{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Time-lapse tracer experiment in sand column\n", "TODO simplify the example (only keep dataset used) and add more context\n", "In this experiment, time-lapse ERT is used to monitor the infiltration of a tracer in a sand column.\n", "\n", "- injection at 40 mm/h\n", "- 2000 uS/cm for 120 min\n", "- seq2 with 250 ms on-time\n", "\n", "Plan of the experimental setup:\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "API path = /media/jkl/data/phd/tmp/resipy/src/resipy\n", "ResIPy version = 2.2.3\n", "cR2.exe found and up to date.\n", "R3t.exe found and up to date.\n", "cR3t.exe found and up to date.\n" ] } ], "source": [ " # trick to import a resipy from a local copy (you won't need that if you `pip install resipy`)\n", "import sys \n", "sys.path.append('../src')\n", "\n", "from resipy import R2\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import pyvista as pv\n", "import os\n", "import shutil\n", "\n", "datadir = '../src/examples/dc-3d-timelapse-column/'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# each sequence is actually composed of 24 repeated injection\n", "# the function below separate each set of injection into different\n", "# dataframe\n", "def writedf(fnames, wd, stride=1):\n", " if os.path.exists(wd + 'data/'):\n", " shutil.rmtree(wd + 'data/')\n", " os.mkdir(wd + 'data/')\n", " dfs = []\n", " c = 0\n", " for i, fname in enumerate(fnames):\n", " df = pd.read_csv(datadir + 'ert/' + fname + '.csv')\n", " for j in range(0, df.shape[0]//100, stride):\n", " sdf = df[j*100:(j+1)*100]\n", " dfs.append(sdf)\n", " c += 1\n", " sdf.to_csv(wd + 'data/dataset{:03.0f}.csv'.format(c), index=False)\n", " return dfs\n", "\n", "def showOutflow(fname, ax=None, label=''):\n", " df = pd.read_csv(fname, header=2, sep=' ')\n", " df['hh:mm:ss'] = pd.to_datetime(df['hh:mm:ss'])\n", " df['t'] = [(a - df['hh:mm:ss'].values[0]).seconds for a in df['hh:mm:ss']]\n", " if ax is None:\n", " fig, ax = plt.subplots()\n", " ax.plot(df['t'], df['mS'], '.-', label=label)\n", " ax.set_xlabel('Time [s]')\n", " ax.set_ylabel('mS')\n", "\n", "def showEvol(k, dt=2, ax=None): # dt is time interval in seconds\n", " vals = np.vstack([s.df['resist'].values[0::20] for s in k.surveys])\n", " xx = np.arange(0, len(k.surveys))*dt\n", " if ax == None:\n", " fig, ax = plt.subplots()\n", " for i in range(vals.shape[1]):\n", " ax.semilogy(xx, vals[:,i], '.-', label='ring {:d}'.format(i+1))\n", " ax.set_xlabel('Time [s]')\n", " ax.legend()\n", " ax.set_ylabel(r'Transfer Resistance [$\\Omega$]')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Working directory is: /media/jkl/data/phd/tmp/resipy/src/examples/dc-3d-timelapse-column\n", "clearing dirname\n", "64/64 imported\n" ] } ], "source": [ "fnames = ['202030201',\n", " '202030202']\n", "# k = R2(datadir, typ='R3t')\n", "k = R2(typ='R3t')\n", "k.dirname = datadir + 'invdir/'\n", "dfs = writedf(fnames, datadir, stride=5)\n", "k.createTimeLapseSurvey(datadir + 'data/')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R3t\n", " x y z remote buried label\n", "0 2.814583 1.625000 45.35 False False 1\n", "1 -1.625000 2.814583 45.35 False False 2\n", "2 -2.814583 -1.625000 45.35 False False 3\n", "3 1.625000 -2.814583 45.35 False False 4\n", "4 2.814583 1.625000 39.80 False False 5\n", "5 -1.625000 2.814583 39.80 False False 6\n", "6 -2.814583 -1.625000 39.80 False False 7\n", "7 1.625000 -2.814583 39.80 False False 8\n", "8 2.814583 1.625000 34.25 False False 9\n", "9 -1.625000 2.814583 34.25 False False 10\n", "10 -2.814583 -1.625000 34.25 False False 11\n", "11 1.625000 -2.814583 34.25 False False 12\n", "12 2.814583 1.625000 28.70 False False 13\n", "13 -1.625000 2.814583 28.70 False False 14\n", "14 -2.814583 -1.625000 28.70 False False 15\n", "15 1.625000 -2.814583 28.70 False False 16\n", "16 2.814583 1.625000 23.15 False False 17\n", "17 -1.625000 2.814583 23.15 False False 18\n", "18 -2.814583 -1.625000 23.15 False False 19\n", "19 1.625000 -2.814583 23.15 False False 20\n", "20 2.814583 1.625000 17.60 False False 21\n", "21 -1.625000 2.814583 17.60 False False 22\n", "22 -2.814583 -1.625000 17.60 False False 23\n", "23 1.625000 -2.814583 17.60 False False 24\n", "x float64\n", "y float64\n", "z float64\n", "remote bool\n", "buried bool\n", "label object\n", "dtype: object\n" ] } ], "source": [ "# TODO make this simpler\n", "radius = 6.5/2 # cm\n", "angles = np.linspace(0, 2*np.pi, 13)[:-1] # radian\n", "celec = np.c_[radius*np.cos(angles), radius*np.sin(angles)]\n", "elec = np.c_[np.tile(celec.T, 8).T, np.repeat(6.5+np.arange(0, 8*5.55, 5.55)[::-1], 12)]\n", "\n", "s2 = np.unique(k.surveys[0].df[['a','b','m','n']].values.flatten())\n", "ielec = k.elec['x'].astype(int).values # electrode indices used in the sequence\n", "x = elec[ielec,:]\n", "k.setElec(x)\n", "# k.importElec(datadir + 'elec.csv') # as not all electrode of a ring are used so label don't match" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Writing .in file and protocol.dat... Matching quadrupoles between surveys for difference inversion...100 in common...done in 0.19609s\n", "done!\n", "------------ INVERTING REFERENCE SURVEY ---------------\n", "\n", "\n", " >> R 3 t E R T M o d e l v2.01 <<\n", "\n", " >> Date: 31-08-2020\n", " >> My beautiful 3D survey \n", " >> I n v e r s e S o l u t i o n S e l e c t e d <<\n", " >> T e t r a h e d r a l E l e m e n t M e s h <<\n", "\n", " >> Reading mesh file \n", " >> Determining storage needed for finite element conductance matrix\n", " >> Generating index array for finite element conductance matrix\n", " >> Reading resistivity model from res0.dat \n", "\n", " >> L o g - D a t a I n v e r s i o n <<\n", " >> N o r m a l R e g u l a r i s a t i o n <<\n", "\n", " >> Memory estimates:\n", " For 1000 measurements the memory needed is: 0.452 Gb\n", " For 2000 measurements the memory needed is: 0.892 Gb\n", " For 5000 measurements the memory needed is: 2.211 Gb\n", " For 10000 measurements the memory needed is: 4.410 Gb\n", "\n", " >> Forming roughness matrix\n", "\n", " >> Number of measurements read: 70\n", "\n", " >> Total Memory required is: 0.043 Gb\n", "\n", "\n", " Processing frame 1 - output to file f001.dat\n", "\n", " Iteration 1\n", " Initial RMS Misfit: 294.80 Number of data ignored: 0\n", " Alpha: 1.342 RMS Misfit: 1.23 Roughness: 47.180\n", " Alpha: 0.623 RMS Misfit: 1.42 Roughness: 69.085\n", " Step length set to 1.000\n", " Final RMS Misfit: 1.23\n", " Attempted to update data weights and caused overshoot\n", " treating as converged\n", "\n", " Solution converged - Outputing results to file\n", "\n", " Calculating sensitivity map\n", "\n", " End of data: Terminating\n", "\n", "\n", " >> Program ended normally\n", "\n", "--------------------- MAIN INVERSION ------------------\n", "________________System-Check__________________\n", "Kernel type: Linux\n", "Processor info: \n", "4 Threads at <= 2900.0 Mhz\n", "Total memory = 7.7 Gb (usage = 60.4)\n", "Wine version = 4.0\n", "63/63 inversions completed\n", "----------- END OF INVERSION IN // ----------\n", "64/64 results parsed (64 ok; 0 failed)\n" ] } ], "source": [ "#k.surveys = k.surveys[::2][:100] # 41.25 s per survey (each survey is 100 quadrupoles) so every 2.75 min\n", "#k.param['reg_mode'] = 1 # background constrain\n", "k.importMesh(datadir + 'mesh3d.dat')\n", "# k.param['node_elec'] = np.c_[np.arange(len(ielec))+1, ielec+1]\n", "k.param['num_xy_poly'] = 0\n", "k.param['xy_poly_table'] = []\n", "k.param['zmin'] = -10\n", "k.param['zmax'] = 50\n", "k.param['a_wgt'] = 0.02\n", "# k.invert(parallel=True)\n", "k.getResults()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['param', 'elm_id', 'region', 'cellType', 'X', 'Y', 'Z', 'Resistivity',\n", " 'Resistivity(log10)', 'Sensitivity_map(log10)', 'Parameter_zones',\n", " 'Conductivity(mS/m)'],\n", " dtype='object')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "k.meshResults[0].df.columns" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "write_vtk is depreciated, use vtk instead\n" ] } ], "source": [ "#k.computeDiff() # run once\n", "res0 = np.array(k.meshResults[0].df['Resistivity'])\n", "for i, m in enumerate(k.meshResults):\n", " res = np.array(m.df['Resistivity'])\n", " m.df['difference(percent)'] = list((res-res0)/res0*100)\n", " m.vtk(datadir + 'out{:03d}.vtk'.format(i))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0, 16186.8)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "k.showRMS()\n", "steps = np.array([60, 120, 180, 240])*60\n", "fig, axs = plt.subplots(2, 1, sharex=True, figsize=(12,4))\n", "showEvol(k, dt=41.25, ax=axs[0])\n", "[axs[0].axvline(a, color='k', linestyle=':') for a in steps]\n", "showOutflow(datadir + 'outflow/20030201.txt', ax=axs[1])\n", "[axs[1].axvline(a, color='k', linestyle=':') for a in steps]\n", "axs[1].set_ylim([0.55, 1.7])\n", "axs[1].set_xlim([0, None])\n", "#fig.savefig(outputdir + 'evolution.png')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p = pv.Plotter(notebook=True)\n", "for i in np.arange(0, 40, 5):\n", " offset = i*2\n", " mesh = pv.read(datadir + 'out{:03d}.vtk'.format(i+1))\n", " mesh.translate([offset, 0, 0])\n", " p.add_mesh(mesh, scalars='difference(percent)', cmap='blues_r', clim=(-50, 0))\n", " melec = pv.PolyData(elec)\n", " melec.translate([offset, 0, 0])\n", " p.add_mesh(melec)\n", " p.add_text('{:.0f} min'.format(i*41.25/60), position=(210 + offset*8, 600), font_size=8)\n", "p.view_xz()\n", "p.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NOTE: opening all the vtk in paraview, enables to create an animated gif that shows the tracer progression in the column.\n", "\n", "" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }