mirror of
https://github.com/wassname/simpeg.git
synced 2026-07-28 11:26:12 +08:00
746 lines
215 KiB
Plaintext
746 lines
215 KiB
Plaintext
{
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"metadata": {
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"name": "",
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"signature": "sha256:6a79886b331e74256f87f4817f8611dc05c2f024c9ebc5064c8b977af7cc4517"
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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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"from pymatsolver import MumpsSolver\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": 25
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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': 20, 'text.usetex': True})\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": 26
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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": 27
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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": 28
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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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"metadata": {},
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"output_type": "display_data",
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"png": 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U4Ng3z9Hah98PoRCsWCFx220OdB2WLYsxcaKBJMHkyQ4mT9Y57DCDDz+UeOml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\nrkJggg==\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x64d5a90>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 29
|
|
},
|
|
{
|
|
"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": 30
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"fig, ax = plt.subplots(1,2, figsize = (18*0.8, 7*0.8))\n",
|
|
"Utils1D.plotLayer(np.log(sigma), mesh.vectorCCz, 'linear', showlayers=True, ax = ax[0])\n",
|
|
"ax[0].invert_xaxis()\n",
|
|
"ax[0].set_ylim(-500, 0)\n",
|
|
"ax[0].set_xlim(-7, -4)\n",
|
|
"ax[0].set_xlabel('$log(\\sigma)$', fontsize = 25)\n",
|
|
"ax[0].set_ylabel('Depth (m)', fontsize = 25)\n",
|
|
"ax[0].text(-7., 10., '(a)', fontsize = 30)\n",
|
|
"dat = mesh.plotSlice((mapping*mtrue), normal='Y', ind = 9, ax = ax[1])\n",
|
|
"cb = plt.colorbar(dat[0], ax =ax[1])\n",
|
|
"ax[1].set_title(\"Vertical section\", fontsize = 25)\n",
|
|
"cb.set_label(\"Conductivity (S/m)\", fontsize = 25)\n",
|
|
"ax[1].set_xlabel('Easting (m)', fontsize = 25)\n",
|
|
"ax[1].set_ylabel(' ', fontsize = 25)\n",
|
|
"ax[1].set_xlim(-1000., 1000.)\n",
|
|
"ax[1].set_ylim(-500., 0.)\n",
|
|
"ax[1].text(-1000., 13., '(b)', fontsize = 30)\n",
|
|
"fig.savefig('mappingDC.png', dpi=200)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": 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L5Z5Ikru/V3PbknvaA3f/SZ3HBoBBqvrdWqVeTgDa93f7sJEFt7NCa3Dm8ZC57bqkE/Hp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\nfyZoT/gncosuAAAAAElFTkSuQmCC\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0xad96110>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 31
|
|
},
|
|
{
|
|
"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": [],
|
|
"prompt_number": 32
|
|
},
|
|
{
|
|
"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": 33
|
|
},
|
|
{
|
|
"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": 34
|
|
},
|
|
{
|
|
"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": 35
|
|
},
|
|
{
|
|
"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": 36,
|
|
"text": [
|
|
"(-0.2, 0.6)"
|
|
]
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": 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|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x649b650>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 36
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"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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fuqYsPL94Mve5dbos5+n8ev3ZjKMTpp80PWPExCzpfV893nlqogWU4IkWYFttZT2LkReq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\nJLXQiBFCCEkt/z8zAwkz3y21HgAAAABJRU5ErkJggg==\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x568a0d0>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 37
|
|
},
|
|
{
|
|
"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": 38
|
|
},
|
|
{
|
|
"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 = pymatsolver.MumpsSolver"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 39
|
|
},
|
|
{
|
|
"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": 40
|
|
},
|
|
{
|
|
"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": 41
|
|
},
|
|
{
|
|
"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 = (16, 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\")\n",
|
|
"cb.set_label(\"Conductivity (S/m)\")\n",
|
|
"ax[0].set_xlabel('Easting (m)')\n",
|
|
"ax[0].set_ylabel('Depth (m)')\n",
|
|
"ax[0].set_xlim(-1000., 1000.)\n",
|
|
"ax[0].set_ylim(-500., 0.)\n",
|
|
"# ax[0].text(-1000, 20, '(b)')\n",
|
|
"fig.savefig('DCfwd.png', dpi=200)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": 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jZra83krMbJmZ7QaWEXqqyzlBCL47Sl49QFcsk/f7ExERaUvm7q1uw6xkZt7X6kaIiLSJ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\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x55daf90>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 42
|
|
},
|
|
{
|
|
"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": 43
|
|
},
|
|
{
|
|
"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 2.06e-01 6.16e+03 4.48e+00 6.16e+03 8.76e+03 0 "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
" 1 2.06e-01 5.04e+02 3.94e+00 5.04e+02 2.75e+03 0 "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
" 2 4.13e-02 1.61e+02 3.92e+00 1.61e+02 6.53e+02 0 "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
" 3 4.13e-02 8.31e+01 4.15e+00 8.33e+01 2.36e+02 0 Skip BFGS "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
" 4 8.26e-03 6.26e+01 4.36e+00 6.27e+01 1.67e+02 0 Skip BFGS "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
" 5 8.26e-03 2.53e+01 5.20e+00 2.54e+01 8.12e+01 0 Skip BFGS "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
" 6 1.65e-03 1.34e+01 5.84e+00 1.34e+01 4.74e+01 0 Skip BFGS "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
" 7 1.65e-03 5.54e+00 6.93e+00 5.55e+00 1.06e+01 0 Skip BFGS "
|
|
]
|
|
},
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"\n",
|
|
"------------------------- STOP! -------------------------\n",
|
|
"1 : |fc-fOld| = 7.8145e+00 <= tolF*(1+|f0|) = 6.1639e+02\n",
|
|
"0 : |xc-x_last| = 5.7468e-01 <= tolX*(1+|x0|) = 2.6641e-14\n",
|
|
"0 : |proj(x-g)-x| = 1.0608e+01 <= tolG = 1.0000e-01\n",
|
|
"0 : |proj(x-g)-x| = 1.0608e+01 <= 1e3*eps = 1.0000e-02\n",
|
|
"1 : maxIter = 7 <= iter = 7\n",
|
|
"------------------------- DONE! -------------------------\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 44
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"# matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 45
|
|
},
|
|
{
|
|
"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(1,2, figsize = (17, 6))\n",
|
|
"ax[0].plot(abhalf, appres_obs, 'k.-')\n",
|
|
"ax[0].plot(abhalf, appres_pred, 'r.-')\n",
|
|
"ax[0].set_xscale('log')\n",
|
|
"ax[0].set_ylim(100., 180.)\n",
|
|
"ax[0].set_xlabel('AB/2', fontsize=25)\n",
|
|
"ax[0].set_ylabel('Apparent resistivity ($\\Omega m$)', fontsize=25)\n",
|
|
"ax[0].grid(True)\n",
|
|
"ax[0].legend(('Observed', 'Predicted'), loc = 1, fontsize=20)\n",
|
|
"ax[0].text(100., 181, '(a)', fontsize = 28)\n",
|
|
"ax[1].plot(1., 1., 'k', lw = 2)\n",
|
|
"ax[1].plot(1., 1., 'r', lw = 2)\n",
|
|
"ax[1].legend(('True', 'Predicted'), loc = 3, fontsize = 20)\n",
|
|
"Utils1D.plotLayer((np.exp(mopt)), mesh.vectorCCz, 'log', ax = ax[1], **{'lw':2, 'color':'r'})\n",
|
|
"Utils1D.plotLayer((np.exp(mtrue)), mesh.vectorCCz, 'log', showlayers=True, ax = ax[1], **{'lw':2})\n",
|
|
"ax[1].set_ylim(-500, 0)\n",
|
|
"ax[1].set_xlabel('Conductivity (S/m)', fontsize = 25)\n",
|
|
"ax[1].set_ylabel('Depth (m)', fontsize = 25)\n",
|
|
"ax[1].text(1e-3, 10., '(b)', fontsize = 28)\n",
|
|
"fig.savefig('obspredDC.png', dpi=200)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": 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UOiUiaSzm34Ybb7zRAW9oaFiWHv9C3i/XHv+hoaG0Pf6Zero7OjrczHxgYMDd3ePxuJuZ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\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x56c4850>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 46
|
|
},
|
|
{
|
|
"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(-500, 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": [],
|
|
"prompt_number": 47
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": []
|
|
}
|
|
],
|
|
"metadata": {}
|
|
}
|
|
]
|
|
} |