mirror of
https://github.com/wassname/simpeg.git
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Merge in @sgkang's work on IP.
This commit is contained in:
Rowan Cockett committed
commit
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7 files changed
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@@ -0,0 +1,402 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 372,
|
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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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"from SimPEG import *\n",
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"import simpegDCIP as DC\n",
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"from ipywidgets import interact, IntSlider, FloatSlider, FloatText, ToggleButtons"
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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": 221,
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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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"%matplotlib inline"
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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": 230,
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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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"import matplotlib\n",
|
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"import matplotlib.pyplot as plt\n",
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"matplotlib.rcParams['font.size'] = 14"
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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": 231,
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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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"npad = 8\n",
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"cs = 1.\n",
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"hx = [(cs,npad, -1.3),(cs,100),(cs,npad, 1.3)]\n",
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"hy = [(cs,npad, -1.3),(cs,50)]\n",
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"mesh = Mesh.TensorMesh([hx, hy], \"CN\")\n",
|
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"x = mesh.vectorCCx"
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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": 232,
|
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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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"circmap = Maps.CircleMap(mesh)\n",
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"circmap.slope = 1e5\n",
|
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"mapping = circmap\n",
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"sighalf = 1e-3\n",
|
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"rhohalf = 1./sighalf"
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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": 233,
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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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"xr = np.linspace(-40, 40, 20)\n",
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"xz_A = Utils.ndgrid(xr, np.r_[-2.5])\n",
|
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"xz_B = Utils.ndgrid(np.ones_like(xr)*19, np.r_[-2.5])\n",
|
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"xz_M = Utils.ndgrid(xr, np.r_[-2.5])\n",
|
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"xz_N = Utils.ndgrid(np.ones_like(xr)*-19, np.r_[-2.5])"
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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": 364,
|
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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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"def DipoleDipolefun(i):\n",
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" plt.figure(figsize=(10, 3))\n",
|
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" nmax = 8\n",
|
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" ntx = xr.size-2\n",
|
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" dxr = np.diff(xr)\n",
|
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" plt.plot(xr[:-1]+dxr*0.5, np.zeros_like(xr[:-1]), 'ko')\n",
|
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" plt.plot(xr[i]+dxr[i]*0.5, np.zeros(1), 'ro')\n",
|
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" # for i in range(ntx):\n",
|
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" if i < ntx-nmax+1:\n",
|
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" txmid = xr[i]+dxr[i]*0.5\n",
|
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" rxmid = xr[i+1:i+1+nmax]+dxr[i+1:i+1+nmax]*0.5\n",
|
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" mid = (txmid+rxmid)*0.5\n",
|
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" plt.plot(rxmid, np.zeros(rxmid.size), 'go')\n",
|
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" plt.plot(mid, np.arange(nmax)+1., 'bo')\n",
|
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" plt.plot(np.r_[txmid, mid[-1]], np.r_[0, nmax], 'k:') \n",
|
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" for j in range(nmax):\n",
|
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" plt.plot(np.r_[rxmid[j], mid[j]], np.r_[0, j+1], 'k:') \n",
|
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"\n",
|
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" else:\n",
|
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" txmid = xr[i]+dxr[i]*0.5\n",
|
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" rxmid = xr[i+1:ntx+1]+dxr[i+1:ntx+1]*0.5\n",
|
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" mid = (txmid+rxmid)*0.5 \n",
|
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" plt.plot((txmid+rxmid)*0.5, np.arange(mid.size)+1., 'bo')\n",
