mirror of
https://github.com/wassname/simpeg.git
synced 2026-07-13 17:45:30 +08:00
591 lines
129 KiB
Plaintext
591 lines
129 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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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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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Vendor: Continuum Analytics, Inc.\n",
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"Package: mkl\n",
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"Message: trial mode expires in 11 days\n"
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]
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}
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],
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"source": [
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"from SimPEG import *\n",
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"import simpegDCIP as DC\n",
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"%pylab inline\n",
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"from pymatsolver import MumpsSolver"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"cs = 12.5\n",
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"nc = 500/cs+1\n",
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"hx = [(cs,7, -1.3),(cs,nc),(cs,7, 1.3)]\n",
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"hy = [(cs,7, -1.3),(cs,int(nc/2+1)),(cs,7, 1.3)]\n",
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"hz = [(cs,7, -1.3),(cs,int(nc/2+1))]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" ---- 3-D TensorMesh ---- \n",
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" x0: -541.97\n",
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" y0: -416.97\n",
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" z0: -548.22\n",
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" nCx: 55\n",
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" nCy: 35\n",
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" nCz: 28\n",
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" hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
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" hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
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" hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n"
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]
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}
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],
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"source": [
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"mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')\n",
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"print mesh"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"sighalf = 1e-2\n",
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"sigma = np.ones(mesh.nC)*sighalf\n",
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"p0 = np.r_[-50., 50., -50.]\n",
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"p1 = np.r_[ 50.,-50., -150.]\n",
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"blk_ind = Utils.ModelBuilder.getIndicesBlock(p0, p1, mesh.gridCC)\n",
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"sigma[blk_ind] = 1e-3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"eta = np.zeros_like(sigma)\n",
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"eta[blk_ind] = 0.1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"sigmaInf = sigma.copy()\n",
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"sigma0 = sigma*(1-eta)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(-600.0, 600.0, -600.0, 0.0)"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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SKuAU2y1n9PRqguj/AmYCd5eTbf7e9lW2t0u6DdgOHACuasjcVwEfB06huPp8VKKPiJhu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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x10ea77dd0>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"dat = mesh.plotSlice(eta, normal='Y')\n",
|
|
"plt.colorbar(dat[0])\n",
|
|
"axis('equal')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"nElecs = 21\n",
|
|
"x_temp = np.linspace(-250, 250, nElecs)\n",
|
|
"aSpacing = x_temp[1]-x_temp[0]\n",
|
|
"y_temp = 0.\n",
|
|
"xyz = Utils.ndgrid(x_temp, np.r_[y_temp], np.r_[0.])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"63\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"srcList = DC.Examples.WennerArray.getSrcList(nElecs,aSpacing)\n",
|
|
"survey = DC.SurveyDC(srcList)\n",
|
|
"print len(survey.srcList)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x10f150e50>]"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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Ev0t6C3BKRLy+qF2tnOF0cu+dwqzx+f3yisodr3/bjjcz3sQ2DRovW94rr0x+aSV66bWb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\nWSX+P3faf3379MqEAAAAAElFTkSuQmCC\n",
|
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"text/plain": [
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"<matplotlib.figure.Figure at 0x10eedb350>"
|
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]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
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"source": [
|
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"fig, ax = plt.subplots(1,1, figsize = (6.5,5))\n",
|
|
"indz = 22\n",
|
|
"dat = mesh.plotSlice(sigma, grid=True, ax = ax, ind=indz)\n",
|
|
"ax.set_xlim(-300, 300)\n",
|
|
"ax.set_ylim(-300, 300)\n",
|
|
"cb = plt.colorbar(dat[0])\n",
|
|
"ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n",
|
|
"\n",
|
|
"txLocs = np.array([[tx.loc[0][0], tx.loc[1][0]] for tx in survey.srcList]).flatten()\n",
|
|
"ax.plot(txLocs, txLocs*0, 'w.', ms = 3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"expmap = Maps.ExpMap(mesh)\n",
|
|
"m2to3 = Maps.Map2Dto3D(mesh,normal='Y')\n",
|
|
"imap = Maps.IdentityMap(mesh)\n",
|
|
"problem = DC.ProblemDC_CC(mesh, mapping= imap )\n",
|
|
"problem.Solver = MumpsSolver\n",
|
|
"try:\n",
|
|
" problem.pair(survey)\n",
|
|
"except Exception, e:\n",
|
|
" survey.unpair()\n",
|
|
" problem.pair(survey)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"phi0 = survey.dpred(sigma0)\n",
|
|
"phiInf = survey.dpred(sigmaInf)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"phiIP_true = phi0-phiInf"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# F = problem.fields(mesh, survey)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"surveyIP = DC.SurveyIP(srcList)\n",
|
|
"problemIP = DC.ProblemIP(mesh, sigma=sigma)\n",
|
|
"problemIP.pair(surveyIP)\n",
|
|
"problemIP.Solver = MumpsSolver"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x10ffa6710>,\n",
|
|
" <matplotlib.lines.Line2D at 0x10ffa6990>]"
|
|
]
|
|
},
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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1t9o87g9Rnyou/gBgMVowJZwcOjecv/IFRJNDbHSXYEE43ShbiImJoptqXuBo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\nKXwAAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x10f177d90>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"datasyn = surveyIP.dpred(eta)\n",
|
|
"surveyIP.makeSyntheticData(eta,std=0.02,force=True)\n",
|
|
"plot(np.c_[surveyIP.dobs,datasyn])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"problemIP.mapping = imap"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"dmis = DataMisfit.l2_DataMisfit(surveyIP)\n",
|
|
"dmis.Wd = 1./abs(phi0)*1e3\n",
|
|
"reg = Regularization.Tikhonov(mesh,mapping=imap)\n",
|
|
"# opt = Optimization.InexactGaussNewton(maxIter=4,tolX=1e-15)\n",
|
|
"opt = Optimization.ProjectedGNCG(maxIter=3,tolX=1e-15)\n",
|
|
"opt.remember('xc')\n",
|
|
"invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
|
|
"beta = Directives.BetaEstimate_ByEig(beta0_ratio=1e-1)\n",
|
|
"betaSched = Directives.BetaSchedule(coolingFactor=5, coolingRate=4)\n",
|
|
"inv = Inversion.BaseInversion(invProb, directiveList=[beta,betaSched])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"reg.alpha_s = 1e-4\n",
|
|
"reg.alpha_x = 1.\n",
|
|
"reg.alpha_z = 1.\n",
|
|
"reg.alpha_y = 1."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"opt.upper = Inf\n",
|
|
"opt.lower = 0."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"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",
|
|
"=============================== Projected GNCG ===============================\n",
|
|
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
|
"-----------------------------------------------------------------------------\n",
|
|
" 0 5.84e+00 9.43e+01 0.00e+00 9.43e+01 1.69e+03 0 \n",
|
|
" 1 5.84e+00 7.43e+00 6.13e-03 7.46e+00 3.04e+02 0 \n",
|
|
" 2 5.84e+00 3.10e+00 1.33e-02 3.18e+00 2.65e+02 0 Skip BFGS \n",
|
|
" 3 5.84e+00 2.13e+00 1.82e-02 2.24e+00 2.49e+02 0 Skip BFGS \n",
|
|
"------------------------- STOP! -------------------------\n",
|
|
"1 : |fc-fOld| = 9.4407e-01 <= tolF*(1+|f0|) = 9.5287e+00\n",
|
|
"0 : |xc-x_last| = 4.4997e-02 <= tolX*(1+|x0|) = 1.0000e-15\n",
|
|
"0 : |proj(x-g)-x| = 2.4936e+02 <= tolG = 1.0000e-01\n",
|
|
"0 : |proj(x-g)-x| = 2.4936e+02 <= 1e3*eps = 1.0000e-02\n",
|
|
"1 : maxIter = 3 <= iter = 3\n",
|
|
"------------------------- DONE! -------------------------\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"m0 = np.ones(problemIP.mapping.nP)*1e-10\n",
|
|
"mopt = inv.run(m0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" ---- 3-D TensorMesh ---- \n",
|
|
" x0: -541.97\n",
|
|
" y0: -416.97\n",
|
|
" z0: -548.22\n",
|
|
" nCx: 55\n",
|
|
" nCy: 35\n",
|
|
" nCz: 28\n",
|
|
" hx: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 41*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
|
|
" hy: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50, 16.25, 21.13, 27.46, 35.70, 46.41, 60.34, 78.44\n",
|
|
" hz: 78.44, 60.34, 46.41, 35.70, 27.46, 21.13, 16.25, 21*12.50\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print mesh"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(-600.0, 600.0, -600.0, 0.0)"
|
|
]
|
|
},
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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QZID/UIbDk/Bhf/cTppaO5x9ZzsPl7cwv7U9peVIRlGbBOmBBKNVWQ+lAWHHO+awt72BB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\n4yavLtDJAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x10faf04d0>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"colorbar(mesh.plotSlice(mopt, normal='Y', ind=17, grid=True, gridOpts={'alpha': 0.2, 'color':'black'})[0])\n",
|
|
"axis('equal')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# sigma_est = expmap * m2to3 * mopt"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"dpred = surveyIP.dpred(mopt)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x11345b510>,\n",
|
|
" <matplotlib.lines.Line2D at 0x11345b750>]"
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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2sMiSuY2ExWChom7uLIhQJMSFSy6ccO3s3KllF61aBdWj+j/VHxtwsrjFgRBw7bWTLdRr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\nXDcyffh06hrq2HJsi9cxIrB4sXa1ebJlCxRbDU484zMrvlnBJedcQtI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|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x10f219190>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plot(np.c_[dpred,surveyIP.dobs], '.-')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 2",
|
|
"language": "python",
|
|
"name": "python2"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.10"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|