mirror of
https://github.com/wassname/simpeg.git
synced 2026-06-28 23:10:14 +08:00
GudniRos
1425 lines
85 KiB
Plaintext
1425 lines
85 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import SimPEG as simpeg\n",
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"from scipy.constants import mu_0\n",
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"def omega(freq):\n",
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" \"\"\"Change frequency to angular frequency, omega\"\"\"\n",
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" return 2.*np.pi*freq"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"source": [
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"%pylab 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": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2529.536000000001"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.sum(100*np.cumprod(np.ones(5)*1.6))\n",
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"\n",
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" "
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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": 4,
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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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"# M = Mesh.TensorMesh([[(100.,32)],[(100.,34)],[(100.,18)]], x0='CCC')\n",
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"M = simpeg.Mesh.TensorMesh([[(100,5,-1.5),(100.,5),(100,5,1.5)],[(100,5,-1.5),(100.,5),(100,5,1.5)],[(100,5,-1.5),(100.,10),(100,5,1.5)]], x0=['C','C','C'])"
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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": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[-2478.125 -1718.75 -1212.5 -875. -650. -500. -400. -300.\n",
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" -200. -100. 0. 100. 200. 300. 400. 500.\n",
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" 650. 875. 1212.5 1718.75 2478.125]\n"
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]
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}
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],
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"source": [
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"print M.vectorNz"
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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": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.colorbar.Colorbar instance at 0x7fcef35a7d40>"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": [
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],
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fcee87a3d10>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Setup the model\n",
|
|
"conds = [1,1e-2]\n",
|
|
"elev = 300\n",
|
|
"sig = simpeg.Utils.ModelBuilder.defineBlock(M.gridCC,[-100000,-100000,-200],[100000,100000,0],conds)\n",
|
|
"sig[M.gridCC[:,2]>elev] = 1e-8\n",
|
|
"sig[M.gridCC[:,2]<-500] = 1e-1\n",
|
|
"sig[M.gridCC[:,2]<-900] = 1e-2\n",
|
|
"# sigBG = np.zeros(M.nC) + conds[0]\n",
|
|
"# sigBG[M.gridCC[:,2]>0] = 1e-8\n",
|
|
"sigBG = sig\n",
|
|
"colorbar(M.plotImage(log10(sig)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Get the mass matrix \n",
|
|
"# The model\n",
|
|
"Msig = M.getEdgeInnerProduct(sig)\n",
|
|
"MsigBG = M.getEdgeInnerProduct(sigBG)\n",
|
|
"Mmu = M.getFaceInnerProduct(mu_0, invProp=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"freq = 0.0001\n",
