Skip to content

Instantly share code, notes, and snippets.

@sgkang
Created September 22, 2014 23:52
Show Gist options
  • Save sgkang/811ca8c2196ae68940a3 to your computer and use it in GitHub Desktop.
Save sgkang/811ca8c2196ae68940a3 to your computer and use it in GitHub Desktop.
1D DC forward modeling example for schlumburger array (accuracy check)
{
"metadata": {
"name": "",
"signature": "sha256:4c81eac7c432b351e702d9aa1f39f2d14f9487903d874e4c95342ece5a8bb266"
},
"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": 113
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 114
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mesh.plotGrid()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x4ecf510>"
]
}
],
"prompt_number": 115
},
{
"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": 121
},
{
"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": 140
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ntx = 16"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 141
},
{
"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": 142
},
{
"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": 124,
"text": [
"(-600, 600)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAs8AAADICAYAAAAX6AwmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x65e8150>"
]
}
],
"prompt_number": 124
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots(1,1, figsize = (5,3))\n",
"mesh.plotSlice(sigma, grid=True, ax = ax, pcolorOpts={'cmap':'binary'})\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.set_xlim(-600, 600)\n",
"ax.set_ylim(-200, 200)\n",
"ax.set_title('Survey geometry (Plan view)')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 144,
"text": [
"<matplotlib.text.Text at 0x62fa710>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADqCAYAAACV1j88AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x4ec6210>"
]
}
],
"prompt_number": 144
},
{
"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": 129
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"survey = DC.SurveyDC(txlist)\n",
"problem = DC.ProblemDC(mesh)\n",
"problem.pair(survey)\n",
"problem.Solver = SolverWrapD(EMUtils.Solver.Mumps)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 133
},
{
"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": 134
},
{
"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.legend(('True', 'simpegDC'), loc = 1, fontsize = 14)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 148,
"text": [
"<matplotlib.legend.Legend at 0x9a06b90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEXCAYAAABsyHmSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x934c3d0>"
]
}
],
"prompt_number": 148
}
],
"metadata": {}
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment