY8cOtLS0yFIgURRRV1eXd//4JjHg44mbE/cTUgiv14tLL70UF1544cc7k8l0\nk1djI3D0aHreSjwOlDLPGCHzSEHBpbW1FTqdDr29vXC73bDb7VCr1RgbG8OWLVuKLsxUnfP50nBk\nggplByCz9cEHH8DtduN73/teesexY+nmr/PPTweYQ4eATNCh0V+EzFjV5BaLx+NT5nFSq9UQRTFr\nX2Z7siaxTZs2YdmyZQDSwamxsRGrVq0CAPT39wMAbS/w7VdeeQUXX3wxjogi+js6sGrPHqCpCf0/\n/CHwmc9g1dKlwO7d6P/a14CtW7Hqo3+j1VJ+2qbtYrf7+/uxa9cuAJDel3IoaBKl2WyWRmbV1NTA\nZrNJwWX8senkGy0WCAQgCIK0PTAwAJ7n0dbWhvXr16O+vj5ntFgoFILb7ca+fftyH4xGi5FpHD9+\nHP98/vl4oq0Nn3vkEUCtBlwu4ItfrHTRCKmYimRFliu3WL6CT2wO83g8EAQha50Ym82W1XQWCoVg\nt9sLeQRCJKF778Wvkkl87he/AJzO9HwVWt+dEHkUsrJYMBhkWq2WKRQKZjKZmCiKzGg0MoVCwcxm\n87Sf53meuVwuptVqmVqtZi6Xi/E8n3Oex+NhJpOJqdVq5na7mSiK0jGXy8X8fj9zuVzM6/VOeq8C\nH40sJC+8wMauuor9bdEi9petWxk7caLSJSKkasj17qTcYmThuO46YP9+YHgYf776atxy+DCefu65\nvFknCFmoKtIslnH48GEIgoDVq1dDpVJJa7wQUpXefz89q37v3vSQYgBv7tuH9kcfpcBCSIkUnLjS\nbDZDo9FIfR3RaBQGgwEjIyOyF46Q8YxGIxYvXgyNRoP+/v7p/7piDPj1r4EvfCGdVPKyywAAyYYG\n/OCf/gnr1q0rQ6kJWZgKCi5tbW1QKpXw+XzSRMeWlhZ4PB5YLJaSFJCQ0dFROBwOPPvss3jvvfcQ\nj8fx1a9+FcuXL8e2bdtw4MCB3EDzyivAunVARwewcyfw+OPAr34FZrGgpbYWt91zD9VaCCmhgoKL\nIAjw+XxoaWnJmrio0+ly0rUQIodEIoF169ZhYGAAl31U8zAYDHj99dfh8/kwNjaGq6++GhdccAHu\nuusu/OW558A6OoBLL00v2nXoEGA2py+mVCLU2or/OXYMV199dQWfipD5r6DgMlmalVgsJkthCBnv\n0KFDaGpqwoUXXoh9+/bB7/fDYrEgGAxCpVJBp9Ohq6sLgiDgP3/xC3wuGsVZl12G/8/jwY5vfhMv\nXnllzgqQP/jBD7Bt27YpM3wTQopXUId+c3MzmpqaYLfbkUgksH//fkSjUXAcB5vNVqoykgVo7969\nuPnmm/Hggw/i61//OgBITbITKQ4cQNN3v4umY8cw1t+PTy9ahN/t3QuTyQSVSoUNGzbAarXi7bff\nxhtvvIGNGzeW+3EIWXgKHbtss9mYQqHI+jKZTLKMi5bTLB6NVIETJ06w9vZ2tmzZsrxzoLL84x+M\ntbUx9qlPMebx5MxXGR0dZc8++yy75ZZb2DnnnMMWLVrEzj//fLZ27VqWTCZL+BSEzF1yvTtnNc9F\nEATwPI9EIoGmpqaqmt+SQfNc5p5EIoGNGzdibGwMjz32GM4+++z8J46OAj09wD33ABs2APfeC0xY\nYyj3I6NYuXKlNC/LYrHkrQURstDJ9e4sqOHZ7XZj+fLlGBkZQUtLC2w2W1UGFjL3HDx4EAaDAZdc\ncgmeeuqp/IHFZkunwFep0ksJh0LAT34ybWABgJNOOglLliwBkB4Q4KHsxoSUVEHBpbu7G4wxqhEQ\nWT322GMwGo2477774Ha7sWhRnq7A4eH0nJWDB4EjR4BzzwUuvrig++zevVsaEDBVBm5CSPEKahZz\nuVywWCx5Z+R7vV60trbKWrhiULNY9Ttx4gQ6OzsRCATw+OOP45JLLsk9ibH00sJbtwKnnQb89a/p\ndVWCQYACBCGyk+vdWVBwicfj6OnpQTweh8lkktZ0YYzBYrEgEokUXSC5UHCpbsPDw9Korcceewxn\nnXVW7klDQ8CWLcDf/55epGv58nTTmMdDgYWQEqlIcJlqboBCocDoR3mbqgEFl+p14MABXHPNNWhp\nacG///u/5zaDHT8O7NiRzgfmcAC33QbkayojhMiuYokre3t7897Y4XAUXRgy/+3Zswff+c538JOf\n/CT/fJPnnkvXTs47L50PTMaV8Qgh5VNQcGlvb590jftkMilLgcj8tHnzZuzbtw/Dw8PYt28fLr/8\n8uwTUilg27Z0DrD77wesVlq4i5A5rOj1XKoVNYtVj+PHj+Pcc8/FP/7xDwAT5pgwBvzyl8Ctt6YT\nTTqdMxpaTAgpjYqu51Isv9+PSCSCrq6urP2iKMLr9UKpVGJoaAgAcs5xu93QaDRSnrNqGqFGch0/\nfhzXXnuttJ01x+T114GbbwZefRXYsweYWJshhMxZZQ0u4XAYPM8jGAxCq9XmHN++fTucTqe0bTAY\nsoY4cxyHNWvWYPXq1QDS/TyBQGDSpjpSWR9++CE2bNiAEydO4Pnnn8ctt9wCj8cD5Sc+ATz4IPD9\n76drLD4fcOqplS4uIURGFWkWczgcEEUR3d3dWfsbGhrgcDiwefNmAIDVagUAqQlFrVZnZWYOh8Nw\nOp3o6+vLuQc1i1XWBx98gJaWFixatAh79+7FKZnsxLFYusN+8WKguxv453+ubEEJIVkqkv4lHA5n\nbfM8D4PBgA0bNsiyEmUoFJICCwAMDQ1h5cqV0r0mUqlUCIVCRd+XyOv999/H1VdfjVNPPRU+ny8d\nWG68EfinfwL+5V+ATZvSa9lTYCFk3ioouIxvsgLSi4RFIhG0traiubm56MIsGzfslOd51NTUYOvW\nrQDSSQ3HL1AGQErhQUssV49jx47hqquuQm1tLfbs2YOTTz4ZePrpdJ/KG2+k57D87nc0EoyQeW7a\nPpdUKpVVTcr3Ik+lUohGo7IUKJVKwefzobe3Nyu5oCiKOYuVZYJNIpFAbW2tLPcns3f06FH867/+\nKz796U/j5z//ORYdPQrccgvwxBPAhRcC0Wg6dQsljSRk3puy5hIOh2GxWKBUKqUmKKVSmfNlsVjQ\n0tIiS4Hq6urQ2tqKvr4+bN68GV6vFwDyJhrMBJuJNRpSfu+++y6uvPJKnHfeeXjkkUewqK8vHVDG\nxoDnn09nMLZYKCcYIQvElDWX5uZmqbnLbrcjHA6D47iszh61Wg2lUgmj0Vh0YURRzAoibW1tsNvt\naG1thVqthiiKOecDmLTWsmnTJqmpTalUorGxEatWrQIA9Pf3AwBty7B95MgRfOlLX8J5552Hh3fs\nwEk33oj+YBDYuhWr7rjj4/O//W2s+ujnW03lp23aXsjb/f392LVrF4DsromiFbKyGMdxs1+WbMJ1\n7HZ71r5gMMgUCgVLpVLSvp6enqx9KpUq5zNmsznvPQp8NDJLqVSKXXrppay1tZWN+nyMnXMOY7fe\nyti771a6aISQWZDr3VlQh/7ECY3jTRxJNk1Ay9nX1NQEu92eVQsJBoOwWCzSPpvNhkAgIB0PhUKw\n2+0zvi+RlyiKMJvNuGz5cvQMD6PmrrsAvx944AHgzDMrXTxCSAXNap7L4cOHs7aTySRsNhsGBgam\n/FwsFkMoFEJPTw+SySQcDgeMRqO0mmXmOJBOya5QKLB9+/asa2Rm6AuCAJVKlTV0OevBaJ5LSSWT\nSZhNJtyiVuP6gwehuOkm4O6702uuEELmrIqt56LX63P6PjIFopT7C8Pw8DC+ccUV+OHRo/jcJz4B\nxcMPA3p9pYtFCJFBRYKLwWAAkO7cV01ILuhwODA4OFh0geRCwaU0/v7OO9ip0+G7iQTOdDigcDiA\nU06pdLEIITKpSOJKnueRTCZRV1eXc4xS7s9/w3/6E4TVq3FjbS3O/OMfobjookoXiRBSpQrq0G9u\nbp50sqRGo5GlQKQKjY5i5N57UXPppRAvvRTnvfYaBRZCyJQKahbLTKrctm0bdDqdFFBm2qFfTtQs\nJpMXX8SH11+Pgy+8gD+2tuLmBx+sdIkIISVUkT6XmprJKzrUoT/PHD8OfOlLYDyPwZoa/Lq9HXfc\nd1+lS0UIKbGK9LnU1dXhZz/7Wd4bOxyOogtDqsTBg8BNN+HdP/8Zi0dHsXx0FLe88EKlS0UImUMK\nCi6dnZ20MNd89uGHwH33ATt34p077gAfi+ErAP4E4CHGsKvCxSOEzB2znkQpCAJWr16NeDyO+vr6\nUpStKNQsVqCBAeCmm4D6erx8++1ovv56/NMnPoHbXnoJ3Y2NePzpp/MmDyWEzC8VWSwMAMxmMzQa\njZR2JRqNwmAw0Joqc9WxYwDHAf/6r8C2bYjcdReuuPZaOJ1OPPnf/42AxUKBhRBSsIKCS1tbG5RK\nJXw+nzTXpaWlBR6PBxaLpSQFJCX0X/8FNDYChw8Dhw7hmfPOw7orr0R3dzeuu+466WdNgYUQUqiC\n+lwEQZDWqx+/kJdOp6uqYchkGu+9B2zbBvT2Aj/9KXDNNejr68N1112H3bt3w2QyVbqEhJA5rqCa\ny8SVIDNisZgshSFlsH8/cNFFgCimF/G65ho8/vjj+MY3voHHH3+cAgshRBYF1Vyam5ul1PiJRAL7\n9+9HNBoFx3Gw2WylKiORQyoFdHQATz4JdHcD69YBAH7xi1+gvb0dTz31FHQ6XYULSQiZLwoeLWa3\n26WlhzOMRqPUXFYtaLTYOE8+CdjtwNq1gMsFfNRftnPnTtx3333o6+vD5z//+QoXkhBSDSoyQz9D\nEATwPI9EIoGmpiZpPZZqQsEFQCIB3H478MwzgNcLfLRkNZBeF2fnzp0IhUKUF44QIqnIUGS3243l\ny5djZGQELS0tsNlsVRlYCIDHHwcuvDBdSzl0SAosjDHcddddePjhh/HMM89QYCGElERBfS7d3d2y\nRDW/349IJJJ32WS32w0AGBgYQFNTE9rb23OOazQaaXBBa2trUWWZd955B7jlFiAWA/buBS6/XDo0\nNjaG22+/Hb///e/xu9/9Dp/61KcqWFBCyHxWUHCx2+2wWCx5Z+R7vd5pX/ThcBg8zyMYDEKr1eYc\ndzgcWQEnszhZJsBwHIc1a9Zg9erV0vmBQIBS0gAAY+lgctttwA03ALt2AaefLh0eHR2FzWbDiy++\niKdpUiQhpMQKXua4p6cH8XgcJpMJBoMBSqUSjDFYLBZEIpEZXcfhcEAURXR3d0v7UqkUPB5PVk3F\n6/WC4ziplqJWq7OGQ4fDYTidzryDCRZUn8ubbwLf/jYwOAg8/DCwcmXW4Q8//BDXX389hoeH8atf\n/QqLFy+uUEEJIdWuIlmRx9c2ent7cwpUjOHhYXAcB4vFgmXLlgEAVCoVRFEEkF4FcyKVSoVQKFTU\nfec0xoArrgD++7+Bz342PeN+yZKsU44dOwaLxYKamhr85je/wWmnnVahwhJCFpKCgguQDiqlSLmv\n0WjA87wUWAAgGAxKk/oSiQTUanXWZzJNOyMjI6itrS3q/nPO4CBgs6X7Vk6cAIaG0n0tPp90ypEj\nR3DVVVdhyZIleOSRR3DyySdXsMCEkIWkoODS3t4+af9GMpksujCNjY3S/4uiiN7eXqnGIopiToaA\nTLBJJBILJ7icOAH86Efp+SrbtgGnnALs2wcYDMC4lDzJZBJr167FRRddhO7ubpx00kkVLDQhZKEp\naCiy0+mc9JjcQ1qtViv2798v1WTydUCP74tZEHg+3Z8SCgF/+hPw3e8Cjz0GWCxAMAh89D16++23\nsWrVKnzpS1+Cx+OhwEIIKbuCm8WA9Houmb4QABgaGoLNZsPw8LAshXI4HHA4HFk1GbVanXVPANL2\nZLWWTZs2ZQWnxsZGrFq1CgDQ398PAHNj++hR9H/rW8BTT2HVAw8AN9yA/t/9DnjttfRxn086X6vV\nwmg04otf/CK++tWvSn1hVfU8tE3btF012/39/di1axcAZHVLFI0VwO/3M4VCkffLZDLN+DocxzG7\n3T7pPcLhsLTN87z0/yqVKuvcYDDIzGZz3usU+GjVKxxmTKtlbONGxt56a8pTX331VbZs2TK2Y8eO\nMhWOEDLfyPXuLKhZbPv27ejo6IDP54NOp0Nvby+6u7uh0Wjg9/sLCWh594dCISQSCej1eoiiCEEQ\nsHfvXum4zWZDIBDIOj+zaNm8k0wC3/oWsGkT8MADwJ49OSPBxnv++eexatUqdHZ24o477ihfOQkh\nJJ9CIpFWq5X+3263s3g8zhhjbGhoiFmt1mk/z/M8c7lcTKvVMrVazVwul1QzSSaTeWtEE6/rcrmY\n3+9nLpeLeb3eSe9V4KNVj7Exxnp7GTvnHMb+7d8YS6Wm/cjAwABbsmQJe/TRR8tQQELIfCbXu7Og\nSZRms1masOj3+yGKIjZv3gwAaGhowODgYCni36zMyUmUf/sbcPPNwMsvpxNNfulLU55+/PhxXHHF\nFRgYGMDFF1+McDhMM+8JIUWpyCTKuro6mM1mKBQK+Hw+6PV6AEAkEpl0ITEyA2Nj6WBy553pmfaP\nPQaceuqUHwkGg7jtttvw5ptv4sSJE+B5HjabDb5x81wIIaRSCgouXq8XFosFSqUSdXV16OrqgtVq\nBQB0dHSUpIDz3ssvpydDfvAB8PTT6UzGU3j11Vdxxx134IUXXsCPfvQj7Ny5E0899RQMBkPW0tOE\nEFJJs1rPZTxBECCKYtWtYlj1zWLHjwNuN3D//cD/+T/pGssU81FGRkbwgx/8AA8//DA6Ojpw6623\n4tRTT4UoirDZbPB4PNQkRggpWkWaxTIOHDggJak0GAxVF1iq3sAAsHkzsHQpEImk84JNYnR0FLt2\n7cJdd92Fr3zlK3j++efx6U9/WjquVCqpKYwQUnUKCi6pVArNzc05SST1ej3C4fDCScEyW++9l66l\nPPpoOoXLtdcCUyT8fPbZZ3HrrbfitNNOw69//WtpCQJCCKl2Bc1zyazX0tvbi0gkgkgkgu7ubiQS\nCVq0azp9fcBFF6UX83r+eeDrX580sLz22mu49tpr8fWvfx1bt27Fs88+S4GFEDKnFNTnMnE9lQxR\nFLNWh6wGVdPnMjwM3HEH0N8PdHcDX/nKpKcePXoULpcLP/nJT3DLLbegvb0dZ555ZvnKSghZ8OR6\ndxZUc9FoNBgZGclbmPGJK8fPol+wGEsPKb7wQkClStdWJgksjDE89thjuOCCC/DSSy+B53ncc889\nFFgIIXNWQTUXr9cLl8sFjuOkZpqBgQF4PB50dnZCo9GAMQabzYaBgYGSFXomKl5zWbECeOUV4JJL\ngN/+VspYPFE0GsWtt96Ko0eP4sEHH8Tl49a8J4SQcpPr3VlQcKmpmVlFR6FQYHR0dNaFkkPFg8ul\nl6ZXiATSKfEnjOh666238L3vfQ+//e1v8f3vfx833ngjpcYnhFRcxWbo/+xnP0NdXd2k54iiWPSq\nlPNCpqYyYRGvDz74AD/+8Y/hdDpx44034qWXXpry+0kIIXNRQcGls7Nz0pUoyQS7d6dn3ns8gFIJ\nxhh+85vf4Lvf/S4uuOAC/OEPf8D5559f6VISQkhJzGqGfiwWQzQaBZCeRDl+Ua9qUfFmsXH+8pe/\n4Pbbb8frr7+O+++/H1+ZYsQYIYRUUkWaxWgSZWESiQTuuece7NmzB3fddRe2bNmCk08+udLFIoSQ\nkisouIyfRFlfXw8gnRHZ6XSitbU1a2GvhW716tV45plncM455+C5556DVqutdJEIIaRsaBJliVxy\nySU4dOgQAMBisVD+L0LInDCnJ1H6/f4pR5RNddztdiMQCMDr9cLr9c6w5OW3dOlSAKBU+ISQBamg\nZjG73Q69Xp93EuW2bdtw4MABMMbQ1dWVd1RZOBwGz/MIBoN5m4mmO85xHNasWYPVq1cDABwOBwKB\nQFWOYNu9ezelwieELFgVmUTpcDggiiK6u7sLOj6xWS4cDsPpdEpLL08sQ7WMFiOEkLliwU2inDhC\nDQBUKhVCoZDs9yKEEFKcOTOJMpFIQK1WZ+3LNDeNjIzQMGhCCKkiBXXod3R0THpsx44d0v+3tLTM\nvkSTEEUxZzRaJthU0yg1QgghBQaXyYTD4SkDjxzydYpngsrEGg0hhJDKKqhZbCKv14uenh7wPA/F\nFMv1ykGtVkMUxax9me3JmsQ2bdqEZcuWAUgHp8bGRqxatQoA0N/fDwC0Tdu0TdsLeru/vx+7du0C\nAOl9KYeCc4vFYjH09PRkzd1YsWIFDhw4MOM0+3KNFguFQnC73di3b1/ONWi0GCGEFK7skyi9Xi8M\nBgP0er0UWJxOJ5LJJKLRqJQOZiamK/hkx202W9YEzVAoBLvdPuP7EkIIKY8pay75aik2mw02mw1G\nozGrFsHzPHQ63ZQ3i8ViCIVC6OnpQTKZhMPhgNFoxIoVK2Z0HEjP0NdoNBAEASqVCps3b87/YFRz\nIYSQgpVlJUqtVot4PA6lUiklp8yYLM9YtaDgQgghhStLs9jQ0BAikQiam5vhdDqxY8eOvLnFAEy6\nnxBCyMJTUIe+x+OBx+OBRqOB3+/H2NiYdGz58uV49dVXS1LI2aCaCyGEFK4szWKTEQQBPT09CAQC\n0Ov1UKlU8Hg8WcGm0ii4EEJI4SoaXMbz+/3gOA6HDx+e8VDkcqDgQgghhaua4JKhUqmQTCbluJQs\nKLgQQkjhKrJY2FTi8bhclyKEEDLHyVZzqTZUcyGEkMJVXc2FEEIIyaDgQgghRHYUXAghhMiOggsh\nhBDZUXAhhBAiOwouhBBCZEfBhRBCiOwouBBCCJEdBRdCCCGyW1SJm/r9fkQiEXR1deUcy6w0mVmI\nbPwCZTM5TgghpPLKWnMJh8Nwu93weDxIpVI5xzmOg16vx/r169Ha2oqhoSEEAoEZHyeEEFIdKpJb\nzOFwQBRFdHd3Z+2fuHRyOByG0+lEX1/fjI6PR7nFCCGkcPMutxjP8zn7VCoVQqHQjI4TQgipHlUT\nXBKJBNRqddY+pVIJABgZGZn2OCGEkOpRNcFFFMWsJi8AUjBJJBLTHifVr7+/v9JFKJu58qyVLmc5\n71+qe8l9XTmuV+mfK1BFwSVTCxkvEzTUavW0x0n1q4Z/8OUyV5610uWk4FKa61X65woAYBXAcRyz\n2+1Z+6LRKFMoFJPum+74RFqtlgGgL/qiL/qirwK+tFqtLO/5isxzyUen0+XUThKJBEwm04yOTzQ4\nOJh3v9frBQBEo1FwHIf6+vpii04IIfOe3++HSqUCz/MwGo1YsWLFlOdXpFmMTTLMzWazZc1bCYVC\nsNvtMz4+nVgsBoPBgNbWVlgsFlgsllmUnhBCFhZBEODxeNDc3IwVK1Zg+/bt036mrPNcYrEYQqEQ\nenp6kEwm4XA4ciJgZga+IAhQqVTYvHlz1jWmOz6VQCCAYDCI7u5uiKKYNdOfEELI5EZGRlBbWwuX\ny4WGhgZcc801U55fkUmUcphtCplUKoW6ujr4/X709vZi7969ZSszIYRUWjHpt8b/gT6dqulzmalw\nOAye5xEMBqHVanOOcxyHNWvWYPXq1QDS2QACgQDWr18PAKirqwMA+Hw+/OxnPytfwQkhpIKKfXcC\nkP7farXC5/NNeb85W3OZbQoZIB2d7XY7amtry1ZeQgipBrN5d/I8j2QyiebmZgiCgIaGBoyNjU15\nn6qZ5yKHmaSI8fv9sNlsqK2tpdQxhBCC6d+d0WhUOkcUxbw1n4nmVXCZLkUMz/Ow2WzQ6/VQq9Vw\nu92VKCYhhFSV6d6dra2tUCqVCAQC8Hg86O3tnfaac67PZSrTpYjR6XQ0OowQQiaY7t1ZW1srde6P\n74OZyryquVCKGEIIKVwp3p3zKrio1WqIopi1L7NNnfeEEJJfKd6d8yq4FJoihhBCSGnenXM2uMw2\nhQwhhCxk5Xp3nnTPPffcM+tPV0AsFsN//ud/4he/+AWi0Shqampwxhln4JxzzgEAGI1GPPHEE3jr\nrVnjtmcAAAOwSURBVLfwxBNPYOnSpbjuuusqXGpCCKmscr875+wkSkIIIdVrzjaLEUIIqV4UXAgh\nhMiOggshhBDZUXAhhBAiOwouhBBCZEfBhRBCiOwouBBCCJEdBRdCCCGyo+BCiAy0Wi1isVjeYx6P\nBw0NDaipqUFNTQ3UajUMBgMaGhpgMBjgcDgmvW4oFEJbWxsAwOVywWq1wmAwwGAwZKXqIKTqMEJI\nUaLRKFMoFMxut095nkajYTU1NVn7/H7/lJ+1WCwsHA4zm83GvF5vzuf8fn/xD0BICVD6F0KKZLFY\nEA6HIYrilOuKa7VaHD58GKOjo1n7VSoVRkZGcvYDQENDAwYHB1FTU4NkMom6urqsz2m1WkQiEfke\nhhCZULMYIUWKxWJwOp0AAK/XO6traDSanH1+vx8mkwmpVAoAoNfrs46nUinE4/FZ3Y+QUqPgQkgR\nPB4P2trapCVge3p6Cvq8y+XCyMhI3s95PB7Y7XbU1dXBaDRCpVJJxwRBAAAYDIYiSk9I6SyqdAEI\nmcs8Hg/2798PIL0ehsfjQSwWw4oVK/KezxiDwWCAKIoQBAFKpRI+nw+rV6/OOi9zvLGxEQDQ19eX\nddxut0OhUIDjuBI8FSHFo5oLIbMkCAI0Go20DKzNZgMwde1FoVAgEolgcHAQoijC6/XCYrHAbDZn\nnefz+WCxWPJew+PxIBwOo7e3NycoEVI1KjyggJA5q6OjgykUCqZSqaQvhULBFApF3vPzjRZjjDGO\n45hCoWAej0fap9frWTwezzk3MzItEAjI9hyElALVXAiZpUAggLGxMSQSCelrNh37mX4TnucBfNyf\nsmzZsqzzBEGA0WhEKBTCNddcAyDdZ0NINaLgQsgs+P3+nNFbwMyaxibKBBOtVitde+PGjVnniKII\ns9kMv9+f1RTm8XgKLjsh5UDzXAiZBa1WC5PJhO7u7pxjer0esVgMwWAQzc3NWZ+Jx+NZc2EEQYBe\nr8fZZ5+NaDSK2tpaNDQ0gOd5qS8HSM+licfjWQFNEATE43EMDg6W6CkJmT0KLoQUQBRF1NfXY2Rk\nBIwxaLVaKSjwPA+r1Zo196SrqwtKpRJOp1PaX1dXB41GA1EUAQAmkwlOpxO1tbUQBAFbtmzBvn37\npGv4/X5YrVYoFAqM/3VVKBTQ6XQYGBgo09MTMnMUXAghhMiO+lwIIYTIjoILIYQQ2VFwIYQQIjsK\nLoQQQmRHwYUQQojsKLgQQgiRHQUXQgghsqPgQgghRHYUXAghhMju/weNCL6ggMZbdgAAAABJRU5E\nrkJggg==\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x656c250>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 40
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"fig, ax = plt.subplots(1,1, figsize = (7.5, 7))\n",
|
|
"ax.plot(1., 1., 'k', lw = 2)\n",
|
|
"ax.plot(1., 1., 'r', lw = 2)\n",
|
|
"ax.legend(('True', 'Predicted'), loc = 3, fontsize = 16)\n",
|
|
"Utils1D.plotLayer((np.exp(mopt)), mesh.vectorCCz, 'log', ax = ax, **{'lw':2, 'color':'r'})\n",
|
|
"Utils1D.plotLayer((np.exp(mtrue)), mesh.vectorCCz, 'log', showlayers=True, ax = ax, **{'lw':2})\n",
|
|
"ax.set_ylim(-600, 0)\n",
|
|
"ax.set_xlabel('Conductivity (S/m)', fontsize = 20)\n",
|
|
"ax.set_ylabel('Depth (m)', fontsize = 20)\n",
|
|
"ax.text(1e-3, 10., '(b)', fontsize = 30)\n",
|
|
"fig.savefig('obspred_dc1d_mod.png', dpi=200)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": 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u5QAAeMnQR6FbltWyP9tRH9aFQkGrq6st6zln0/WcYA4Gg13L29mquxF8cXFR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\nXNby8nLDqHgAGHe5XE6SdHR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|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x51b9310>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 47
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": []
|
|
}
|
|
],
|
|
"metadata": {}
|
|
}
|
|
]
|
|
} |