|
|
"C = M.edgeCurl\n",
|
|
"A = C.T*Mmu*C - 1j*omega(freq)*Msig\n",
|
|
"ARH = -(C.T*Mmu*C - 1j*omega(freq)*MsigBG)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"CPU times: user 7.68 s, sys: 27.9 ms, total: 7.7 s\n",
|
|
"Wall time: 7.7 s\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%time\n",
|
|
"# Solve the systems for each polarization\n",
|
|
"Ainv = simpeg.SolverLU(A)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Need to solve x and y polarizations of the source.\n",
|
|
"from simpegMT.Utils import get1DEfields\n",
|
|
"# Get a 1d solution for a halfspace background\n",
|
|
"mesh1d = simpeg.Mesh.TensorMesh([M.hz],np.array([M.x0[2]]))\n",
|
|
"e0_1d = get1DEfields(mesh1d,M.r(sigBG,'CC','CC','M')[0,0,:],freq,sourceAmp=None).conj()\n",
|
|
"# Setup the primary field (p) for the x (east) polarization (_px)\n",
|
|
"ex_px = np.zeros((M.vnEx),dtype=complex)\n",
|
|
"ey_px = np.zeros(M.nEy,dtype=complex)\n",
|
|
"ez_px = np.zeros(M.nEz,dtype=complex)\n",
|
|
"# Assign the source to ex_x\n",
|
|
"for i in arange(M.vnEx[0]):\n",
|
|
" for j in arange(M.vnEx[1]):\n",
|
|
" ex_px[i,j,:] = -e0_1d\n",
|
|
"ep_px = np.r_[simpeg.Utils.mkvc(ex_px),ey_px,ez_px]\n",
|
|
"rhs_px = ARH.dot(ep_px)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Setup y (north) polarization (_y)\n",
|
|
"ex_py = np.zeros(M.nEx, dtype='complex128')\n",
|
|
"ey_py = np.zeros((M.vnEy), dtype='complex128')\n",
|
|
"ez_py = np.zeros(M.nEz, dtype='complex128')\n",
|
|
"# Assign the source to ey_y\n",
|
|
"for i in arange(M.vnEy[0]):\n",
|
|
" for j in arange(M.vnEy[1]):\n",
|
|
" ey_py[i,j,:] = e0_1d \n",
|
|
" \n",
|
|
"ep_py = np.r_[ex_py,simpeg.Utils.mkvc(ey_py),ez_py]\n",
|
|
"rhs_py = ARH.dot(ep_py)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We are using splu in scipy package. This is bit slow, but on the cluster you can use mumps, which might a lot faster. We can think about having better iterative solver. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"CPU times: user 100 ms, sys: 0 ns, total: 100 ms\n",
|
|
"Wall time: 99.4 ms\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%%time\n",
|
|
"es_px = Ainv*rhs_px\n",
|
|
"es_py = Ainv*rhs_py"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Need to sum the ep and es to get the total field.\n",
|
|
"e_x = es_px #+ ep_px\n",
|
|
"e_y = es_py #+ ep_py"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"I want to visualize electrical field, which is a vector, so I average them on to cell center. Also I want to see current density ($\\vec{j} = \\sigma \\vec{e}$)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"Meinv = M.getEdgeInnerProduct(np.ones_like(sig), invMat=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"j_x = Meinv*Msig*e_x\n",
|
|
"j_y = Meinv*Msig*e_x"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(15200,)"
|
|
]
|
|
},
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"e_x.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"e_x_CC = M.aveE2CCV*e_x\n",
|
|
"e_y_CC = M.aveE2CCV*e_y\n",
|
|
"j_x_CC = M.aveE2CCV*j_x\n",
|
|
"j_y_CC = M.aveE2CCV*j_y\n",
|
|
"# j_x_CC = Utils.sdiag(np.r_[sig, sig, sig])*e_x_CC"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Then use \"plotSlice\" function, to visualize 2D sections"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": [
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"kBsLXBYRKySdXpRfGhFLJc2XtBp4Gjit2bkAkt4MXEh6o/+npDsj4riOK2zWdfcAE0g3qPu7onXC\n",
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"Pdhmo+g60rDXl5DuSneibe0qpzckIq4Frh2w79IBzxe0em6x/7sMP2GwWQZ+RJqmbx/ShDpOtK1d\n",
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"vbt2wVB8k2NT91ddgVGSa7sC2EwaF/vFYvtRsW+rXNvWin5tWxXt2tjGZr1vddUVGEW5tm0L6VaE\n",
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"a4CDSHNi7z3IMTm2q1X92rYq2lVKzO7q2gU1TbAtTyeSZjMbLLE2Gwn3YJuNvjcAkxg8sTYbiVJi\n",
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"dlfXLlBEq6+XN0n1aKhZTUREW0Gwk1jQ7mvayDlmm/WXqmN2MVXqooiYVzw/C9jSeLOipC8CyyJi\n",
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"zLqhtvNgS/qspBWS7pL0HUm7NpSdJWmVpJWS5jbsP1zS3UXZBQ37J0i6uth/q6T9ut2ehrqcJOke\n",
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"SZslHTagLNt2DUfSvKJdqySdWXV9WiHpPyQ9Kunuhn27S7pR0n2SbpC0W0PZiP5+VZE0VdJNxfvw\n",
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"l5L+Z7E/+7ZZdfo1Zhf1cdzOIG47ZufXtkpFRC034FhgTPH4M8BnisczgJ8D40jrcq9mW0//cuCI\n",
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"4vFSYF7x+AzgC8Xjk4HFFbbrpaSlDm8CDmvYn3W7hmnz2KI904r2/Rw4qOp6tVDv1wCHAnc37DsP\n",
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"+Ejx+MxO3pcVtmtv4K+LxzsBvyLN05V927xV+r7qy5hd1MFxO4O47ZidX9uq3Grbgx0RN0bEluLp\n",
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"bcCU4vEJwFURsTHShOKrgZmS9gF2jojlxXFXAG8qHj83MTlpmaqjR7v+Q4mIlRFx3yBFWbdrGM9N\n",
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"Hh8RG4GtE8D3tIj4CfDHAbsb/80vZ9vfop2/XyUi4pGI+Hnx+CnSRPyT6YO2WXX6NWaD43Yucdsx\n",
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"O7+2Vam2CfYA7yJ904I0mfjahrLGScYb968r9kPDxOQRsQl4XNLuo1nhNvRru2DoieFztFdEPFo8\n",
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"fhTYq3jczt+vckp3XB9KSoj6qm1WqTrEbOjvtvVL3O6ruOaYXZ6+nkVE0o0MPsP92RHx/eKYjwEb\n",
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"IuLKrlauA620q2b68k7diAhlPBewpJ1IPWgfiIgnpW0zLeXeNhsd/RqzwXF7EH33+c89rjlml6uv\n",
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"E+yIOLZZuaRTgfls/xPaOtJygVtNIX0jW8e2nyQb9289Z1/gIUk7ALtGxB86qnwTw7VrCD3frg4M\n",
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"bNtUtv8WnZNHJe0dEY8UP7f9ttg/kr/fuq7UtAlJ40iB+msRcU2xuy/aZqOnX2M2OG4Pol/idl/E\n",
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"Ncfs8tV2iIikecCHgRMi4tmGoiXAWyWNl7Q/MB1YHhGPAE9Imqn0te7twPcaznln8fhE4IddacTw\n",
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"Gidn76d2DfTc5PGSxpNu7FlScZ3a1fhv/k7gmob9rf79rhl40W4q6nEZcG9EfL6hKPu2WXVqErPB\n",
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"cTs32cc1x+xRUvVdllVtwCrgAeDOYvtCQ9nZpEH7K4HXN+w/HLi7KLuwYf8E4BvFNW8FplXYrjeT\n",
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|
"xrU9AzwCXNsP7Wqh3ceR7nxeDZxVdX1arPNVpBWhNhR/s9OA3YEfAPcBNwC7tfv3q7Bdrwa2kO4y\n",
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|
"3/r5mtcPbfNW6fuqL2N2UR/H7QzitmN2fm2rcvNCM2ZmZmZmJartEBEzMzMzs9HgBNvMzMzMrERO\n",
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"sM3MzMzMSuQE28zMzMysRE6wzczMzMxK5ATbzMzMzKxETrDNzMzMzErkBNvMzMzMrEROsC17kl4h\n",
|
|
"6S5JEyTtKOmXkmZUXS8zMxuc47b1O6/kaH1B0jnARGAS8GBEnFtxlczMrAnHbetnTrCtL0gaB9wB\n",
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"PAO8MvzGNjPraY7b1s88RMT6xZ7AjsBOpN4QMzPrbY7b1rfcg219QdIS4ErgAGCfiHh/xVUyM7Mm\n",
|
|
"HLetn+1QdQXMOiXpHcCfI2KxpDHATyXNiYhlFVfNzMwG4bht/c492GZmZmZmJfIYbDMzMzOzEjnB\n",
|
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"NjMzMzMrkRNsMzMzM7MSOcE2MzMzMyuRE2wzMzMzsxI5wTYzMzMzK5ETbDMzMzOzEjnBNjMzMzMr\n",
