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Create pseudo-section simulation in Notebook.
Sub-functions added to BaseDC. Required for the simulation
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D Fournier committed
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# DCR Pseudo-section Simulation"
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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": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Efficiency Warning: Interpolation will be slow, use setup.py!\n",
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"\n",
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" python setup.py build_ext --inplace\n",
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" \n"
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]
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}
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],
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"source": [
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"from SimPEG import *\n",
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"from SimPEG.Examples import DC_PseudoSection_Simulation\n",
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"\n",
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"from ipywidgets import interactive, FloatText, FloatSlider, ToggleButtons #interactive plots!\n",
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"\n",
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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": 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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"Transmitter 8 of 9 -> Time:0.976000070572 sec Transmitter 8 of 9\n",
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"Forward completed\n"
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]
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},
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{
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"data": {
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truncated
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"text/plain": [
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"<matplotlib.figure.Figure at 0x154fecc0>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"fig, ax = DC_PseudoSection_Simulation.run()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now that we have a tool to forward model data in 3D space, we can experiment with different survey configurations.\n",
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"\n",
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"We here give the two options:\n",
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"pole-dipole (pdp) or dipole-dipole (dpdp).\n",
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"\n",
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"In both cases we need to specify three important parameter.\n",
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"\n",
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"a: Transmitter and receivers seperation (m)\n",
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"\n",
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"b: Dipole seperation (m)\n",
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"\n",
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"n: Number of receiver dipoles along line\n",
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"\n",
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"We can also specify values of conductivity for the background (sig0) and the two sphere anomalies (sig1 and sig2)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Transmitter 8 of 9 -> Time:0.976999998093 sec Transmitter 8 of 9\n",