|
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" plt.plot(rxmid, np.zeros(rxmid.size), 'go')\n",
|
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" plt.plot(np.r_[txmid, mid[-1]], np.r_[0, mid.size], 'k:') \n",
|
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" for j in range(ntx-i):\n",
|
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" plt.plot(np.r_[rxmid[j], mid[j]], np.r_[0, j+1], 'k:') \n",
|
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" plt.xlabel(\"X (m)\")\n",
|
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" plt.ylabel(\"N-spacing\") \n",
|
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" xlim(xr.min(), xr.max())\n",
|
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" ylim(nmax+1, -1)\n",
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" plt.show()\n",
|
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" return "
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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": 365,
|
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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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"def getPseudoLocs(xr, ntx, nmax):\n",
|
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" dxr = np.diff(xr)\n",
|
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" xloc = []\n",
|
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" yloc = [] \n",
|
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" for i in range(ntx):\n",
|
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" if i < ntx-nmax+1:\n",
|
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" txmid = xr[i]+dxr[i]*0.5\n",
|
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" rxmid = xr[i+1:i+1+nmax]+dxr[i+1:i+1+nmax]*0.5\n",
|
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" mid = (txmid+rxmid)*0.5\n",
|
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" xloc.append(mid)\n",
|
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" yloc.append(np.arange(nmax)+1.)\n",
|
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" else:\n",
|
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" txmid = xr[i]+dxr[i]*0.5\n",
|
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" rxmid = xr[i+1:ntx+1]+dxr[i+1:ntx+1]*0.5\n",
|
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" mid = (txmid+rxmid)*0.5 \n",
|
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" xloc.append(mid)\n",
|
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" yloc.append(np.arange(mid.size)+1.)\n",
|
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" xlocvec = np.hstack(xloc)\n",
|
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" ylocvec = np.hstack(yloc)\n",
|
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" return np.c_[xlocvec, ylocvec]"
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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": 366,
|
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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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"dxr = np.diff(xr)\n",
|
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"ntx, nmax = xr.size-2, 8\n",
|
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"xzlocs = getPseudoLocs(xr, ntx, nmax)"
|
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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": 367,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
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{
|
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"data": {
|
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"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10a35a610>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"interact(DipoleDipolefun, i=IntSlider(min=0, max=ntx-1, step = 1, value=0))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 238,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"txList = []\n",
|
||||
"zloc = -2.5\n",
|
||||
"for i in range(ntx):\n",
|
||||
" A = np.r_[xr[i]+dxr[i]*0.5, zloc]\n",
|
||||
" B = np.r_[mesh.vectorCCx.min(), zloc] \n",
|
||||
" if i < ntx-nmax+1:\n",
|
||||
" M = np.c_[xr[i+1:i+1+nmax], np.ones(nmax)*zloc] \n",
|
||||
" N = np.c_[xr[i+2:i+2+nmax], np.ones(nmax)*zloc] \n",
|
||||
" else:\n",
|
||||
" M = np.c_[xr[i+1:ntx+1], np.ones(ntx-i)*zloc] \n",
|
||||
" N = np.c_[xr[i+2:i+2+nmax], np.ones(ntx-i)*zloc] \n",
|
||||
" rx = DC.RxDipole(M, N)\n",
|
||||
" src = DC.SrcDipole([rx], A, B)\n",
|
||||
" txList.append(src)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 239,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"survey = DC.SurveyDC(txList)\n",
|
||||
"problem = DC.ProblemDC_CC(mesh, mapping = mapping)\n",
|
||||
"problem.pair(survey)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 240,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pymatsolver import MumpsSolver\n",
|
||||
"problem.Solver = MumpsSolver"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 422,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sigblk, sighalf = 2e-2, 2e-3\n",
|
||||
"xc, yc, r = -15, -8, 4\n",
|
||||
"mtrue = np.r_[np.log(sigblk), np.log(sighalf), xc, yc, r]\n",
|
||||
"dtrue = survey.dpred(mtrue) \n",
|
||||
"perc = 0.1\n",
|
||||
"floor = np.linalg.norm(dtrue)*1e-3\n",
|
||||
"uncert = np.random.randn(survey.nD)*perc + floor\n",
|
||||
"dobs = dtrue + uncert "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 429,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def DC2Dfwdfun(mesh, rhohalf, rhoblk, xc, yc, r, dobs, uncert, predmis):\n",
|
||||
" sighalf, sigblk = 1./rhohalf, 1./rhoblk\n",
|
||||
" m0 = np.r_[np.log(sighalf), np.log(sighalf), xc, yc, r]\n",
|
||||
" dini = survey.dpred(m0)\n",
|
||||
" mtrue = np.r_[np.log(sigblk), np.log(sighalf), xc, yc, r]\n",
|
||||
" dpred = survey.dpred(mtrue)\n",
|
||||
" xi, yi = np.meshgrid(np.linspace(xr.min(), xr.max(), 120), np.linspace(1., nmax, 100))\n",
|
||||
" appres = dpred/dini/sighalf\n",
|
||||
" appresobs = dobs/dini/sighalf\n",
|
||||
" pred = griddata(xzlocs[:,0], xzlocs[:,1], appres, xi, yi, interp='linear')\n",
|
||||
" obs = griddata(xzlocs[:,0], xzlocs[:,1], appresobs, xi, yi, interp='linear')\n",
|
||||
" fig = plt.figure(figsize = (12, 8))\n",
|
||||
" ax1 = plt.subplot(311)\n",
|
||||
" dat1 = mesh.plotImage(np.log10(1./(mapping*mtrue)), ax=ax1, clim=(0, 3), grid=True, gridOpts={'color':'k', 'alpha':0.5})\n",
|
||||
" cb1 = plt.colorbar(dat1[0], ticks=np.linspace(0, 3, 5), ax=ax1, format=\"$10^{%4.1f}$\")\n",
|
||||
" cb1.set_label(\"Resistivity (ohm-m)\")\n",
|
||||
" ax1.set_ylim(-30, 0.)\n",
|
||||
" ax1.set_xlim(-40, 40)\n",
|
||||
" ax1.set_xlabel(\"\")\n",
|
||||
" ax1.set_ylabel(\"Depth (m)\") \n",
|
||||
" ax2 = plt.subplot(312)\n",
|
||||
" dat2 = ax2.contourf(xi, yi, obs, 10)\n",
|
||||
" ax2.contour(xi, yi, obs, 10, colors='k', alpha=0.5)\n",
|
||||
" ax2.plot(xzlocs[:,0], xzlocs[:,1],'k.', ms = 3)\n",
|
||||
" cb2 = plt.colorbar(dat2, ax=ax2, ticks=np.linspace(appresobs.min(), appresobs.max(), 5), format=\"%4.1f\")\n",
|
||||
" \n",
|
||||
" cb2.set_label(\"Apparent Resistivity \\n (ohm-m)\")\n",
|
||||
" ax2.set_ylim(nmax+1, 0.)\n",
|
||||
" ax2.set_ylabel(\"N-spacing\") \n",
|
||||
" ax2.text(-38, 7, \"Observed\")\n",
|
||||
" \n",
|
||||
" ax3 = plt.subplot(313)\n",
|
||||
" if predmis==\"pred\":\n",
|
||||
" dat3 = ax3.contourf(xi, yi, pred, 10)\n",
|
||||
" ax3.contour(xi, yi, pred, 10, colors='k', alpha=0.5)\n",
|
||||
" ax3.plot(xzlocs[:,0], xzlocs[:,1],'k.', ms = 3)\n",
|
||||
" cb3 = plt.colorbar(dat3, ax=ax3, ticks=np.linspace(appres.min(), appres.max(), 5),format=\"%4.0f\")\n",
|
||||
" cb3.set_label(\"Apparent Resistivity \\n (ohm-m)\")\n",
|
||||
" ax3.text(-38, 7, \"Predicted\")\n",
|
||||
" elif predmis==\"mis\":\n",
|
||||
" mis = (appresobs-appres)/(0.1*appresobs)\n",
|
||||
" Mis = griddata(xzlocs[:,0], xzlocs[:,1], mis, xi, yi, interp='linear') \n",
|
||||
" dat3 = ax3.contourf(xi, yi, Mis, 10)\n",
|
||||
" ax3.contour(xi, yi, Mis, 10, colors='k', alpha=0.5)\n",
|
||||
" ax3.plot(xzlocs[:,0], xzlocs[:,1],'k.', ms = 3)\n",
|
||||
" cb3 = plt.colorbar(dat3, ax=ax3, ticks=np.linspace(mis.min(), mis.max(), 5), format=\"%4.2f\")\n",
|
||||
" cb3.set_label(\"Normalized misfit\")\n",
|
||||
" ax3.text(-38, 7, \"Misifit\") \n",
|
||||
" ax3.set_ylim(nmax+1, 0.)\n",
|
||||
" ax3.set_ylabel(\"N-spacing\") \n",
|
||||
" ax3.set_xlabel(\"Distance (m)\") \n",
|
||||
" plt.show()\n",
|
||||
" return True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 430,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"DC2Dfwd = lambda rhohalf, rhosph, xc, zc, r, predmis: DC2Dfwdfun(mesh, rhohalf, rhosph, xc, zc, r, dobs, uncert, predmis)\n",
|
||||
"# DC2Dfwdfun(mesh, 1000., 1e1, 0., -9, 4, dobs, uncert, \"pred\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 431,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x109567310>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"interact(DC2Dfwd,\n",
|
||||
" rhohalf = FloatSlider(min=0, max=1000, step=1, value = 500),\n",
|
||||
" rhosph = FloatSlider(min=0, max=1000, step=1, value = 50),\n",
|
||||
" xc = FloatSlider(min=-40, max=40, step=1, value = -15),\n",
|
||||
" zc = FloatSlider(min= -20, max=0, step=1, value = -8),\n",
|
||||
" r = FloatSlider(min= 0, max=15, step=0.5, value = 4),\n",
|
||||
" predmis = ToggleButtons(options=['pred','mis']) \n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 2",
|
||||
"language": "python",
|
||||
"name": "python2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@@ -0,0 +1,506 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from SimPEG import *"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING: pylab import has clobbered these variables: ['linalg']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%pylab inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cs = 0.5\n",
|
||||
"mesh = Mesh.TensorMesh([np.ones(100)*cs, np.ones(50)*cs], \"CN\")\n",
|
||||
"x = mesh.vectorCCx"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"actind = mesh.gridCC[:,1] < -1.\n",
|
||||
"meshact = Mesh.TensorMesh([mesh.hx, mesh.hy[:-2]], x0=mesh.x0)\n",
|
||||
"actmap = Maps.ActiveCells(mesh, actind, 1e-2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"circmap = Maps.CircleMap(meshact)\n",
|
||||
"circmap.slope = 1e5"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mapping = actmap*circmap"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"# mapping = Maps.CircleMap(mesh)\n",
|
||||
"mtrue = np.r_[np.log(1e0), np.log(1e-3), 0., -3., 2.]\n",
|
||||
"m0 = np.r_[np.log(1e-3), np.log(1e-3), -3, -5., 1]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import simpegDCIP as DC"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"xr = np.linspace(-15, 15, 20)\n",
|
||||
"xz_A = Utils.ndgrid(xr, np.r_[-0.25])\n",
|
||||
"xz_B = Utils.ndgrid(np.ones_like(xr)*19, np.r_[-0.25])\n",
|
||||
"xz_M = Utils.ndgrid(xr, np.r_[-0.25])\n",
|
||||
"xz_N = Utils.ndgrid(np.ones_like(xr)*-19, np.r_[-0.25])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ntx = xz_A.shape[0]\n",
|
||||
"txList = []\n",
|
||||
"for i in range(ntx):\n",
|
||||
" offset = abs(xz_A[i,0]-xz_M[:,0])\n",
|
||||
" actrx = offset > 5.\n",
|
||||
" rx = DC.RxDipole(xz_M[actrx,:], xz_N[actrx,:])\n",
|
||||
" src = DC.SrcDipole([rx], xz_A[i,:], xz_B[i,:])\n",
|
||||
" txList.append(src)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"survey = DC.SurveyDC(txList)\n",
|
||||
"problem = DC.ProblemDC_CC(mesh, mapping = mapping)\n",
|
||||
"problem.pair(survey)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pymatsolver import MumpsSolver\n",
|
||||
"problem.Solver = MumpsSolver"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dini = survey.dpred(m0)\n",
|
||||
"dtrue = survey.dpred(mtrue)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x107614750>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plot(dini)\n",
|
||||
"plot(dtrue)\n",
|
||||
"figsize(12, 5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(array([ 1., 2., 5., 4., 10., 11., 13., 20., 17., 20., 23.,\n",
|
||||
" 10., 0., 0., 4., 17., 29., 36., 32., 18.]),\n",
|
||||
" array([-0.21911748, -0.05445749, 0.1102025 , 0.27486249, 0.43952249,\n",
|
||||
" 0.60418248, 0.76884247, 0.93350246, 1.09816245, 1.26282245,\n",
|
||||
" 1.42748244, 1.59214243, 1.75680242, 1.92146242, 2.08612241,\n",
|
||||
" 2.2507824 , 2.41544239, 2.58010239, 2.74476238, 2.90942237,\n",
|
||||
" 3.07408236]),\n",
|
||||
" <a list of 20 Patch objects>)"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10a2ad990>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"hist(np.log10(abs(dtrue)), bins = 20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"m1D = Mesh.TensorMesh([5])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.colorbar.Colorbar instance at 0x10a7b12d8>"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10a698690>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"figsize(14*0.5,7*0.5)\n",
|
||||
"circmodelest = mapping*mtrue\n",
|
||||
"dat = mesh.plotImage(np.log10(circmodelest), clim=(-4, 1), grid=True, gridOpts={'alpha':0.5})\n",
|
||||
"plot(xz_A[:,0], xz_A[:,1], 'w.')\n",
|
||||
"plot(xz_B[:,0], xz_B[:,1], 'k.')\n",
|
||||
"plot(xz_N[:,0], xz_N[:,1], 'r.')\n",
|
||||
"# plot(temp.rxList[0].locs[0][:,0], temp.rxList[0].locs[0][:,1], 'bo')\n",
|
||||
"plt.colorbar(dat[0])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.\n",
|
||||
" ***Done using same solver as the problem***\n",
|
||||
"SimPEG.SaveModelEveryIteration will save your models as: '###-InversionModel-2015-11-10-16-33.npy'\n",
|
||||
"============================ Inexact Gauss Newton ============================\n",
|
||||
" # beta phi_d phi_m f |proj(x-g)-x| LS Comment \n",
|
||||
"-----------------------------------------------------------------------------\n",
|
||||
" 0 0.00e+00 4.69e+03 0.00e+00 4.69e+03 3.97e+04 0 \n",
|
||||
" 1 0.00e+00 2.80e+03 4.41e-03 2.80e+03 1.09e+03 0 \n",
|
||||
" 2 0.00e+00 2.59e+03 3.14e+01 2.59e+03 1.43e+04 1 \n",
|
||||
" 3 0.00e+00 1.31e+03 4.23e+01 1.31e+03 1.48e+04 3 \n",
|
||||
" 4 0.00e+00 1.22e+03 5.74e+01 1.22e+03 1.49e+04 4 \n",
|
||||
" 5 0.00e+00 8.55e+02 3.58e+01 8.55e+02 2.20e+04 0 \n",
|
||||
" 6 0.00e+00 6.43e+02 5.20e+01 6.43e+02 1.71e+04 3 \n",
|
||||
" 7 0.00e+00 4.57e+02 4.43e+01 4.57e+02 5.20e+03 0 \n",
|
||||
" 8 0.00e+00 3.83e+02 6.55e+01 3.83e+02 1.54e+04 2 \n",
|
||||
" 9 0.00e+00 3.67e+02 5.78e+01 3.67e+02 5.76e+03 1 \n",
|
||||
" 10 0.00e+00 3.31e+02 6.61e+01 3.31e+02 1.20e+04 1 \n",
|
||||
" 11 0.00e+00 3.30e+02 6.15e+01 3.30e+02 1.76e+04 2 \n",
|
||||
" 12 0.00e+00 2.01e+02 4.84e+01 2.01e+02 8.75e+03 0 \n",
|
||||
" 13 0.00e+00 1.54e+02 6.25e+01 1.54e+02 8.56e+03 2 \n",
|
||||
"------------------------- STOP! -------------------------\n",
|
||||
"1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 4.6952e+02\n",
|
||||
"0 : |xc-x_last| = 5.6930e-01 <= tolX*(1+|x0|) = 1.2421e-19\n",
|
||||
"0 : |proj(x-g)-x| = 8.5645e+03 <= tolG = 1.0000e-01\n",
|
||||
"0 : |proj(x-g)-x| = 8.5645e+03 <= 1e3*eps = 1.0000e-02\n",
|
||||
"0 : maxIter = 30 <= iter = 14\n",
|
||||
"------------------------- DONE! -------------------------\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"survey.dobs = dtrue\n",
|
||||
"dmis = DataMisfit.l2_DataMisfit(survey)\n",
|
||||
"dmis.Wd = 1./(0.01*abs(dtrue)+1.)\n",
|
||||
"reg = Regularization.BaseRegularization(m1D)\n",
|
||||
"opt = Optimization.InexactGaussNewton(maxIter=30,tolX=1e-20, maxIterLS=20)\n",
|
||||
"opt.remember('xc')\n",
|
||||
"invProb = InvProblem.BaseInvProblem(dmis, reg, opt)\n",
|
||||
"invProb.beta = 0.\n",
|
||||
"betaSched = Directives.BetaSchedule(coolingFactor=1, coolingRate=1)\n",
|
||||
"targetmis = Directives.TargetMisfit()\n",
|
||||
"savemodel = Directives.SaveModelEveryIteration()\n",
|
||||
"inv = Inversion.BaseInversion(invProb, directiveList=[betaSched,targetmis, savemodel])\n",
|
||||
"reg.mref = m0\n",
|
||||
"mopt = inv.run(m0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"XC = opt.recall('xc')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from ipywidgets import interact, IntSlider"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def viewinv(iteration):\n",
|
||||
"# iteration = 15\n",
|
||||
" figsize(10,6)\n",
|
||||
" ax1 = plt.subplot(211)\n",
|
||||
" circmodelest = mapping*mtrue\n",
|
||||
" if iteration > opt.iter-1:\n",
|
||||
" circmodeltrue = mapping*mopt\n",
|
||||
" else:\n",
|
||||
" circmodeltrue = mapping*XC[iteration]\n",
|
||||
" mesh.plotImage(np.log10(circmodelest), ax=ax1, clim=(-3, 0), grid=True, gridOpts={'alpha':0.5})\n",
|
||||
" ax2 = plt.subplot(212)\n",
|
||||
" mesh.plotImage(np.log10(circmodeltrue), ax=ax2, clim=(-3, 0), grid=True, gridOpts={'alpha':0.5})\n",
|
||||
" plt.show()\n",
|
||||
" return True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10c979dd0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"interact(viewinv, iteration=IntSlider(min=0, max=opt.iter, step = 1, value=0))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x10c8ed390>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10c8ed2d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plot(invProb.dpred, '.')\n",
|
||||
"plot(dtrue)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"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
|
||||
}
|
||||
@@ -0,0 +1,426 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from SimPEG import *"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"WARNING: pylab import has clobbered these variables: ['linalg']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%pylab inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 69,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cs = 25.\n",
|
||||
"hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
|
||||
"hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]\n",
|
||||
"hz = [(cs,7, -1.3),(cs,20)]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 72,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mesh = Mesh.TensorMesh([hx, hy, hz], \"CCN\")\n",
|
||||
"sigma = np.ones(mesh.nC)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 79,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" ---- 3-D TensorMesh ---- \n",
|
||||
" x0: -833.94\n",
|
||||
" y0: -833.94\n",
|
||||
" z0: -1071.44\n",
|
||||
" nCx: 35\n",
|
||||
" nCy: 35\n",
|
||||
" nCz: 27\n",
|
||||
" hx: 156.87, 120.67, 92.82, 71.40, 54.93, 42.25, 32.50, 21*25.00, 32.50, 42.25, 54.93, 71.40, 92.82, 120.67, 156.87\n",
|
||||
" hy: 156.87, 120.67, 92.82, 71.40, 54.93, 42.25, 32.50, 21*25.00, 32.50, 42.25, 54.93, 71.40, 92.82, 120.67, 156.87\n",
|
||||
" hz: 156.87, 120.67, 92.82, 71.40, 54.93, 42.25, 32.50, 20*25.00\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print mesh"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 81,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"33075"
|
||||
]
|
||||
},
|
||||
"execution_count": 81,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mesh.nC"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 82,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Div = mesh.faceDiv"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 83,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[['neumann', 'neumann'], ['neumann', 'neumann'], ['neumann', 'neumann']]"
|
||||
]
|
||||
},
|
||||
"execution_count": 83,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mesh.setCellGradBC(\"neumann\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 84,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Grad = mesh.cellGrad"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 101,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Afc = mesh.aveF2CC\n",
|
||||
"Msig = Utils.sdiag(1./(Afc.T*(1./sigma)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 102,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"A = Div*Msig*Grad"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 103,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# A[-1, -1] / mesh.vol[-1]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 104,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"A[0,0] /= mesh.vol[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 105,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"inds = Utils.closestPoints(mesh, np.r_[0., 0., 0.])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 106,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"q = np.zeros(mesh.nC)\n",
|
||||
"q[inds] = 1."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 109,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pymatsolver import MumpsSolver"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 110,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Ainv = MumpsSolver(A)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 111,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"phi = Ainv*q"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 115,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(27,)"
|
||||
]
|
||||
},
|
||||
"execution_count": 115,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mesh.vectorCCz.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 117,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(<matplotlib.collections.QuadMesh at 0x10e663790>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x10e663c10>)"
|
||||
]
|
||||
},
|
||||
"execution_count": 117,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10da13090>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mesh.plotSlice(phi,ind=26, grid=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 120,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"xyzM = Utils.ndgrid(mesh.vectorCCx+5., mesh.vectorCCy, np.r_[0.])\n",
|
||||
"xyzN = Utils.ndgrid(mesh.vectorCCx-5., mesh.vectorCCy, np.r_[0.])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 123,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"PM = mesh.getInterpolationMat(xyzM, \"CC\")\n",
|
||||
"PN = mesh.getInterpolationMat(xyzN, \"CC\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"PM = mesh.getInterpolationMat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 124,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data = PM*phi-PN*phi"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 126,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.image.AxesImage at 0x108812c10>"
|
||||
]
|
||||
},
|
||||
"execution_count": 126,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10ceae110>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.imshow(data.reshape(mesh.nCx, mesh.nCy))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"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
|
||||
}
|
||||
+474
-460
@@ -1,470 +1,484 @@
|
||||
{
|
||||
"metadata": {
|
||||
"name": "",
|
||||
"signature": "sha256:788d43ec10deaa6de8f97cf0db8cb6daa952455b30356e2a2a83472d9d0d4c10"
|
||||
},
|
||||
"nbformat": 3,
|
||||
"nbformat_minor": 0,
|
||||
"worksheets": [
|
||||
"cells": [
|
||||
{
|
||||
"cells": [
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"from SimPEG import *\n",
|
||||
"import simpegDCIP as DC\n",
|
||||
"from simpegem1d import Utils1D\n",
|
||||
"%pylab inline"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"output_type": "stream",
|
||||
"stream": "stderr",
|
||||
"text": [
|
||||
"Vendor: Continuum Analytics, Inc.\n",
|
||||
"Package: mkl\n",
|
||||
"Message: trial mode expires in 29 days\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 1
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 1,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"DC Forward Modeling of Schlumber array"
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Here we test the accuracy of DC forward modeling using analytic solution."
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Vendor: Continuum Analytics, Inc.\n",
|
||||
"Package: mkl\n",
|
||||
"Message: trial mode expires in 29 days\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": 2
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 3
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"mesh.plotGrid()"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x104395510>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 4
|
||||
},
|
||||
{
|
||||
"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": 5
|
||||
},
|
||||
{
|
||||
"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": [
|
||||
"matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 6
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"ntx = 16"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 7
|
||||
},
|
||||
{
|
||||
"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": 8
|
||||
},
|
||||
{
|
||||
"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": 9,
|
||||
"text": [
|
||||
"(-600, 600)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x10e08b5d0>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 9
|
||||
},
|
||||
{
|
||||
"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(sigma, 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('x (m)')\n",
|
||||
"ax.set_ylabel('y (m)')\n",
|
||||
"ax.text(-600, 210, '(a)', fontsize = 16)\n",
|
||||
"# fig.savefig('DCsurvey.png', dpi = 200)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 10,
|
||||
"text": [
|
||||
"<matplotlib.text.Text at 0x10ee9dcd0>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x10e069890>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 10
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"txlist = []\n",
|
||||
"rx = DC.RxDipole(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.SrcDipole([rx], [xtemp_txP[i], ytemp_tx[i], -12.5],[xtemp_txN[i], ytemp_tx[i], -12.5])\n",
|
||||
" txlist.append(tx)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 11
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"survey = DC.SurveyDC(txlist)\n",
|
||||
"problem = DC.ProblemDC_CC(mesh)\n",
|
||||
"problem.pair(survey)\n",
|
||||
"try:\n",
|
||||
" from pymatsolver import MumpsSolver\n",
|
||||
" problem.Solver = MumpsSolver\n",
|
||||
"except Exception, e:\n",
|
||||
" problem.Solver = SolverLU"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 13
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 2,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Step4: Run DC forward modeling"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"%%time\n",
|
||||
"data = survey.dpred(sigma)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"stream": "stdout",
|
||||
"text": [
|
||||
"CPU times: user 5.26 s, sys: 554 ms, total: 5.81 s\n",
|
||||
"Wall time: 4.06 s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 14
|
||||
},
|
||||
{
|
||||
"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.text(100, 122, '(b)', fontsize = 16)\n",
|
||||
"ax.legend(('True', 'simpegDC'), loc = 1, fontsize = 14)\n",
|
||||
"# fig.savefig('comp_dc.png')"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 15,
|
||||
"text": [
|
||||
"<matplotlib.legend.Legend at 0x10f990a10>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x10ea722d0>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 15
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 15
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 15
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 15
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 15
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": []
|
||||
}
|
||||
],
|
||||
"metadata": {}
|
||||
"source": [
|
||||
"from SimPEG import *\n",
|
||||
"import simpegDCIP as DC\n",
|
||||
"from simpegem1d import Utils1D\n",
|
||||
"%pylab inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"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": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step1: Generate mesh"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"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)]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x104395510>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mesh.plotGrid()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step2: Generating model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sighalf = 1e-2\n",
|
||||
"sigma = np.ones(mesh.nC)*sighalf"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"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": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### $$ \\rho_a = \\frac{V}{I}\\pi\\frac{b(b+a)}{a}$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Let $b=na$, then we rewrite above equation as:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### $$ \\rho_a = \\frac{V}{I}\\pi na(n+1)$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Since AB/2 can be a good measure for depth of investigation, we express "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### $$AB/2 = \\frac{(2n+1)a}{2}$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"matplotlib.rcParams.update({'font.size': 14, 'text.usetex': True, 'font.family': 'arial'})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ntx = 16"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(-600, 600)"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10e08b5d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"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)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.text.Text at 0x10ee9dcd0>"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10e069890>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"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(sigma, 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('x (m)')\n",
|
||||
"ax.set_ylabel('y (m)')\n",
|
||||
"ax.text(-600, 210, '(a)', fontsize = 16)\n",
|
||||
"# fig.savefig('DCsurvey.png', dpi = 200)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"txlist = []\n",
|
||||
"rx = DC.RxDipole(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.SrcDipole([rx], [xtemp_txP[i], ytemp_tx[i], -12.5],[xtemp_txN[i], ytemp_tx[i], -12.5])\n",
|
||||
" txlist.append(tx)\n",
|
||||
"survey = DC.SurveyDC(txlist)\n",
|
||||
"problem = DC.ProblemDC_CC(mesh)\n",
|
||||
"problem.pair(survey)\n",
|
||||
"try:\n",
|
||||
" from pymatsolver import MumpsSolver\n",
|
||||
" problem.Solver = MumpsSolver\n",
|
||||
"except Exception, e:\n",
|
||||
" problem.Solver = SolverLU "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step4: Run DC forward modeling"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CPU times: user 5.26 s, sys: 554 ms, total: 5.81 s\n",
|
||||
"Wall time: 4.06 s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"data = survey.dpred(sigma)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"$$ \\rho_a = \\frac{V}{I}\\pi\\frac{b(b+a)}{a}$$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.legend.Legend at 0x10f990a10>"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10ea722d0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"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.text(100, 122, '(b)', fontsize = 16)\n",
|
||||
"ax.legend(('True', 'simpegDC'), loc = 1, fontsize = 14)\n",
|
||||
"# fig.savefig('comp_dc.png')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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
|
||||
}
|
||||
+212
-69
@@ -11,10 +11,6 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Efficiency Warning: Interpolation will be slow, use setup.py!\n",
|
||||
"\n",
|
||||
" python setup.py build_ext --inplace\n",
|
||||
" \n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
@@ -33,9 +29,16 @@
|
||||
"%pylab inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Step1 Generate mesh "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -49,7 +52,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -60,7 +63,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -75,7 +78,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -83,19 +86,19 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(<matplotlib.collections.QuadMesh at 0x15c5eac8>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x15c5ef60>)"
|
||||
"(<matplotlib.collections.QuadMesh at 0x1077c3550>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x1077c39d0>)"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15940f98>"
|
||||
"<matplotlib.figure.Figure at 0x1077d5550>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -108,7 +111,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -116,14 +119,14 @@
|
||||
"source": [
|
||||
"xtemp = np.linspace(-150, 150, 21)\n",
|
||||
"ytemp = np.linspace(-150, 150, 21)\n",
|
||||
"xyz_rxP = Utils.ndgrid(xtemp-10., ytemp, np.r_[0.])\n",
|
||||
"xyz_rxM = Utils.ndgrid(xtemp-10., ytemp, np.r_[0.])\n",
|
||||
"xyz_rxN = Utils.ndgrid(xtemp+10., ytemp, np.r_[0.])\n",
|
||||
"xyz_rxM = Utils.ndgrid(xtemp, ytemp, np.r_[0.])"
|
||||
"# xyz_rxM = Utils.ndgrid(xtemp, ytemp, np.r_[0.])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -131,18 +134,52 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x159e1048>]"
|
||||
"[<matplotlib.lines.Line2D at 0x108464ed0>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEACAYAAABRQBpkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15b93f60>"
|
||||
"<matplotlib.figure.Figure at 0x10847d090>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.plot(xyz_rxP[:,0], xyz_rxP[:,1], 'k.')\n",
|
||||
"plt.plot(xyz_rxM[:,0], xyz_rxM[:,1], 'r.')\n",
|
||||
"plt.plot(xyz_rxN[:,0], xyz_rxN[:,1], 'g.')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x108590810>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVIAAAFRCAYAAAAmQSVBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x108590610>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -158,32 +195,102 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<simpegDCIP.BaseDC.RxDipole at 0x10859ae90>"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"rx"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"rx = DC.RxDipole(xyz_rxP, xyz_rxN)\n",
|
||||
"rx = DC.RxDipole(xyz_rxM, xyz_rxN)\n",
|
||||
"tx = DC.SrcDipole([rx], [-200, 0, -12.5],[+200, 0, -12.5])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 24,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"txList = []\n",
|
||||
"txList.append(DC.SrcDipole([rx], [-200, 0, -12.5],[+200, 0, -12.5]))\n",
|
||||
"txList.append(DC.SrcDipole([rx], [-200, 0, -12.5],[+100, 0, -12.5]))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<simpegDCIP.BaseDC.SrcDipole at 0x108657a90>,\n",
|
||||
" <simpegDCIP.BaseDC.SrcDipole at 0x108657ad0>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"txList"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"survey = DC.SurveyDC([tx])\n",
|
||||
"# survey = DC.SurveyDC([tx])\n",
|
||||
"survey = DC.SurveyDC(txList)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# survey.unpair()\n",
|
||||
"problem = DC.ProblemDC_CC(mesh)\n",
|
||||
"problem.pair(survey)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 31,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -198,43 +305,91 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 43,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ImportError",
|
||||
"evalue": "No module named pymatsolver",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-10-d6799536a06f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpymatsolver\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMumpsSolver\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;31mImportError\u001b[0m: No module named pymatsolver"
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CPU times: user 1.25 s, sys: 177 ms, total: 1.42 s\n",
|
||||
"Wall time: 970 ms\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from pymatsolver import MumpsSolver"
|
||||
"%%time\n",
|
||||
"dataP = survey.dpred(sigmahomo)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 44,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data = survey.dpred(sigma)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Data = (data/dataP).reshape((21,21,2), order='F')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.collections.PolyCollection at 0x109fa69d0>"
|
||||
]
|
||||
},
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEACAYAAACuzv3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10b0df390>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"problem.Solver = SolverLU\n",
|
||||
"\n",
|
||||
"data = survey.dpred(sigmahomo)"
|
||||
"plt.pcolor(Data[:,:,0].T)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -246,19 +401,19 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 34,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Msig1 = Utils.sdiag(1./(mesh.aveF2CC.T*(1./sigma)))\n",
|
||||
"Msig2 = Utils.sdiag(1./(mesh.aveF2CC.T*(1./sigmahomo)))"
|
||||
"# Msig1 = Utils.sdiag(1./(mesh.aveF2CC.T*(1./sigma)))\n",
|
||||
"# Msig2 = Utils.sdiag(1./(mesh.aveF2CC.T*(1./sigmahomo)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -270,31 +425,19 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 21,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "TypeError",
|
||||
"evalue": "unsupported operand type(s) for -: 'FieldsDC_CC' and 'FieldsDC_CC'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-22-fd20083b1fc4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mus\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mu1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mu2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mjs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mj1\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mj2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'FieldsDC_CC' and 'FieldsDC_CC'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"us = u1-u2\n",
|
||||
"js = j1-j2"
|
||||
"# us = u1-u2\n",
|
||||
"# js = j1-j2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -305,15 +448,15 @@
|
||||
"(-300, 0)"
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15fa7860>"
|
||||
"<matplotlib.figure.Figure at 0x108233510>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -338,10 +481,10 @@
|
||||
"evalue": "name 'js' is not defined",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-23-575f23801c4a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmesh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotSlice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmesh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maveF2CCV\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mjs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvType\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'CCv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnormal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Y'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mview\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'vec'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstreamOpts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"density\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"color\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;34m'w'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mxlim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mylim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mNameError\u001b[0m: name 'js' is not defined"