|
|
"0f8HMpKKgpyCorYAAAAASUVORK5CYII=\n"
|
|
],
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fcee86ce690>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, ax = plt.subplots(1,2, figsize = (12, 5))\n",
|
|
"dat0 = M.plotSlice(abs(e_x_CC), vType='CCv', view='vec', streamOpts={'color': 'k'}, normal='Y', ax = ax[0])\n",
|
|
"cb0 = plt.colorbar(dat0[0], ax = ax[0])\n",
|
|
"dat1 = M.plotSlice(abs(j_x_CC), vType='CCv', view='vec', streamOpts={'color': 'k'}, normal='Y', ax = ax[1])\n",
|
|
"cb1 = plt.colorbar(dat1[0], ax = ax[1])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Is it reasonable?: Based on that you put resistive target that makes sense to me; current does not want to flow on resistive target so they just do roundabout:). And see air interface. It is continuous on current but not on electric field, which looks reasonable. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Calculate the data\n",
|
|
"rx_x, rx_y = np.meshgrid(np.arange(-500,501,50),np.arange(-500,501,50))\n",
|
|
"rx_loc = np.hstack((simpeg.Utils.mkvc(rx_x,2),simpeg.Utils.mkvc(rx_y,2),elev+np.zeros((np.prod(rx_x.shape),1))))\n",
|
|
"# Get the projection matrices\n",
|
|
"Qex = M.getInterpolationMat(rx_loc,'Ex')\n",
|
|
"Qey = M.getInterpolationMat(rx_loc,'Ey')\n",
|
|
"Qez = M.getInterpolationMat(rx_loc,'Ez')\n",
|
|
"Qfx = M.getInterpolationMat(rx_loc,'Fx')\n",
|
|
"Qfy = M.getInterpolationMat(rx_loc,'Fy')\n",
|
|
"Qfz = M.getInterpolationMat(rx_loc,'Fz')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"e_x_loc = np.hstack([simpeg.Utils.mkvc(Qex*e_x,2),simpeg.Utils.mkvc(Qey*e_x,2),simpeg.Utils.mkvc(Qez*e_x,2)])\n",
|
|
"e_y_loc = np.hstack([simpeg.Utils.mkvc(Qex*e_y,2),simpeg.Utils.mkvc(Qey*e_y,2),simpeg.Utils.mkvc(Qez*e_y,2)])\n",
|
|
"Ciw = -C/(1j*omega(freq)*mu_0)\n",
|
|
"h_x_loc = np.hstack([simpeg.Utils.mkvc(Qfx*Ciw*e_x,2),simpeg.Utils.mkvc(Qfy*Ciw*e_x,2),simpeg.Utils.mkvc(Qfz*Ciw*e_x,2)])\n",
|
|
"h_y_loc = np.hstack([simpeg.Utils.mkvc(Qfx*Ciw*e_y,2),simpeg.Utils.mkvc(Qfy*Ciw*e_y,2),simpeg.Utils.mkvc(Qfz*Ciw*e_y,2)])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Make a combined matrix\n",
|
|
"dt = np.dtype([('ex1',complex),('ey1',complex),('ez1',complex),('hx1',complex),('hy1',complex),('hz1',complex),('ex2',complex),('ey2',complex),('ez2',complex),('hx2',complex),('hy2',complex),('hz2',complex)])\n",
|
|
"combMat = np.empty((len(e_x_loc)),dtype=dt)\n",
|
|
"combMat['ex1'] = e_x_loc[:,0]\n",
|
|
"combMat['ey1'] = e_x_loc[:,1]\n",
|
|
"combMat['ez1'] = e_x_loc[:,2]\n",
|
|
"combMat['ex2'] = e_y_loc[:,0]\n",
|
|
"combMat['ey2'] = e_y_loc[:,1]\n",
|
|
"combMat['ez2'] = e_y_loc[:,2]\n",
|
|
"combMat['hx1'] = h_x_loc[:,0]\n",
|
|
"combMat['hy1'] = h_x_loc[:,1]\n",
|
|
"combMat['hz1'] = h_x_loc[:,2]\n",
|
|
"combMat['hx2'] = h_y_loc[:,0]\n",
|
|
"combMat['hy2'] = h_y_loc[:,1]\n",
|
|
"combMat['hz2'] = h_y_loc[:,2]\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def calculateImpedance(fieldsData):\n",
|
|
" ''' \n",
|
|
" Function that calculates MT impedance data from a rec array with E and H field data from both polarizations\n",
|
|
" '''\n",
|
|
" zxx = (fieldsData['ex1']*fieldsData['hy2'] - fieldsData['ex2']*fieldsData['hy1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
|
|
" zxy = (-fieldsData['ex1']*fieldsData['hx2'] + fieldsData['ex2']*fieldsData['hx1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
|
|
" zyx = (fieldsData['ey1']*fieldsData['hy2'] - fieldsData['ey2']*fieldsData['hy1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
|
|
" zyy = (-fieldsData['ey1']*fieldsData['hx2'] + fieldsData['ey2']*fieldsData['hx1'])/(fieldsData['hx1']*fieldsData['hy2'] - fieldsData['hx2']*fieldsData['hy1'])\n",
|
|
" return zxx, zxy, zyx, zyy\n",
|
|
"\n",
|
|
"zxx, zxy, zyx, zyy = calculateImpedance(combMat)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(array([ 91.22293905]), array([ 42.49194681]))\n",
|
|
"(array([ 91.22293906]), array([-137.50805341]))\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"ind = np.where(np.sum(np.power(rx_loc - np.array([0,0,elev]),2),axis=1)< 5)\n",
|
|
"def appResPhs(freq,z):\n",
|
|
" app_res = ((1./(8e-7*np.pi**2))/freq)*np.abs(z)**2\n",
|
|
" app_phs = np.arctan2(z.imag,z.real)*(180/np.pi)\n",
|
|
" return app_res, app_phs\n",
|
|
"print appResPhs(freq,zyx[ind])\n",
|
|
"print appResPhs(freq,zxy[ind])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[ 1.00551946-0.00563967j] [ 2776.87342066+2515.31936068j]\n",
|
|
"(array([ 91.22293906]), array([ 41.84924238]))\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"e0_1d = e0_1d.conj()\n",
|
|
"Qex = mesh1d.getInterpolationMat(np.array([elev]),'Ex')\n",
|
|
"Qfx = mesh1d.getInterpolationMat(np.array([elev]),'Fx')\n",
|
|
"h0_1dC = -(mesh1d.nodalGrad*e0_1d)/(1j*omega(freq)*mu_0)\n",
|
|
"h0_1d = mesh1d.getInterpolationMat(mesh1d.vectorNx,'Ex')*h0_1dC\n",
|
|
"indSur = np.where(mesh1d.vectorNx==elev)\n",
|
|
"\n",
|
|
"print (Qfx*e0_1d),(Qex*h0_1dC)#e0_1d, h0_1d\n",
|
|
"print appResPhs(freq,(Qfx*e0_1d)/(Qex*h0_1dC).conj())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import simpegMT as simpegmt\n",
|
|
"sig1D = M.r(sig,'CC','CC','M')[0,0,:]\n",
|
|
"anaEd, anaEu, anaHd, anaHu = simpegmt.Utils.MT1Danalytic.getEHfields(mesh1d,sig1D,freq,mesh1d.vectorNx)\n",
|
|
"anaEtemp = anaEd+anaEu\n",
|
|
"anaHtemp = anaHd+anaHu\n",
|
|
"# Scale the solution\n",
|
|
"anaE = (anaEtemp/anaEtemp[-1])#.conj()\n",
|
|
"anaH = (anaHtemp/anaEtemp[-1])#.conj()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[ 0.00019869+0.00019869j 0.00019869+0.00019869j 0.00019869+0.00019869j\n",
|
|
" 0.00019869+0.00019869j 0.00019868+0.0001971j 0.00019867+0.00019605j\n",
|
|
" 0.00019867+0.00019605j 0.00019867+0.00019605j 0.00019867+0.00019605j\n",
|
|
" 0.00019842+0.00018849j 0.00019791+0.00018125j 0.00019791+0.00018125j\n",
|
|
" 0.00019790+0.00018126j 0.00019789+0.00018127j 0.00019789+0.00018135j\n",
|
|
" 0.00019789+0.00018143j 0.00019789+0.00018154j 0.00019789+0.00018172j\n",
|
|
" 0.00019789+0.00018199j 0.00019789+0.00018239j 0.00019789+0.00018299j]\n",
|
|
"(array([ 91.21391269]), array([ 42.48936455]))\n",
|
|
"(array([ 91.21391269]), array([-137.51063545]))\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"anaZ = anaE/anaH\n",
|
|
"indSur = np.where(mesh1d.vectorNx==elev)\n",
|
|
"print anaZ\n",
|
|
"print appResPhs(freq,anaZ[indSur])\n",
|
|
"print appResPhs(freq,-anaZ[indSur])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([-2478.125, -1718.75 , -1212.5 , -875. , -650. , -500. ,\n",
|
|
" -400. , -300. , -200. , -100. , 0. , 100. ,\n",
|
|
" 200. , 300. , 400. , 500. , 650. , 875. ,\n",
|
|
" 1212.5 , 1718.75 , 2478.125])"
|
|
]
|
|
},
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"mesh1d.vectorNx"
|
|
]
|
|
},
|
|
{
|
|
"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"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|