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"Forward completed\n"
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]
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},
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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 0x2183a90>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(<matplotlib.figure.Figure at 0x2183a90>,\n",
|
||||
" <matplotlib.axes._subplots.AxesSubplot at 0x187ccba8>)"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"Simul_Fct = lambda a,b,n, sig0, sig1, sig2, stype: DC_PseudoSection_Simulation.run(param = np.r_[a,b,n], sig=np.r_[sig0,sig1,sig2], stype = stype)\n",
|
||||
"\n",
|
||||
"interactive(Simul_Fct, a=FloatText(min=10.,max=40.,step=5.,value=30.),\n",
|
||||
" b=FloatText(min=10.,max=40.,step=5.,value=30.),\n",
|
||||
" n=FloatText(min=1,max=30,step=5,value=10.),\n",
|
||||
" sig0=FloatText(min=1e-4,max=1e+4,step=1e+1,value=1e-2),\n",
|
||||
" sig1=FloatText(min=1e-4,max=1e+4,step=1e+1,value=1e-1),\n",
|
||||
" sig2=FloatText(min=1e-4,max=1e+4,step=1e+1,value=1e-3),\n",
|
||||
" stype=ToggleButtons(options=['pdp','dpdp'],value='dpdp'))\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": "2.7.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@@ -2,7 +2,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -35,7 +35,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -49,7 +49,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -60,7 +60,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -75,7 +75,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -83,19 +83,19 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(<matplotlib.collections.QuadMesh at 0x1b15fda0>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x1b170240>)"
|
||||
"(<matplotlib.collections.QuadMesh at 0x157f41d0>,\n",
|
||||
" <matplotlib.lines.Line2D at 0x15631320>)"
|
||||
]
|
||||
},
|
||||
"execution_count": 33,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1a117c50>"
|
||||
"<matplotlib.figure.Figure at 0x15482e10>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -103,9 +103,9 @@
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
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||||
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x1b0d3a58>"
|
||||
"<matplotlib.figure.Figure at 0x1577eba8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -119,7 +119,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -134,7 +134,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -142,18 +142,18 @@
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x15a0fda0>]"
|
||||
"[<matplotlib.lines.Line2D at 0x15be3940>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"execution_count": 7,
|
||||
"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 0x15c45550>"
|
||||
"<matplotlib.figure.Figure at 0x155f4a20>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -169,7 +169,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -190,7 +190,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -203,7 +203,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -218,7 +218,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -230,7 +230,7 @@
|
||||
"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;32m<ipython-input-11-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"
|
||||
]
|
||||
}
|
||||
@@ -241,7 +241,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
@@ -254,20 +254,18 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"441"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
"ename": "IndentationError",
|
||||
"evalue": "expected an indented block (<ipython-input-13-ff092c0e869a>, line 3)",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-13-ff092c0e869a>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m \u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -528,7 +526,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.10"
|
||||
"version": "2.7.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -0,0 +1,441 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Objective:** \n",
|
||||
"\n",
|
||||
"In this tutorial we will create a simple two-sphere model and simulate DC Resistivity data for various transmitter-receiver configurations.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"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"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from SimPEG import *\n",
|
||||
"import simpegDCIP as DC\n",
|
||||
"import scipy.interpolate as interpolation\n",
|
||||
"import time\n",
|
||||
"import re"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\dominiquef.MIRAGEOSCIENCE\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\kernel\\__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n",
|
||||
" \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n",
|
||||
"WARNING: "
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"pylab import has clobbered these variables: ['linalg']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%matplotlib notebook\n",
|
||||
"%pylab inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(-200, 200)"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15e3dd30>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# First we need to create a mesh and a model.\n",
|
||||
"\n",
|
||||
"# This is our mesh\n",
|
||||
"dx = 5.\n",
|
||||
"\n",
|
||||
"hxind = [(dx,15,-1.3), (dx, 75), (dx,15,1.3)]\n",
|
||||
"hyind = [(dx,15,-1.3), (dx, 10), (dx,15,1.3)]\n",
|
||||
"hzind = [(dx,15,-1.3),(dx, 15)]\n",
|
||||
"\n",
|
||||
"mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN')\n",
|
||||
"\n",
|
||||
"# Define our model\n",
|
||||
"bckgr = 1e-2\n",
|
||||
"cond = 1e-1\n",
|
||||
"resis = 1e-3\n",
|
||||
"zloc = -50.\n",
|
||||
"xloc = 50.\n",
|
||||
"yloc = 0.\n",
|
||||
"radi = 25.\n",
|
||||
"\n",
|
||||
"# Set background conductivity\n",
|
||||
"model = np.ones(mesh.nC) * bckgr\n",
|
||||
"\n",
|
||||
"# First anomaly (conductor)\n",
|
||||
"ind = Utils.ModelBuilder.getIndicesSphere([-xloc,yloc,zloc],radi,mesh.gridCC)\n",
|
||||
"model[ind] = cond\n",
|
||||
"\n",
|
||||
"# Second anomaly (resistor)\n",
|
||||
"ind = Utils.ModelBuilder.getIndicesSphere([xloc,yloc,zloc],radi,mesh.gridCC)\n",
|
||||
"model[ind] = resis\n",
|
||||
"\n",
|
||||
"# Get index of the center\n",
|
||||
"indy = int(mesh.nCy/2)\n",
|
||||
"indz = int(np.argmin( np.abs(mesh.vectorCCz - zloc) ))\n",
|
||||
"\n",
|
||||
"# Plot the model for reference\n",
|
||||
"# Define core mesh extent\n",
|
||||
"xlim = 200\n",
|
||||
"zlim = 200\n",
|
||||
"\n",
|
||||
"plt.figure()\n",
|
||||
"ax = plt.subplot(1,2,1, aspect='equal')\n",
|
||||
"mesh.plotSlice(np.log10(model), ax =ax, normal = 'Y', ind = indy,grid=True)\n",
|
||||
"ax.set_title('E-W section at '+str(mesh.vectorCCy[indy])+' m')\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')\n",
|
||||
"plt.xlim([-xlim,xlim])\n",
|
||||
"plt.ylim([-zlim,0])\n",
|
||||
"\n",
|
||||
"ax = plt.subplot(1,2,2, aspect='equal')\n",
|
||||
"mesh.plotSlice(np.log10(model), ax =ax, normal = 'Z', ind = indz,grid=True)\n",
|
||||
"ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')\n",
|
||||
"plt.xlim([-xlim,xlim])\n",
|
||||
"plt.ylim([-xlim,xlim])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now that we have model in 3D, we can define a survey.\n",
|
||||
"There area various ground DC configurations used in the field.\n",
|
||||
"We can explore two survey types here: pole-dipole (pdp) and dipole-dipole (dpdp).\n",
|
||||
"In both cases we need to specify three important parameter.\n",
|
||||
"\n",
|
||||
"a: Seperation (m) between the transmitter and receivers\n",