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-23-575f23801c4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmesh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotSlice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmesh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maveF2CCV\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mjs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvType\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'CCv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Y'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mview\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'vec'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstreamOpts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"density\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"color\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mxlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mNameError\u001b[0m: name 'js' is not defined"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
+146
-33
@@ -18,9 +18,8 @@
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Vendor: Continuum Analytics, Inc.\n",
|
||||
"Package: mkl\n",
|
||||
"Message: trial mode expires in 11 days\n"
|
||||
"WARNING: pylab import has clobbered these variables: ['linalg']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -86,7 +85,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 31,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -102,7 +101,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 32,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -114,7 +113,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 33,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -126,7 +125,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 34,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -137,15 +136,15 @@
|
||||
"(-600.0, 600.0, -600.0, 0.0)"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10ea77dd0>"
|
||||
"<matplotlib.figure.Figure at 0x10986eb90>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -160,7 +159,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 35,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -175,7 +174,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 36,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -196,7 +195,29 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 40,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"DATA = Survey.Data(survey)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tx0 = srcList[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -204,18 +225,62 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x10f150e50>]"
|
||||
"'c5f9fd3e-9e73-4561-b2c2-52a2747439e8'"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"execution_count": 55,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tx0.uid"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import SimPEG"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"SimPEG.Survey.Data??"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x1089f8550>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10eedb350>"
|
||||
"<matplotlib.figure.Figure at 0x1087a6750>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -237,7 +302,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -257,19 +322,67 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 18,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CPU times: user 6.49 s, sys: 705 ms, total: 7.2 s\n",
|
||||
"Wall time: 4.94 s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"phi0 = survey.dpred(sigma0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"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": 22,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"phi0 = survey.dpred(sigma0)\n",
|
||||
"phiInf = survey.dpred(sigmaInf)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 23,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -280,7 +393,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 24,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -291,7 +404,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 25,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -314,7 +427,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": 26,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -322,19 +435,19 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x10ffa6710>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x10ffa6990>]"
|
||||
"[<matplotlib.lines.Line2D at 0x10984ab90>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x10984ae10>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEACAYAAACpoOGTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x10f177d90>"
|
||||
"<matplotlib.figure.Figure at 0x1089dcf90>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -349,7 +462,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"execution_count": 27,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -360,7 +473,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 28,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -380,7 +493,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"execution_count": 29,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -394,7 +507,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"execution_count": 30,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
|
||||
+16
-14
@@ -15,29 +15,31 @@ class FieldsDC_CC(Problem.Fields):
|
||||
|
||||
def startup(self):
|
||||
self._cellGrad = self.survey.prob.mesh.cellGrad
|
||||
self._Mfinv = self.survey.prob.mesh.getFaceInnerProduct(invMat=True)
|
||||
|
||||
def _phi(self, phi_sol, srcList):
|
||||
phi = phi_sol
|
||||
for i, src in enumerate(srcList):
|
||||
phi_p = src.phi_p(self.survey.prob)
|
||||
if phi_p is not None:
|
||||
phi[:,i] += phi_p
|
||||
# for i, src in enumerate(srcList):
|
||||
# phi_p = src.phi_p(self.survey.prob)
|
||||
# if phi_p is not None:
|
||||
# phi[:,i] += phi_p
|
||||
return phi
|
||||
|
||||
def _e(self, phi_sol, srcList):
|
||||
e = -self._cellGrad*phi_sol
|
||||
for i, src in enumerate(srcList):
|
||||
e_p = src.e_p(self.survey.prob)
|
||||
if e_p is not None:
|
||||
e[:,i] += e_p
|
||||
# for i, src in enumerate(srcList):
|
||||
# e_p = src.e_p(self.survey.prob)
|
||||
# if e_p is not None:
|
||||
# e[:,i] += e_p
|
||||
return e
|
||||
|
||||
def _j(self, phi_sol, srcList):
|
||||
j = -self.survey.prob.Msig*self._cellGrad*phi_sol
|
||||
for i, src in enumerate(srcList):
|
||||
j_p = src.j_p(self.survey.prob)
|
||||
if j_p is not None:
|
||||
j[:,i] += j_p
|
||||
|
||||
j = -self._Mfinv*self.survey.prob.Msig*self._cellGrad*phi_sol
|
||||
# for i, src in enumerate(srcList):
|
||||
# j_p = src.j_p(self.survey.prob)
|
||||
# if j_p is not None:
|
||||
# j[:,i] += j_p
|
||||
return j
|
||||
|
||||
|
||||
@@ -176,7 +178,7 @@ class ProblemDC_CC(Problem.BaseProblem):
|
||||
G = self.mesh.cellGrad
|
||||
self._A = D*self.Msig*G
|
||||
# Remove the null space from the matrix.
|
||||
self._A[-1,-1] = 1. #self.mesh.vol[-1]
|
||||
self._A[0,0] /= self.mesh.vol[0]
|
||||
self._A = self._A.tocsc()
|
||||
return self._A
|
||||
|
||||
|
||||
Reference in new issue
Block a user