From 90957676b4b07df31465edfd4bbb90afa26b3658 Mon Sep 17 00:00:00 2001 From: seogi Date: Mon, 22 Sep 2014 16:56:44 -0700 Subject: [PATCH] 1D DC examples --- notebooks/DC_schumberger_FWD.ipynb | 397 +++++++++++++++ notebooks/DC_schumberger_Inv.ipynb | 753 +++++++++++++++++++++++++++++ notebooks/obspred_dc1d_dat.png | Bin 0 -> 15821 bytes notebooks/obspred_dc1d_mod.png | Bin 0 -> 7978 bytes 4 files changed, 1150 insertions(+) create mode 100644 notebooks/DC_schumberger_FWD.ipynb create mode 100644 notebooks/DC_schumberger_Inv.ipynb create mode 100644 notebooks/obspred_dc1d_dat.png create mode 100644 notebooks/obspred_dc1d_mod.png diff --git a/notebooks/DC_schumberger_FWD.ipynb b/notebooks/DC_schumberger_FWD.ipynb new file mode 100644 index 00000000..20430bce --- /dev/null +++ b/notebooks/DC_schumberger_FWD.ipynb @@ -0,0 +1,397 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:4c81eac7c432b351e702d9aa1f39f2d14f9487903d874e4c95342ece5a8bb266" + }, + "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": 1 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "DC Forward Modeling of Schlumber array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we test the accuracy of DC forward modeling using analytic solution." + ] + }, + { + "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": 113 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 114 + }, + { + "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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+ "text": [ + "" + ] + } + ], + "prompt_number": 115 + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Step2: Generating model" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "sighalf = 1e-2\n", + "sigma = np.ones(mesh.nC)*sighalf" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 121 + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Step3: Design survey: Schulumberger array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "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": [ + "matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 140 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ntx = 16" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 141 + }, + { + "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": 142 + }, + { + "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": 124, + "text": [ + "(-600, 600)" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 124 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "fig, ax = plt.subplots(1,1, figsize = (5,3))\n", + "mesh.plotSlice(sigma, 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(-600, 600)\n", + "ax.set_ylim(-200, 200)\n", + "ax.set_title('Survey geometry (Plan view)')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 144, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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TIroWfBOQh62R1+12F9gSIqL58E1AvnnzputCpoqicJFTIroWpBtD9jJsIdPz8/OpFjl9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\nHTe9yH7HYq/XQ71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+ "text": [ + "" + ] + } + ], + "prompt_number": 144 + }, + { + "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)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 129 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "survey = DC.SurveyDC(txlist)\n", + "problem = DC.ProblemDC(mesh)\n", + "problem.pair(survey)\n", + "problem.Solver = SolverWrapD(EMUtils.Solver.Mumps)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 133 + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Step4: Run DC forward modeling" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "data = survey.dpred(sigma)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 134 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$ \\rho_a = \\frac{V}{I}\\pi\\frac{b(b+a)}{a}$$" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "appres = data*np.pi*b*(b+a)/a\n", + "\n", + "\n", + "fig, ax = plt.subplots(1,1, figsize = (6, 4))\n", + "ax.semilogx(np.r_[100., 500.], np.r_[100., 100.], 'k--')\n", + "ax.semilogx(abhalf, appres, 'k.-')\n", + "ax.set_ylim(90., 120.)\n", + "ax.set_xscale('log')\n", + "ax.set_xlabel('AB/2')\n", + "ax.set_ylabel('Apparent resistivity ($\\Omega m$)')\n", + "ax.grid(True)\n", + "ax.legend(('True', 'simpegDC'), loc = 1, fontsize = 14)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 148, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 148 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/notebooks/DC_schumberger_Inv.ipynb b/notebooks/DC_schumberger_Inv.ipynb new file mode 100644 index 00000000..5884f5ee --- /dev/null +++ b/notebooks/DC_schumberger_Inv.ipynb @@ -0,0 +1,753 @@ +{ + "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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\n4UGDBnEylGxQYoOIRqOBWCxuNf0IoOkMCW2wMpxsIG4oW0L/WAxdL83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+ "text": [ + "" + ] + } + ], + "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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A8PDDd/PBB4/QrVs7QkMPNHSUKoyZzdW1LV26tGbixJ5MmRLJ1atlvP76g3z9dTA9e75H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\nuzfnjxzRz1c0GiZ+9hnNWrV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+ "text": [ + "" + ] + } + ], + "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": [ + "" + ] + } + ], + "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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wkBIRkdiASPHJP7r5tP34v3Smi3bvumvYnb6KxWLHZiXr6+uo1WqoVqtdm56MGgcbERGR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\nr9eLUqmEra0teDyeof9Kufv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+ "text": [ + "" + ] + } + ], + "prompt_number": 213 + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Step3: Design survey: Schulumberger array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " " + ] + }, + { + "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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+ "text": [ + "" + ] + } + ], + "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": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "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": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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0+PGPAbO50hESlV4p7puqYnfUaDTYtGkTEokEIpEILBYLVqxYUdpx6Ykq4Hd9\nv4M34kX4/Aik2++C6kU70teHsOlX2b4OTCBEoys6iXR0dCiTR9lsNlgsFng8nhFzsRPNJDfGbsT3\ntn0Pt9Ztw3cu/CR6H9NgaEs70q9p8NvfVjo6ouo3oSeRjRs3Ih6PAwA8Hg9MJhM0bGZBM1SgN4Af\nPvhDrJK2wbH0eJx2WrYvA5BthsrBGIjGV/SkVDnBYBB6vb4qKtOJJmtjz0b8aNs6fOyBB/HwLiMe\neww4/njgssvYDJVoIop+ErFYLKivr4ff74fH40EkEhl1UEaianbd47+G9+712Nn2INwOI7q6sgkE\nyCaO9nYmEKJiTWjYk1yfEY1Gg1gshh07dkCSJFit1mkLsFhsnUXF+Nbt1+PX0Z/hjH9sw+9+XoOj\njqp0RESVU9bWWUA2eeTqQBYtWoS6ujoEg8GiP2+zjT1ngt1uz1uWZRk+nw+RSAQ+nw+ZTGYi4RIp\nBgeB0674Ba7v+TmuMz2IrVuYQIhKoSzziUQiEcTjcbjdbgzlJo3eQzgcRn19fd52i8WitAjLZDJo\namoqOPQKwCcRGt3ddwPn3fBTDJlvwGNND+KkY46pdEhEVWHGzCditVphtVrhdrsLbs9kMkoP+Jxo\nNAqdTqcsq9VqhMPhcc9FlPP668A3vwmE3/PhwDPa8KS7C0erj650WESzyphJZDiXy1Wwx7rBYJhy\nEOFwGMuXL89bJ8vyiObDOp0OfX19qK2tnfI5aXZbuhR45BFg/rIN0HzxRjx0cRc+Pv/jlQ6LaNYp\nuk7EYDAU7LFunmJ33kgkUrCupBpGBqaZ5913s9PQPvkk8OFnr0HymJtwwtNMIETTpaI91hOJBHQ6\nHebPnz9i24IFC5TWYDlMLDSWF14ATjkFeHt3EqpvnAicfg0OEkfhVz8/oNKhEc1aRXc2zPVYz9VL\neDwe1NTUTKnHejQaRTKZVJJTOp3Gpk2bYLVaodfrCyaNsYqyVq1ahYULFyrx1tbWYsmSJQCArq4u\nAODyLF32ervwG//7sP2gDw/u+in2je/GO++/h7drHsSah5247NDLqipeLnO5Esu59yUd87DYeXTt\ndrsQQgiXyyUMBsOI9UXNxStJE9puNpuV9/F4XDgcjlE/O4FLoVnk7beFOO+CXeJjX24Th7ccJRrb\nG8Vft/9VnHXbWQJXQVj8llHnBCea60px3yxLj/VYLIbW1lZIkgSv14tIJJK3PZPJKNuvvfZaJBIJ\nAEAgEICGw/7jAAAamklEQVTP50NnZyf8fj8CgUDRyZFmv7/8ReC4rwbx56NPxHENd+DP/3MnOuwd\nOP6Q47F5+WbYT7DPudkDicqNPdZpxhECWHPDNlz3vBdHHr0LfvsG1BvqR0xFS0RjK+ukVDl9fX2Q\nZRkNDQ2IxWJYtGjRlAIoFSaRueGxeAz2tma8KV7Curqr8e36FVBJExp4gYj+o6zDniQSCRiNRphM\nJng8HgBAW1sb7rzzzikFQFSMeDKOZb/9Os4ILMPCD87GGz94EVeceS4TCFGFFf0b6HK50NLSgmQy\nqTx9bNy4EevWrZu24Ihef/t1/O+938B//eoUPNT5SbSd+BIe/8U3oDl430qHRkSYYBPfPXuVAxjR\nl4OoFHYP7capgVPR93of9tv5cRy97Sn86XdGZch2IqoORT+JpFIpbNq0KW8kXZ/Px5kNqeTe+eAd\nNLQ34JlX+rFb7Ma7+/4Dx112JRMIURUqumJdlmXYbDal+S2QfTqJRCJVUbnOivXZ4bW3X8M5t5+D\neYMn4NG+1wHjA8C/LPjKYAh/vIN/sBCVUkVaZwWDQWVwxBUrVkCtVk8pgFJhEpn5Xtz+Ipb9fhmO\n3L4K/77jBzj8mAyePMSJ2lf8ePA+DWcbJCqxaR0Kfk8OhwORSAQ7duyY0gmJCuka6IK93YEjnvNh\n3r8uRPfTwF57aeB0tsN/H6erJapWE6oT8Xq90xkLzVG3PXsblt/hwLx7bscXtRfigQeABQs43znR\nTFB0EvF6vdBoNBgcHMxbf+mll5Y8KJobhBD4yUM/wXfu+R7E7x7EVRdYcd11wN5FPx8TUaUVXSdS\nX1+PZDKJaDQKg8EAjUYDrVaLSCQy7vS45cA6kZll1+5dcN3txtZn+7Dr5rtx581H4POfr3RURHNL\nWSvWtVptwYr0QCBQFfN8MInMHJn3M2i4oxF/e2E/HNJ1B+7qPAhHc9ZaorIra8W60+lES0vLiPWl\nmB6X5o6XMy+j/uYvY0ffafjie7/ETV174wDOGUU0Y024iW+14pNI9Yv9O4Yzbz4HOx/6P3i/8B14\nvRI48C5R5ZT1SYRoKu576T6suOMCSPfegN9/146zz650RERUCkwiNO1ueLoNa+75IbRb/4Stv/0c\nTjih0hERUalMOokkEgnU1NSUMhaaZYbEEL59z5UIPHonFr3wKP58jxE6XaWjIqJSKrqfiM/ny1vu\n6OhAfX095xOhgt7/8H2cfdPX0XbfI7hg1+PoupMJhGg2Krpi3eFwoL29fcR6nU5XdBNfm82GUCiU\nty4ajSKVSiGdTiMUCsHj8ShPOLIso7OzEyaTCdFoFE6nc9SxulixXj2OW3sR+ve9E+L9A7Du2Bia\nL/9YpUMiogKmvWLd4XAo84X09PTgzDPPhBBCObEsy9AV8edlJBJBPB5HJBIZsa2urg4DAwOYP38+\nkskk7HY7enp6lPPn3lssFjQ1NRVMZFQ9hoYE4qp7IPYZBPYZxA2J/4dm8P+MaLYaszirvb0dGzdu\nhBACOp0OarUaarUa8+fPR01NDRobG0c8WRRitVrhdDoLbsslECDboVH6T5vPaDSal6DUajXC4XDR\nF0blt3MnsPjbLRiSPgAAHJC24FGPv8JREdF0GrdiXa/XIxQKobW1FWvXri15ALkEAgB+v1/p0Jgb\nbn44nU6Hvr4+1NbWljwOmprt24ElTfdC/vT1iKx8DBfe+CM86vXj2MM5eiLRbFZ066zREkgmk5ny\nnCKJRALBYBD19fVYunQpAFTFUCpUnOeeA8467+9IfnUVQhf9EacdeyJe/hmLsIjmgqJbZ+UMDg4q\nr0wmA4fDMeUgampqsGbNGtTU1KC+vh5A9qljz/nbmViqz733AkvOHMSQ4yv4xTnX4LRjP1fpkIio\njIp+EolEIrDb7SNu7NIUxq3Itb5as2YNgGzdid1ux8DAAAwGQ8GkMVZR1qpVq7Bw4UIA2al7a2tr\nsWTJEgBAV1cXAHC5RMsPPtiFYBC48w9fwCe/+z9YMHgcPvHWJ5BT6fi4zGUuj1zOvR8YGECpFN3E\n12g0oq6uDo2NjXkV3l6vF1u3bi3qZCqVCkNDQ8pyJBJBKBTChg0bAGQr0202mzJ7osViUVpnybKM\n5uZmbNmypfCFsIlv2XzwAfC//ws8/TTwhau+h2fSDyN8QRj77rVvpUMjogko+9hZGzduHLGura1t\n3M/FYjGEQiFIkgSv1wubzQar1Qqr1Yp0Oo1AIACdTodQKJTXDDgQCMDn80Gv16O7uxuBQGAi4dI0\nePNNoLERUKuBNTcF8d2Hb0V3UzcTCNEcVfSTiM/ngyRJuOKKK/LWNzc3Y/369dMS3ETwSWT6vfAC\ncM45gN0OrPzms7DdZsUD5z0A0xGmSodGRJNQ1kmpLBYLotEoJEmCyWRSThyLxTiz4Rxw//3ABRcA\nPh9wtn0HFgcW45ql1+Dck86tdGhENEllLc6Kx+NYu3btiBPKsjylAKi6CQFcfz2wfj3whz8Ap372\nQ5x5mwONJzQygRBR8Umkubm5YF+RxYsXlzQgqh67dgHf+Abw+OPAE08ACxcC37p/DfZR7YP11soX\nYRJR5U14ZsO+vj7IsoyGhgbEYjEsWrRoumKbEBZnlVYyma1AP+AAYPNmYP584JZnbsFPHv4Jnl79\nNLT7aysdIhFNUSnum0V3NkwkEjAajTCZTPB4PACyrbU4FPzs87e/AaeeCpjNwJ/+lE0gT//raXxn\n63fwxxV/ZAIhIkXRScTlcqGlpQXJZFJ5+mhra8O6deumLTgqv1AI+MIXgObmbCX6XnsBr739Gpa3\nL8emczbhxMNOrHSIRFRFiq4T0Wg0WL58+Yj1e/Zgp5nr178GfvIToKMjm0gAYOeHO7G8fTlWL1qN\nr3zyK5UNkIiqTtFPIqlUCps2bUImk1HW+Xy+ESPt0syzcyfwqU8Ba9YAn/wkcPLJ2fVCCFx+3+U4\n7MDD8P0zvl/ZIImoKhVdsS7LMmw2GxKJhLJOo9EgEolUReU6K9Yn55VXshXo/f3Af0abgd0OtLcD\nv+n+DX7d/Ws8cckTOHi/gysbKBGVXFk7G+YEg0Flro8VK1ZMeRj4UmESmbiHHgLOPRe4/HLg4Yez\nHQotlmy9yLOZh2HvsOPxix+HQWeodKhENA3KmkQcDgcikYgyOGK1YRIpnhDAddcBGzYAt9wC1NcD\n6TTgdAJ+PzAo/ROf2fQZ3PzVm2Ez2CodLhFNk7L2WE+lUvB6vVM6GVXeO+9kk8WLLwJPPpntQAgA\nGk22COvdXe/iazd9Dd/57HeYQIhoXEVXrHu9Xmg0GgwODuatv/TSS0seFE2PeBz47GezzXYfe+yj\nBJIjhEDTXU044dAT8O3PfrsiMRLRzFJ0cVZ9fT2SySSi0SgMBgM0Gg20Wi0ikQgHYJwB7r0XuOgi\n4Ac/AC67DCg0l9i1j1+LO567A49c9Aj232f/8gdJRGVV1uKs7u5urFixAlarNW99btIoqk5DQ8DV\nVwNtbcCddwKf//zIfZLvJbHs98sQ/XcUnz/689i5eyeTCBEVpegk4nQ60dLSMmK9wcCWO9UqnQbO\nPx9IpYCeHuCIIz7atntoNyK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+ "text": [ + "" + ] + } + ], + "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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VmrItGo0Ks9msNBRyOBwplffJuozx6kRG3hvNZrNy7Gg0KlpaWoTNZkvZ3+l0img0mnJf\ny1VM45nse5GL782kR+jo6FAqr+LxuAgEAsLhcIj6+voxN/3JjE4i0WhUaDSatPv29vYKs9mcsm68\nfYWY+39ERBOZq9//trY2odfrlRunRqMRJpNJab00UkdHh9Dr9UKlUgm9Xp9SYZ3kdDqFXq8XGo1G\nGI3GMZXdye1Go1E4nU7lJq/VakUoFFIaGqlUKqHValPO4XA4lFasyQQy8rgajUYYDIZxzzmVmJLJ\nZjz5SCIZDcDodDqVNtFqtRperxerV69GLBaDTqfLuExQpVJheHhYWQ4EAmhqakJzczPUajVCoRDW\nrFmD8vJy+Hw+tLe3o31E+arBYIDP50NFRcWYY3MAOprP+P2ndIpmUqqWlhY0NzdjaGgopaVAb2/v\ntAYVk2UZoVAIZrMZJSUlMJlMMBqN6O/vz7qyioiI8i/jYU/UavWYCqLRw8JnK9nCKzlTYmlpKWRZ\nxuDgYErrhSQmFiKi4jJuEkkOGLZixYpJDxIOhyHLMlavXp3VyUe3fwb+2ZJBr9enTRrpirKSNmzY\noLQhV6vVqKioQFVVFQCgq6sLALjM5Tm5TDSR5Pcl+X5wcDBnx56wTsTpdCISiaC+vh4mk0l5YkgK\nBoPK6JntGbQNH10nAgAmkwnBYBClpaWIxWIwm83o7u5WtiWbv8myjObmZmzfvj39hbBMmOYxfv8p\nnXzUiUxasR4IBOBwONLWfeh0OrS0tEz6BBIOh+H3+9Hc3IyGhgaYzWalKCwSiaCtrQ1Lly5Fd3c3\n6uvrlaeJcDiMQCAAnU6H7u5ubNmyZUwiUy6Ef0Q0j/H7T+kURRJJSlaCy7Ks1GVUVlZO6+S5xD8i\nms/4/ad0iiqJFDv+EdF8xu8/pVN0c6wTERGNlNXMhkRUnDQaTVHNu03FQaPRzPg5WJxFRDRPsTiL\niIgKKuMkUkyToBARUXHIOImsWLGCiYSIiFJknEQ0Gg1aW1thsViwbds27N27dybjIiKiWSDjivVw\nOIwlS5YA+OdwJ5IkweFwZDS+1kxjxToRUXbyNhQ8AESjUQDA4OAg/H4//H4/4vE4NBoN2tvbYbFY\nUFtbO61giIhodsn4ScRgMECv18Pv90On08HpdMJms6G0tBRAYj7heDyOzZs3z2jA4+GTCBFRdvLa\nxFeWZZSWlsLv96O/vx91dXVKAgESk8u3trZOKxgiIppdMk4ijY2NaG9vTxl9d6Tf/OY3qKmpyW10\nRERU1DKuEykrK0tZ7ujoQCAQQH19PWpra9Hb25vz4IiIqLhlXCdis9nSTjyl1WqLYtpa1okQEWVn\nxltn2Ww2ZZ7znp4enHvuuRBCKCeWZRlarXZaARAR0ew16ZOILMvKzIaVlZVK1tJqtdBoNHA4HCgv\nL89LsBPhkwgRUXbyOimVy+VCY2PjtE42k5hEiIiyk9cmvuMlkE2bNmV8MrPZPGad0+mESqWCVquF\nyWRCOBxWtsmyDLfbjWAwCLfbjXg8nvG5iIho5o1bJ2IymbBu3Tql86DBYEi7XyQSwb333jvhSYLB\nIAYGBhAMBsdsMxgMGB4eTvs5m82Gnp4eJZ66urq0lftENLHly5fjv//7v7Fs2TL8+te/hlqtLnRI\nNEeMm0TsdjuWLl2qLO/ZswdbtmwZ8+jj8XgmPUl1dTWqq6tRX1+fcWChUCil0r60tBSBQCDjzxNR\nwhNPPIEXX3wRBw8exJNPPgm73c4fY5QzEyaR0csNDQ1j9tPr9dMOorOzE2q1Gn6/H83NzSgtLYUs\ny2N+LWm1WvT19aGiomLa5ySaD/7whz/gkksugdFoxAsvvICFCxfirrvuKnRYNIdk3NlwvKeI1atX\nTysAk8mkjA6s1WpRXV2Nnp6eouh7QjSbvfnmmzj//PNx9913w2KxwG63Y8+ePWhvb8eVV15Z6PBo\njsg4iRiNRuzatSvnTwHJBJJ8HwqFsHfvXmi1WqWPShITC80Xb775Ji666CK8//77OO644/DQQw9l\nVY/x7rvv4vzzz8eVV14Jm80GAGhvb0c4HMaqVauwYcMGHHvssYAQQDwO/O1vwObNwOAgcMQRwHe/\nC6hUwIcf/vP1u98Be/YACxcCF14IlJQARx6ZWF64EOjoAN55BzjmGOD664HTTgM0msTrmGMASZqh\nfy0qpIyb+Or1ehiNRgDAunXrpjTsu0qlSqlED4VCsNvtSuX5yH3SbZuod7wkSbj00kuxePFiAIBa\nrUZFRQWqqqoAAF1dXQDAZS4X5fKTTz6Jl19+GW+99RaeeuopvPnmmwCgTP62fPlyXH/99Rkd79Ch\nQ/h//+//4fjjj8ejv/0tJFlG1/r1wF//iqqjjkLPoUMYOHgQJ6hUqIrFgCOPRFdpKRCNomrfvsTx\nTjgBOPtsVBkMwNFHo+tvfwOeeAJVf/tbYvvixcA556DqlFOAAwfQFYkAu3ej6qO/z66SEkCrRdWB\nA0A0iq4DBwCVClVf+hJw/PHo+t73gGOPLZp///mynHw/ODgIAHjggQem3zVCZCgQCCjvfT6fsFqt\nor6+XoTD4UwPISRJSlmOxWLC5/Mpy36/X9hsNmXZaDQq7wcGBlK2jZbFpRAV3PDwsHj55ZeF2+0W\nZrNZHHvssWL58uXihhtuEC+99JI4dOiQWLlypQAg1Gq10Ov1Yvfu3RMdUIg33hDDjz8uOs86S/hP\nPlkMV1QI8bGPCaHTCaHVCpF47hDvV1SIVZ/4hHjnhReEeO+9fx5j5crEPiaTENHo2HNMZ/uHHwqx\nbJkSg7Bap/TvRrmVi/tmxkcIhUJCCCHi8bhwuVxCr9cLSZKExWLJ6LMtLS1CpVIJp9OZkpACgYDw\neDzC4/EIp9Mp4vF4yudcLpfw+Xxjto25ECYRKnL79u0T1dXV4oQTThALFy4Un/rUp8SmTZvEww8/\nnPa7HY1GhdVqFdFoVDz88MPik5/8pLjsssvEO++8I8T+/UI884wQ110nxIknCqFSCbFwofizTice\nKCsT7991lxAvvijEu+8mDjbqBn/FFVeIH/zgB6NPmLi5p0sQQojvX3KJ2HXccWJNTY2Ipttnks//\n4ZRThADEn0pLRWxwMKt/O5oZeU0iRqNR2Gw2IUmS0Gg0wul0ilgsNu0AcoVJhIrRvn37REdHh7Ba\nraKkpESo1WoBQAAQ1mx+jR88KN4LBMTvli0TXQsXigNHHy2GjUYhNm8W4otfVH7h/+7oo8Ubb7wx\n9vOjbvBvvfWW0Gq1IhKJZBzC6aefrsT+9a9/XQwPD2f0uddff11cddVVQqtSid8AojTba6cZk4v7\nZsY91kOhEKLRKDo6OjA0NIStW7emTEpFRAn79u1DR0cHbDYbTjrpJHg8HpjNZgwMDGDZsmUAEq0S\nJ+xjJQSwejWg1wOLFgFaLY655hp886yzcNJNN2GFwYBvlJVhoL4eOPVUAEBowQJ86qmncOpHyynU\naqC9PfFfACeccAKuvPJK/PjHP87omu677z688cYbAIBTTz0Vf/7zn1FRUQGv14t9H9WjpIYvsGvX\nLlxwwQVYtmwZjj32WHzpa1/DOgCnT3btNLtkmm3sdvu0M9ZMyuJSiHLuvffeE9u3bxdr1qwRJSUl\nwmKxCK/XK95+++2U/UYWUaX12mtC/OQnQnz2s0IcddQ/6xAuuCBltwMHDgiXyyXKysrEV7/4RdEu\nSeKcM88c/7hpxONxccIJJ4g//OEPE+73q1/9Spx88snipZdeUmI/fPiweOqpp8QFF1wgtFqtuPrq\nq8X//u//ig8++ED8/Oc/F2eccYb43Oc+J9ra2sS+ffsyu3bKu1zcN6d9hPr6+mkHkQtMIlQI69ev\nF8cdd5xYsGCB+PrXv542cUxKloW45RYhzjxTiJNPFuLqq4V44QUhvvGNiSuyhRCyLIuysrKpFZEJ\nIX72s5+J8847b9ztHR0d4sQTTxR//OMfx91ncHBQNDU1iaOOOkosWLBALFq0SOzYsSPj4i4qnBlN\nIkajUbjdbmVZr9enfalUqmkHkQtMIpRvXV1d4sgjj5zaDfwvfxHittuE+PKXhVi0SIhNm4R4+mkh\nDh/+5z6TVFQnJVtxmUymrH/lf/jhh2Lx4sXimWeeGbPtkUceEccff3zGLTCXL18+5WRGhZGL++a4\n/UQ8Hg+WLl2qdAbUaDTjjp3V398/I0Vt2eBQ8JQvQgjcfvvt2Lp1K0477TR0d3fDZDLB7/dP3CHw\nww+Bc88F+vqADz4AbDbgkkuAFSsSHfymKBaLwW63w+PxTGlgxQcffBD33nsvnn/+eUgfdQh88skn\ncckll+Dxxx9PGUNvIqtWrcITTzyR2b8FFYWc3DczzTaNjY1p13d0dEw7k+VCFpdCNGX79u0T3/72\nt0VFRYWQZTmzcv7/+z8h/vVfhTj+eCE0mqLrK3Ho0CFxxhlniN/+9rdCiESz+0WLFonnn38+q+Ow\nzmP2ycV9c8pHkGV52ifPJSYRmmmyLIuKigpx0UUXKZXFE/qv/xJi/fpE4rjiCiH+538m77BXII89\n9pj4/Oc/L3bv3i2OO+440dXVVeiQKA9ycd/MuImv2+1OWe7o6IDFYsGOHTum9yhENAvs3LkTZ599\nNi677DL84he/wMc//vH0Ox44ADz0EHDWWcC3vw0sXQrIMnDXXcBnPpPYZrUCfr/S3LYYrFq1Cnv2\n7EF1dTXKy8tx5plnFjokmiUyHjvLZrOlnYNgovGs8ol1IjQThBBwuVy4/fbb8etf/xrnnHNO+h3f\nfhtoawPuvTeRLH7wA+D884EFC/Ib8DR8+ctfRnd3NwDAarVyzpF5IBf3zQlr82w2mzKSbk9PD849\n91wIIZQTy7KcMnEU0Vzy3nvv4bLLLsMbb7yBl156CZ/85CfH7rR2LfD004nRbdetA554AvjSl/If\nbA4cd9xxADLoCEk0woTFWe3t7WhtbYUQAlqtFqWlpSgtLUVJSQnKy8uxevVq+P3+fMVKlDevv/46\nzjrrLKjVajz99NNjE8ibbwIOB7BjB/D3vwOHDgH798/aBAIADz30EKxWK1tWUVYyLs5yu91pZzYs\nFizOolx57LHHcPnll+PGG28cM8Mn3n4b2LoVuP9+oK4O6OkBgkHAZCq6eg6iyeTivplxxbparca2\nbdsQiUQQDAZhMpmwdu1aZVx6otnu8OHDMBqNWL16NfR6vTKZE4DExE3XXgt89rOJJ44//jGRTHy+\noqwoJ8qXjJNIR0eHMkGU2WyGyWSC0+kc+0uNaBb6xz/+gW984xsYGBjAgQMH8MILLyS+2++/D7jd\nwOmnA3/+c+LJ4667gJNOSnxw1MCGRPNNVk8ira2tGBgYAAA4nU5UVlay7JRmvaeffhqVlZU4++yz\ncfbZZwMAzq6sxP1nnZVIHi++CHR1JYqwyssLGitRscl6rAWfzwedTody/jHRLDc8PIytW7fizjvv\nxP33349zzz0XsT178OCqVfje3/+OBX4/8MgjifoOIkor4yRiMplgsVgQCATQ1taGYDAIh8OBysrK\nmYyPaEa88847uPjii/Hee++hu7s70frqueeg/pd/wVWHDiXqPn7zGxZTEU0i49ZZAJQ+I2q1GuFw\nGHv27IEkSaiurp6xADPF1lmUqeeffx7r16/H+vXrceONN+LIWAxwOoGdO4GSEuC11xI7Wq2J+g6i\nOSqvrbOARPJI1oEsWbIENTU18Pl8GX/ebDZPuN1qtaYsy7IMt9uNYDAIt9uNeDyeTbhEKYQQuPXW\nW1FbW4t77rkHLbfcgiPvuw/4whcSTxyvvgosXpzY2WQC2OGOaFLjPomYTCasW7cOmzdvBgAYDIa0\nB4hEIjh8+PCEJwkGgxgYGEB9fT2Gh4fT7hMIBGCxWFK2m0wmpUVYPB5HXV3duEMx8EmEJjI0NIQN\nGzbg7bffxvbt23HaO+8AmzYlhmC/5x4gOVZULAbY7YkEwqIsmuNmdNgTu92eMo/Anj17xp1PZDLV\n1dWorq5GfX192u3xeBxarTalpVcoFEoZUqW0tBSBQGDScxGN9uKLL2Lt2rWora2F7+c/x8IbbkgU\nU918M7BhA6Aa8UCebLJLRBmZMImM5HA40vZY1+v10w4iEAhg9erVKetkWR7TfFir1aKvrw8VFRXT\nPifNfeL+p6uJAAAb0UlEQVSjyaNuvvlmeNracOEHHySeOL75TeCVV4CyskKHSDTrZdw6S6/XY9u2\nbaiuroYsy3A6ndDr9WhpaZlWAMFgMG1dSTGMDEyz16WXXorHH38cBw8exAv33YfP3XknEI0mxrr6\nqC8IEU1fQXusRyIRaLValJSUjNlWVlamtAZLYmKhTPzlL39BR0cHPtizB869e3HK+vXAt74FdHcz\ngRDlWMZPIske68l6CafTifLy8mn1WA+FQhgaGlKSUywWU552dDpd2qQxUVHWhg0bsPij1jVqtRoV\nFRWoqqoCAHR1dQEAl+f48qJFi7By5UpsHR7GZwEsOfJIiGefRdf77wPPPVfw+LjM5UIuJ9/ndMzD\nTKdAtH40H7TD4RB6vX7M+kxIkpTVdqPRqLwfGBgQNptt3M9mcSk0Rz333HNCd9xx4vVzzhHDRx1V\ndHOZExWbXNw3My7OSvZY93g8cDqdCAaD4zb7HS0cDsPlckGSJDQ1NSEYDKZsj8fjyvZbb70VkUgE\nAOD1euF2u9HZ2QmPxwOv15txcqT55dFHH4XrvPPw3wsWwHD66ZC+9rXEBvb3IJpR7LFOs94DbW34\n4JprcPkxx2DhffcB553H/h5EGcjFfTOrJAIAfX19kGUZtbW1CIfDWLJkybQCyBUmkflHCIH7r7oK\nX/F4cNKKFfjEL38JfDTFKxFNLq/DnkQiERgMBlRWVsLpdAIA2trasGPHjmkFQDQVw/v344lly/Av\nra1Y9NOf4hNPPMEEQlQAGT+JWCwWOBwOVFdXw263K8OPjByapJD4JDJ/HHj5Zbzx9a9jSAh85rnn\nUPr5zxc6JKJZKe/T465evXpMk97RfTmIZszwMD50ufC+yYTgqafiS//3f0wgRAWWcRKJRqPYtm1b\nyki6brebMxtSfrzxBg587Wv40/XX49baWmzs7cXRH/tYoaMimvcyLs6SZRlms1lpfgsknk6CwWBR\nVK6zOGuOEgL4xS9w+JprcJskYd+mTbj2+ushSVKhIyOa9QrSOsvn8ymDI65duxalpaXTCiBXmETm\noH/8A7Db8cGrr+KCWAy1112HTZs2FToqojkjr0nEZrMhGAxiz5490zrhTGESmWN27gQuvBAfHnss\nfh+L4V2vF/9y6aWFjopoTslrxXo0GkVTU9O0TkY0qUOHgH/9V+Dyy9GvUuHot9/GioMHsfK3vy10\nZESURsZJpKmpCWq1Gnv37k1Zz+IFypk33wRWrAC6u9HR3IzX338fAPASADufMomKUlb9RIaGhhAK\nhaDX66FWq6HRaBAMBiedHjcfWJw1yz3xBHDZZcD3v48HTjoJW/7t3/Cl007DhhdeQGtFBR7evZst\nAYlyLK91IhqNJm1FutfrLYp5PphEZqmDB4F/+zfgoYeAX/0KP3/9dVx77bUIBAI48cQTYbfb4fF4\nmECIZsCMzrE+mt1uTzuLYS6mx6V56i9/AdatA0pKgFAInocfxo033ojdu3fj9NNPBwBlZAQiKk5Z\nN/EtVnwSmWUeewz47neBa64BGhpwb1sbtm7dmtUUA0Q0PXl9EiHKiYMHgeZmoL09Md/5V76Cu+66\nC7feeit2794NnU5X6AiJKAtMIpQ/f/4zsHZtYrTdcBgoK8PPfvYz3HHHHejq6lKmNiai2SPjJr6j\njRz+hGhSjzwCLF0KrFkD/O53QFkZ/v3f/x133nkndu/ezQRCNEtlnETcbnfKckdHBywWC+cToYkd\nOAD88IfA97+fSCSbNwMqFVwuF1pbW9HV1YVPfepThY6SiKYoq2FP0rWU0Wq1GTfxNZvN8Pv9KetC\noRCi0ShisRj8fj+cTifKy8sBJAZ97OzsRGVlJUKhEOx2+7hjdbFivQhFIoniqxNPBO6/H9BqAQA3\n33wzHnjgAezatQunnHJKYWMkmsdmvGLdZrMp84X09PTg3HPPhRBCObEsy9B+dGOYSDAYxMDAAILB\n4JhtNTU1GBwcRElJCYaGhmC1WpVJrmw2m/LeZDKhrq6OTT5nA7sdeP554PXXgZ/8JFGR/tGouzfc\ncAMeeughdHV14aSTTipsnEQ0bRMWZ7W3t6O1tRVCCGi1WpSWlqK0tBQlJSUoLy/HmjVrxjxZpJOc\nDTGdZAIBEh0ak0N8h0KhlARVWlqKQCCQ8YVRgRw6lOh9/uqriZZYfX3ARz86rr32WvzmN7/B7t27\nmUCI5ohJW2fpdDr4/X64XC40NjbmPIBkAgEAj8ejdGhMDjc/klarRV9fHyoqKnIeB+XA3/+eKL76\n4IPEsskEeDwQQuBHP/oRHnnkEezevRvHH398YeMkopzJuGJ9vAQycqbDqYpEInC73bBYLFixYgUA\nFMVQKpSFF15IJI3ly4E//QmwWgG/H6K0FFu2bMGjjz6KXbt2MYEQzTFZN/Hdu3ev8orH47DZbNMO\nory8HA0NDSgvL4fFYgGQeOoYPX87E0sREgK45x7ggguAu+8GbrgBKCsD2tshSkvR2NiIJ598Ert2\n7cKiRYsKHS0R5VjGnQ2DwSCsVuuYG/t0pilNtr5qaGgAkKg7sVqtGBwchF6vT5s0JirK2rBhg9Lf\nQK1Wo6KiAlVVVQCArq4uAOByLpc//BBVv/41EAqh67bbgJISJLYCu3fvxj333ANZlhEMBvHyyy8X\nPl4uc3meLyffDw4OIlcybuJrMBhQU1ODNWvWpFR4NzU1YefOnRmdTKVSYXh4WFkOBoPw+/3YunUr\ngERlutlsVmZPNJlMSussWZbR3NyM7du3p78QNvHNL1kGamuBL3wB8HiAY45RNl1++eV46qmnsG/f\nPvT19bEjIVGRyutQ8AaDAf39/WPWRyIRpV/HeMLhMPx+P5qbm9HQ0ACz2Yzq6moAQGdnJ4aGhqDV\nauH3+1FfX688bYTDYQQCAeh0OnR3d2PLli0pFfEpF8Ikkj9PPAFs2JCYgfCqq5Tmu3//+99xzz33\n4Oabb8ahQ4cAAFarlc2yiYpUXpOI2+2GJEnYvHlzyvrm5mbccsst0woiF5hE8mB4OFHn4fEA27cD\nX/0qAOCVV17Bbbfdhh07dmDdunV45ZVX8Oyzz8JkMsHv93MuEKIildckYjKZEAqFIEkSKisrlROH\nw2HObDgfRKPAxRcD8Xii0vzEExEIBHDbbbehr68PV1xxBerr63HcccchFotxMimiWSDvMxs6HI4x\nJ+TMhvPAH/6QqP/45jex/8Yb8WufD7fddhuEELjmmmuwfv16HH300YWOkoiylNf5RJqbm9P2FVm6\ndOm0AqAi98tfAj/8Id696Sbc8fbbuPvTn8YZZ5yBW2+9FWazeVqt84ho9st6ZsO+vj7Isoza2lqE\nw2EsWbJkpmLLCp9EcuzAAeCaa3DgscfgOuss/PvOnfjWt76Fa665Bl/84hcLHR0R5UAu7psZdzaM\nRCIwGAyorKyE0+kEALS2tnIo+DlIvPkm4pWVeNHnw+feew/7P/1pvPbaa/iP//gPJhAiSpHxk4jF\nYoHD4VAGU0w22xzZl6OQ+CQyfQcPHsTT11+PM1ta8MuSEnzs+utxyYYN+PjHP17o0IhoBuS1TkSt\nVmP16tVj1o/uwU6zjxACX6+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+ "text": [ + "" + ] + } + ], + "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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