|
||||
"\n",
|
||||
"b: Dipole seperation (m) of the receiver (and transmitter for dpdp)\n",
|
||||
"\n",
|
||||
"n: Number of receiver dipoles along line\n",
|
||||
"\n",
|
||||
"We also need to give a starting and end point for the survey.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(-200, 200)"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x15e3d240>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Survey parameters\n",
|
||||
"a = 30.\n",
|
||||
"b = 30.\n",
|
||||
"n = 20 # Integer number of rx dipoles\n",
|
||||
"\n",
|
||||
"# Specify the survey type: \"pdp\" | \"dpdp\"\n",
|
||||
"stype = 'dpdp'\n",
|
||||
"\n",
|
||||
"# Then specify the end points of the survey. Let's keep it simple for now and survey above the anomalies, top of the mesh\n",
|
||||
"ends = [(-175,0),(175,0)]\n",
|
||||
"ends = np.c_[np.asarray(ends),np.ones(2).T*mesh.vectorNz[-1]]\n",
|
||||
"\n",
|
||||
"# Snap the endpoints to the grid. Easier to create 2D section.\n",
|
||||
"indx = Utils.closestPoints(mesh, ends )\n",
|
||||
"locs = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*mesh.vectorNz[-1]]\n",
|
||||
"\n",
|
||||
"# We will handle the geometry of the survey for you and create all the combination of tx-rx along line\n",
|
||||
"[Tx, Rx] = DC.gen_DCIPsurvey(locs, mesh, stype, a, b, n)\n",
|
||||
"\n",
|
||||
"# Here is an example for the first tx-rx array\n",
|
||||
"fig, ax = plt.subplots(1,1, figsize = (6.5,5))\n",
|
||||
"mesh.plotSlice(np.log10(model), ax =ax, normal = 'Z', ind = indz,grid=True)\n",
|
||||
"ax.set_title('Depth at '+str(mesh.vectorCCz[indz])+' m')\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')\n",
|
||||
"\n",
|
||||
"plt.scatter(Tx[0][0,:],Tx[0][1,:],s=20,c='g')\n",
|
||||
"plt.scatter(Rx[0][:,0::3],Rx[0][:,1::3],s=20,c='y')\n",
|
||||
"plt.xlim([-xlim,xlim])\n",
|
||||
"plt.ylim([-xlim,xlim])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The next section will specify all the parameters used to forward model the data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Set boundary conditions\n",
|
||||
"mesh.setCellGradBC('neumann')\n",
|
||||
"\n",
|
||||
"# Define the differential operators needed for the DC problem\n",
|
||||
"Div = mesh.faceDiv\n",
|
||||
"Grad = mesh.cellGrad\n",
|
||||
"Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))\n",
|
||||
"\n",
|
||||
"A = Div*Msig*Grad\n",
|
||||
"\n",
|
||||
"# Change one corner to deal with nullspace\n",
|
||||
"A[0,0] = 1\n",
|
||||
"A = sp.csc_matrix(A)\n",
|
||||
"\n",
|
||||
"# We will solve the system iteratively, so a pre-conditioner is helpful\n",
|
||||
"# This is simply a Jacobi preconditioner (inverse of the main diagonal)\n",
|
||||
"dA = A.diagonal()\n",
|
||||
"P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Transmitter 8 of 9 -> Time:0.990999937057 sec Transmitter 8 of 9\n",
|
||||
"Forward completed\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Now we can solve the system for all the transmitters\n",
|
||||
"# We want to store the data\n",
|
||||
"data = []\n",
|
||||
"\n",
|
||||
"# There is probably a more elegant way to do this, but we can just for-loop through the transmitters\n",
|
||||
"for ii in range(len(Tx)):\n",
|
||||
" \n",
|
||||
" start_time = time.time() # Let's time the calculations\n",
|
||||
" \n",
|
||||
" #print(\"Transmitter %i / %i\\r\" % (ii+1,len(Tx)))\n",
|
||||
" \n",
|
||||
" # Select dipole locations for receiver\n",
|
||||
" rxloc_M = np.asarray(Rx[ii][:,0:3])\n",
|
||||
" rxloc_N = np.asarray(Rx[ii][:,3:])\n",
|
||||
" \n",
|
||||
" # Number of receivers\n",
|
||||
" nrx = rxloc_M.shape[0]\n",
|
||||
" \n",
|
||||
" # For usual cases \"dpdp\" or \"gradient\"\n",
|
||||
" if not re.match(stype,'pdp'): \n",
|
||||
" inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )\n",
|
||||
" RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] ) \n",
|
||||
" \n",
|
||||
" else: \n",
|
||||
" \n",
|
||||
" # Create an \"inifinity\" pole\n",
|
||||
" tx = np.squeeze(Tx[ii][:,0:1])\n",
|
||||
" tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2\n",
|
||||
" inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)\n",
|
||||
" RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] ) \n",
|
||||
"\n",
|
||||
"\n",
|
||||
" # Iterative Solve\n",
|
||||
" Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)\n",
|
||||
"\n",
|
||||
" # We now have the potential everywhere\n",
|
||||
" phi = mkvc(Ainvb[0])\n",
|
||||
" \n",
|
||||
" # Solve for phi on pole locations\n",
|
||||
" P1 = mesh.getInterpolationMat(rxloc_M, 'CC')\n",
|
||||
" P2 = mesh.getInterpolationMat(rxloc_N, 'CC')\n",
|
||||
" \n",
|
||||
" # Compute the potential difference\n",
|
||||
" dtemp = (P1*phi - P2*phi)*np.pi\n",
|
||||
" \n",
|
||||
" data.append( dtemp ) \n",
|
||||
" print '\\rTransmitter {0} of {1} -> Time:{2} sec'.format(ii,len(Tx),time.time()- start_time),\n",
|
||||
" \n",
|
||||
"print 'Transmitter {0} of {1}'.format(ii,len(Tx))\n",
|
||||
"print 'Forward completed'\n",
|
||||
" \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Once we have our problem, we can use the inversion tools in SimPEG to run our inversion:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x18d372e8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Let's just convert the 3D format into 2D (distance along line) and plot\n",
|
||||
"[Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(Tx,Rx)\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots(1,1, figsize = (8,7))\n",
|
||||
"plt.gca().set_aspect('equal', adjustable='box')\n",
|
||||
"\n",
|
||||
"# Plot the location of the spheres for reference\n",
|
||||
"circle1=plt.Circle((-xloc-Tx[0][0,0],zloc),radi,color='w',fill=False, lw=3)\n",
|
||||
"circle2=plt.Circle((xloc-Tx[0][0,0],zloc),radi,color='k',fill=False, lw=3)\n",
|
||||
"ax.add_artist(circle1)\n",
|
||||
"ax.add_artist(circle2)\n",
|
||||
"\n",
|
||||
"# Add the speudo section\n",
|
||||
"DC.plot_pseudoSection(Tx2d,Rx2d,data,mesh.vectorNz[-1],stype)\n",
|
||||
"\n",
|
||||
"plt.xlim([0,2*xlim])\n",
|
||||
"plt.ylim([-zlim,0])\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Back in the days (not so long ago really), geophysicists used to interpret DCR data directly from pseudo-section. Hopefully this example will convince you that interpretating speudo-section is really tricky, arguably impossible.\n",
|
||||
"Fortunately for us, we now have inversion techniques to make sense of the data."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"125.0\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print -xloc-Tx[0][0,0]"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "2.7.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
@@ -424,7 +424,7 @@ def gen_DCIPsurvey(endl, mesh, stype, a, b, n):
|
||||
Input:
|
||||
:param endl -> input endpoints [x1, y1, z1, x2, y2, z2]
|
||||
:object mesh -> SimPEG mesh object
|
||||
:switch stype -> "dpdp" (dipole-dipole) | "pdp" (pole-dipole)
|
||||
:switch stype -> "dpdp" (dipole-dipole) | "pdp" (pole-dipole) | 'gradient'
|
||||
: param a, n -> pole seperation, number of rx dipoles per tx
|
||||
|
||||
Output:
|
||||
|
||||
@@ -1,17 +1,16 @@
|
||||
import os
|
||||
from SimPEG import np, sp, Utils, Mesh, mkvc
|
||||
from SimPEG import *
|
||||
import simpegDCIP as DC
|
||||
import pylab as plt
|
||||
#from ipywidgets import interact, IntSlider
|
||||
from matplotlib import animation
|
||||
from JSAnimation import HTMLWriter
|
||||
import time
|
||||
import re
|
||||
from readUBC_DC2DMesh import readUBC_DC2DMesh
|
||||
from readUBC_DC2DModel import readUBC_DC2DModel
|
||||
from readUBC_DC2DLoc import readUBC_DC2DLoc
|
||||
from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
|
||||
from readUBC_DC3Dobs import readUBC_DC3Dobs
|
||||
#from readUBC_DC2DMesh import readUBC_DC2DMesh
|
||||
#from readUBC_DC2DModel import readUBC_DC2DModel
|
||||
#from readUBC_DC2DLoc import readUBC_DC2DLoc
|
||||
#from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
|
||||
#from readUBC_DC3Dobs import readUBC_DC3Dobs
|
||||
|
||||
#%%
|
||||
home_dir = 'C:\\Users\\dominiquef.MIRAGEOSCIENCE\\ownCloud\\Research\\Modelling\\Synthetic\\Two_Sphere'
|
||||
@@ -25,20 +24,20 @@ dsep = '\\'
|
||||
slvr = 'BiCGStab' #'LU'
|
||||
|
||||
# Preconditioner
|
||||
pcdr = 'Jacobi'#'Gauss-Seidel'#
|
||||
pcdr = 'Jacobi' #'Gauss-Seidel'#
|
||||
|
||||
# Number of padding cells to remove from plotting
|
||||
padc = 15
|
||||
|
||||
# Load UBC mesh 2D
|
||||
mesh = readUBC_DC2DMesh(home_dir + dsep + msh_file)
|
||||
mesh = DC.readUBC_DC2DMesh(home_dir + dsep + msh_file)
|
||||
|
||||
# Load model
|
||||
model = readUBC_DC2DModel(home_dir + dsep + mod_file)
|
||||
model = DC.readUBC_DC2DModel(home_dir + dsep + mod_file)
|
||||
|
||||
# load obs file
|
||||
[Tx,Rx,d,wd] = readUBC_DC3Dobs(home_dir + dsep + obs_file)
|
||||
[Tx, Rx] = convertObs_DC3D_to_2D(Tx,Rx)
|
||||
[Tx,Rx,d,wd] = DC.readUBC_DC3Dobs(home_dir + dsep + obs_file)
|
||||
[Tx, Rx] = DC.convertObs_DC3D_to_2D(Tx,Rx)
|
||||
#%% Create system
|
||||
#Set boundary conditions
|
||||
mesh.setCellGradBC('neumann')
|
||||
|
||||
@@ -20,7 +20,7 @@
|
||||
|
||||
|
||||
#%%
|
||||
from SimPEG import np, Utils, Mesh, mkvc, sp
|
||||
from SimPEG import *
|
||||
import simpegDCIP as DC
|
||||
import pylab as plt
|
||||
from pylab import get_current_fig_manager
|
||||
@@ -28,13 +28,16 @@ from scipy.interpolate import griddata
|
||||
import time
|
||||
import re
|
||||
import numpy.matlib as npm
|
||||
from readUBC_DC3Dobs import readUBC_DC3Dobs
|
||||
from readUBC_DC2DModel import readUBC_DC2DModel
|
||||
from writeUBC_DCobs import writeUBC_DCobs
|
||||
import scipy.interpolate as interpolation
|
||||
from plot_pseudoSection import plot_pseudoSection
|
||||
from gen_DCIPsurvey import gen_DCIPsurvey
|
||||
from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
|
||||
#==============================================================================
|
||||
# from readUBC_DC3Dobs import readUBC_DC3Dobs
|
||||
# from readUBC_DC2DModel import readUBC_DC2DModel
|
||||
# from writeUBC_DCobs import writeUBC_DCobs
|
||||
|
||||
# from plot_pseudoSection import plot_pseudoSection
|
||||
# from gen_DCIPsurvey import gen_DCIPsurvey
|
||||
# from convertObs_DC3D_to_2D import convertObs_DC3D_to_2D
|
||||
#==============================================================================
|
||||
from matplotlib.colors import LogNorm
|
||||
import os
|
||||
|
||||
@@ -43,7 +46,7 @@ dsep = '\\'
|
||||
#from scipy.linalg import solve_banded
|
||||
|
||||
# Load UBC mesh 3D
|
||||
mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_5m.msh')
|
||||
mesh = Mesh.TensorMesh.readUBC(home_dir + '\Mesh_5m.msh')
|
||||
#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\MtIsa_20m.msh')
|
||||
#mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_50m.msh')
|
||||
|
||||
@@ -51,7 +54,7 @@ mesh = Utils.meshutils.readUBCTensorMesh(home_dir + '\Mesh_5m.msh')
|
||||
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\MtIsa_3D.con',mesh)
|
||||
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Synthetic.con',mesh)
|
||||
#model = Utils.meshutils.readUBCTensorModel(home_dir + '\Lalor_model_50m.con',mesh)
|
||||
model = Utils.meshutils.readUBCTensorModel(home_dir + '\TwoSpheres.con',mesh)
|
||||
model = Mesh.TensorMesh.readModelUBC(mesh,home_dir + '\TwoSpheres.con')
|
||||
