mirror of
https://github.com/wassname/simpeg.git
synced 2026-08-11 11:26:01 +08:00
405 lines
86 KiB
Plaintext
405 lines
86 KiB
Plaintext
{
|
|
"metadata": {
|
|
"name": "",
|
|
"signature": "sha256:73d0cc81c1a231c2a808e89facb3717f7496e7473309088a86f2512a257912a1"
|
|
},
|
|
"nbformat": 3,
|
|
"nbformat_minor": 0,
|
|
"worksheets": [
|
|
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"from SimPEG import *\n",
|
|
"import simpegDC as DC\n",
|
|
"from simpegem1d import Utils1D\n",
|
|
"import simpegEM.Utils as EMUtils\n",
|
|
"%pylab inline"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"Populating the interactive namespace from numpy and matplotlib\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 1
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 1,
|
|
"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": "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": [
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stderr",
|
|
"text": [
|
|
"C:\\Users\\SEOGI\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\matplotlib\\lines.py:503: RuntimeWarning: invalid value encountered in greater_equal\n",
|
|
" return np.alltrue(x[1:] - x[0:-1] >= 0)\n"
|
|
]
|
|
},
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXwAAAABJRU5ErkJggg==\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x3d01c18>"
|
|
]
|
|
}
|
|
],
|
|
"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": "iVBORw0KGgoAAAANSUhEUgAAAtYAAADNCAYAAACcnje3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJkiRJknRE/w/JTh9bHBh1kwAAAABJRU5ErkJggg==\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0xa8d19b0>"
|
|
]
|
|
}
|
|
],
|
|
"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": "display_data",
|
|
"png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAEiCAYAAADNgWQ8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x16750ba8>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 36
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"txlist = []\n",
|
|
"rx = DC.DipoleRx(np.r_[xtemp_rxP, ytemp_rx, -12.5], np.r_[xtemp_rxN, ytemp_rx, -12.5])\n",
|
|
"for i in range(ntx): \n",
|
|
" tx = DC.DipoleTx([xtemp_txP[i], ytemp_tx[i], -12.5],[xtemp_txN[i], ytemp_tx[i], -12.5], [rx])\n",
|
|
" txlist.append(tx)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 11
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"survey = DC.SurveyDC(txlist)\n",
|
|
"problem = DC.ProblemDC(mesh)\n",
|
|
"problem.pair(survey)\n",
|
|
"problem.Solver = SolverLU\n",
|
|
"# problem.Solver = SolverWrapD(EMUtils.Solver.Mumps)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 12
|
|
},
|
|
{
|
|
"cell_type": "heading",
|
|
"level": 2,
|
|
"metadata": {},
|
|
"source": [
|
|
"Step4: Run DC forward modeling"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"data = survey.dpred(sigma)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 13
|
|
},
|
|
{
|
|
"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": "display_data",
|
|
"png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAErCAYAAAAfcL5EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7/8A2+G8FnnyU2kAAAAASUVORK5CYII=\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x171e7dd8>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 40
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": []
|
|
}
|
|
],
|
|
"metadata": {}
|
|
}
|
|
]
|
|
} |