|
||||
#model = model**0 * 1e-2
|
||||
# Specify survey type
|
||||
@@ -66,7 +69,7 @@ n = 20
|
||||
slvr = 'BiCGStab' #'LU'
|
||||
|
||||
# Preconditioner
|
||||
pcdr = 'Jacobi'#'Gauss-Seidel'#
|
||||
pcdr = 'Jacobi'#
|
||||
|
||||
# Inversion parameter
|
||||
pct = 0.01
|
||||
@@ -99,9 +102,6 @@ if re.match(slvr,'BiCGStab'):
|
||||
dA = A.diagonal()
|
||||
P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
|
||||
|
||||
# Create Gauss-Seidel Preconditioner
|
||||
elif re.match(pcdr,'Gauss-Seidel'):
|
||||
LD = sp.tril(A,k=0)
|
||||
#LDinv = sp.linalg.splu(LD)
|
||||
|
||||
elif re.match(slvr,'LU'):
|
||||
@@ -116,8 +116,8 @@ top = int(mesh.nCz)-1
|
||||
plt.figure()
|
||||
ax_prim = plt.subplot(1,1,1)
|
||||
mesh.plotSlice(model, ind=top, normal='Z', grid=False, pcolorOpts={'alpha':0.5}, ax =ax_prim)
|
||||
plt.xlim([423000,424000])
|
||||
plt.ylim([546200,547000])
|
||||
plt.xlim([423200,423750])
|
||||
plt.ylim([546350,546650])
|
||||
plt.gca().set_aspect('equal', adjustable='box')
|
||||
|
||||
plt.show()
|
||||
@@ -153,7 +153,7 @@ var = np.c_[np.asarray(gin),np.ones(2).T*nz[-1]]
|
||||
indx = Utils.closestPoints(mesh, var )
|
||||
endl = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*nz[-1]]
|
||||
|
||||
[Tx, Rx] = gen_DCIPsurvey(endl, mesh, stype, a, b, n)
|
||||
[Tx, Rx] = DC.gen_DCIPsurvey(endl, mesh, stype, a, b, n)
|
||||
|
||||
dl_len = np.sqrt( np.sum((endl[0,:] - endl[1,:])**2) )
|
||||
dl_x = ( Tx[-1][0,1] - Tx[0][0,0] ) / dl_len
|
||||
@@ -206,10 +206,6 @@ for ii in range(len(Tx)):
|
||||
# Iterative Solve
|
||||
Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
|
||||
|
||||
# Create Gauss-Seidel Preconditioner
|
||||
elif re.match(pcdr,'Gauss-Seidel'):
|
||||
LD = sp.tril(A,k=0)
|
||||
|
||||
|
||||
phi = mkvc(Ainvb[0])
|
||||
|
||||
@@ -233,17 +229,17 @@ for ii in range(len(Tx)):
|
||||
if not re.match(stype,'gradient'):
|
||||
|
||||
#%% Write data file in UBC-DCIP3D format
|
||||
writeUBC_DCobs(home_dir+'\FWR_data3D.dat',Tx,Rx,data,unct,'3D')
|
||||
DC.writeUBC_DCobs(home_dir+'\FWR_data3D.dat',Tx,Rx,data,unct,'3D')
|
||||
|
||||
|
||||
#%% Load 3D data
|
||||
[Tx, Rx, data, wd] = readUBC_DC3Dobs(home_dir + '\FWR_data3D.dat')
|
||||
[Tx, Rx, data, wd] = DC.readUBC_DC3Dobs(home_dir + '\FWR_data3D.dat')
|
||||
|
||||
|
||||
#%% Convert 3D obs to 2D and write to file
|
||||
[Tx2d, Rx2d] = convertObs_DC3D_to_2D(Tx,Rx)
|
||||
[Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(Tx,Rx)
|
||||
|
||||
writeUBC_DCobs(home_dir+'\FWR_3D_2_2D.dat',Tx2d,Rx2d,data,unct,'2D')
|
||||
DC.writeUBC_DCobs(home_dir+'\FWR_3D_2_2D.dat',Tx2d,Rx2d,data,unct,'2D')
|
||||
|
||||
#%% Create a 2D mesh along axis of Tx end points and keep z-discretization
|
||||
dx = np.min( [ np.min(mesh.hx), np.min(mesh.hy) ])
|
||||
@@ -315,7 +311,7 @@ if not re.match(stype,'gradient'):
|
||||
axs.add_artist(circle1)
|
||||
axs.add_artist(circle2)
|
||||
|
||||
plot_pseudoSection(Tx2d,Rx2d,data,nz[-1],stype)
|
||||
DC.plot_pseudoSection(Tx2d,Rx2d,data,nz[-1],stype)
|
||||
plt.show()
|
||||
|
||||
#%% Run two inversions with different reference models and compute a DOI
|
||||
@@ -355,7 +351,7 @@ if not re.match(stype,'gradient'):
|
||||
fid.close()
|
||||
|
||||
# Export data file
|
||||
writeUBC_DCobs(inv_dir + dsep + obsfile2d,Tx2d,Rx2d,data,unct,'2D')
|
||||
DC.writeUBC_DCobs(inv_dir + dsep + obsfile2d,Tx2d,Rx2d,data,unct,'2D')
|
||||
|
||||
# Write input file
|
||||
fid = open(inv_dir + dsep + inp_file,'w')
|
||||
@@ -377,7 +373,7 @@ if not re.match(stype,'gradient'):
|
||||
|
||||
|
||||
#Load model
|
||||
minv = readUBC_DC2DModel(inv_dir + dsep + 'dcinv2d.con')
|
||||
minv = DC.readUBC_DC2DModel(inv_dir + dsep + 'dcinv2d.con')
|
||||
|
||||
axs = plt.subplot(2,1,jj+1)
|
||||
|
||||
|
||||
Reference in new issue
Block a user