.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "content/examples/20-published/plot_heagyetal2017_cyl_inversions.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_content_examples_20-published_plot_heagyetal2017_cyl_inversions.py: Heagy et al., 2017 1D FDEM and TDEM inversions ============================================== Here, we perform a 1D inversion using both the frequency and time domain codes. The forward simulations are conducted on a cylindrically symmetric mesh. The source is a point magnetic dipole source. This example is used in the paper Lindsey J. Heagy, Rowan Cockett, Seogi Kang, Gudni K. Rosenkjaer, Douglas W. Oldenburg, A framework for simulation and inversion in electromagnetics, Computers & Geosciences, Volume 107, 2017, Pages 1-19, ISSN 0098-3004, http://dx.doi.org/10.1016/j.cageo.2017.06.018. This example is on figshare: https://doi.org/10.6084/m9.figshare.5035175 This example was updated for SimPEG 0.14.0 on January 31st, 2020 by Joseph Capriotti .. GENERATED FROM PYTHON SOURCE LINES 21-274 .. image-sg:: /content/examples/20-published/images/sphx_glr_plot_heagyetal2017_cyl_inversions_001.png :alt: (a) Recovered Models, (b) FDEM observed vs. predicted, (c) TDEM observed vs. predicted :srcset: /content/examples/20-published/images/sphx_glr_plot_heagyetal2017_cyl_inversions_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none min skin depth = 158.11388300841895 max skin depth = 500.0 max x 1267.687908603637 min z -1242.6879086036365 max z 1242.687908603637 SimPEG.InvProblem will set Regularization.reference_model to m0. SimPEG.InvProblem will set Regularization.reference_model to m0. SimPEG.InvProblem will set Regularization.reference_model to m0. SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv. ***Done using same Solver, and solver_opts as the Simulation3DMagneticFluxDensity problem*** model has any nan: 0 ============================ Inexact Gauss Newton ============================ # beta phi_d phi_m f |proj(x-g)-x| LS Comment ----------------------------------------------------------------------------- x0 has any nan: 0 0 4.01e+00 7.90e+02 0.00e+00 7.90e+02 5.69e+02 0 1 4.01e+00 1.98e+02 3.13e+01 3.23e+02 5.12e+02 0 2 4.01e+00 5.58e+01 3.46e+01 1.94e+02 9.49e+01 0 3 1.00e+00 5.43e+01 3.20e+01 8.64e+01 6.75e+01 0 Skip BFGS 4 1.00e+00 2.10e+01 4.77e+01 6.88e+01 1.10e+02 0 5 1.00e+00 1.37e+01 4.59e+01 5.97e+01 1.87e+01 0 6 2.51e-01 1.05e+01 4.69e+01 2.22e+01 3.38e+01 0 Skip BFGS 7 2.51e-01 5.76e+00 5.70e+01 2.00e+01 5.87e+01 0 ------------------------- STOP! ------------------------- 1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 7.9079e+01 1 : |xc-x_last| = 3.7089e-01 <= tolX*(1+|x0|) = 2.4026e+00 0 : |proj(x-g)-x| = 5.8690e+01 <= tolG = 1.0000e-01 0 : |proj(x-g)-x| = 5.8690e+01 <= 1e3*eps = 1.0000e-02 0 : maxIter = 20 <= iter = 8 ------------------------- DONE! ------------------------- min diffusion distance 114.18394340269786 max diffusion distance 510.64611877484225 SimPEG.InvProblem will set Regularization.reference_model to m0. SimPEG.InvProblem will set Regularization.reference_model to m0. SimPEG.InvProblem will set Regularization.reference_model to m0. SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv. ***Done using same Solver, and solver_opts as the Simulation3DMagneticFluxDensity problem*** model has any nan: 0 ============================ Inexact Gauss Newton ============================ # beta phi_d phi_m f |proj(x-g)-x| LS Comment ----------------------------------------------------------------------------- x0 has any nan: 0 0 3.29e+00 1.64e+03 0.00e+00 1.64e+03 7.01e+02 0 1 3.29e+00 1.15e+03 8.05e+01 1.41e+03 2.20e+03 0 2 3.29e+00 8.36e+01 6.15e+01 2.86e+02 3.00e+02 0 3 8.21e-01 6.64e+01 5.34e+01 1.10e+02 2.44e+02 0 Skip BFGS 4 8.21e-01 3.94e+01 5.68e+01 8.60e+01 2.04e+02 1 5 8.21e-01 1.69e+01 5.73e+01 6.40e+01 1.99e+02 0 Skip BFGS 6 2.05e-01 5.36e+00 5.41e+01 1.65e+01 1.43e+01 0 ------------------------- STOP! ------------------------- 1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.6433e+02 1 : |xc-x_last| = 5.6398e-01 <= tolX*(1+|x0|) = 2.4026e+00 0 : |proj(x-g)-x| = 1.4283e+01 <= tolG = 1.0000e-01 0 : |proj(x-g)-x| = 1.4283e+01 <= 1e3*eps = 1.0000e-02 0 : maxIter = 20 <= iter = 7 ------------------------- DONE! ------------------------- | .. code-block:: default import discretize from SimPEG import ( maps, utils, data_misfit, regularization, optimization, inversion, inverse_problem, directives, ) import numpy as np from SimPEG.electromagnetics import frequency_domain as FDEM, time_domain as TDEM, mu_0 import matplotlib.pyplot as plt import matplotlib try: from pymatsolver import Pardiso as Solver except ImportError: from SimPEG import SolverLU as Solver def run(plotIt=True, saveFig=False): # Set up cylindrically symmeric mesh cs, ncx, ncz, npad = 10.0, 15, 25, 13 # padded cyl mesh hx = [(cs, ncx), (cs, npad, 1.3)] hz = [(cs, npad, -1.3), (cs, ncz), (cs, npad, 1.3)] mesh = discretize.CylindricalMesh([hx, 1, hz], "00C") # Conductivity model layerz = np.r_[-200.0, -100.0] layer = (mesh.cell_centers_z >= layerz[0]) & (mesh.cell_centers_z <= layerz[1]) active = mesh.cell_centers_z < 0.0 sig_half = 1e-2 # Half-space conductivity sig_air = 1e-8 # Air conductivity sig_layer = 5e-2 # Layer conductivity sigma = np.ones(mesh.shape_cells[2]) * sig_air sigma[active] = sig_half sigma[layer] = sig_layer # Mapping actMap = maps.InjectActiveCells(mesh, active, np.log(1e-8), nC=mesh.shape_cells[2]) mapping = maps.ExpMap(mesh) * maps.SurjectVertical1D(mesh) * actMap mtrue = np.log(sigma[active]) # ----- FDEM problem & survey ----- # rxlocs = utils.ndgrid([np.r_[50.0], np.r_[0], np.r_[0.0]]) bzr = FDEM.Rx.PointMagneticFluxDensitySecondary(rxlocs, "z", "real") bzi = FDEM.Rx.PointMagneticFluxDensitySecondary(rxlocs, "z", "imag") freqs = np.logspace(2, 3, 5) srcLoc = np.array([0.0, 0.0, 0.0]) print( "min skin depth = ", 500.0 / np.sqrt(freqs.max() * sig_half), "max skin depth = ", 500.0 / np.sqrt(freqs.min() * sig_half), ) print( "max x ", mesh.cell_centers_x.max(), "min z ", mesh.cell_centers_z.min(), "max z ", mesh.cell_centers_z.max(), ) source_list = [ FDEM.Src.MagDipole([bzr, bzi], freq, srcLoc, orientation="Z") for freq in freqs ] surveyFD = FDEM.Survey(source_list) prbFD = FDEM.Simulation3DMagneticFluxDensity( mesh, survey=surveyFD, sigmaMap=mapping, solver=Solver ) rel_err = 0.03 dataFD = prbFD.make_synthetic_data(mtrue, relative_error=rel_err, add_noise=True) dataFD.noise_floor = np.linalg.norm(dataFD.dclean) * 1e-5 # FDEM inversion np.random.seed(1) dmisfit = data_misfit.L2DataMisfit(simulation=prbFD, data=dataFD) regMesh = discretize.TensorMesh([mesh.h[2][mapping.maps[-1].indActive]]) reg = regularization.WeightedLeastSquares(regMesh) opt = optimization.InexactGaussNewton(maxIterCG=10) invProb = inverse_problem.BaseInvProblem(dmisfit, reg, opt) # Inversion Directives beta = directives.BetaSchedule(coolingFactor=4, coolingRate=3) betaest = directives.BetaEstimate_ByEig(beta0_ratio=1.0, seed=518936) target = directives.TargetMisfit() directiveList = [beta, betaest, target] inv = inversion.BaseInversion(invProb, directiveList=directiveList) m0 = np.log(np.ones(mtrue.size) * sig_half) reg.alpha_s = 5e-1 reg.alpha_x = 1.0 prbFD.counter = opt.counter = utils.Counter() opt.remember("xc") moptFD = inv.run(m0) # TDEM problem times = np.logspace(-4, np.log10(2e-3), 10) print( "min diffusion distance ", 1.28 * np.sqrt(times.min() / (sig_half * mu_0)), "max diffusion distance ", 1.28 * np.sqrt(times.max() / (sig_half * mu_0)), ) rx = TDEM.Rx.PointMagneticFluxDensity(rxlocs, times, "z") src = TDEM.Src.MagDipole( [rx], waveform=TDEM.Src.StepOffWaveform(), location=srcLoc, # same src location as FDEM problem ) surveyTD = TDEM.Survey([src]) prbTD = TDEM.Simulation3DMagneticFluxDensity( mesh, survey=surveyTD, sigmaMap=mapping, solver=Solver ) prbTD.time_steps = [(5e-5, 10), (1e-4, 10), (5e-4, 10)] rel_err = 0.03 dataTD = prbTD.make_synthetic_data(mtrue, relative_error=rel_err, add_noise=True) dataTD.noise_floor = np.linalg.norm(dataTD.dclean) * 1e-5 # TDEM inversion dmisfit = data_misfit.L2DataMisfit(simulation=prbTD, data=dataTD) regMesh = discretize.TensorMesh([mesh.h[2][mapping.maps[-1].indActive]]) reg = regularization.WeightedLeastSquares(regMesh) opt = optimization.InexactGaussNewton(maxIterCG=10) invProb = inverse_problem.BaseInvProblem(dmisfit, reg, opt) # directives beta = directives.BetaSchedule(coolingFactor=4, coolingRate=3) betaest = directives.BetaEstimate_ByEig(beta0_ratio=1.0, seed=518936) target = directives.TargetMisfit() directiveList = [beta, betaest, target] inv = inversion.BaseInversion(invProb, directiveList=directiveList) m0 = np.log(np.ones(mtrue.size) * sig_half) reg.alpha_s = 5e-1 reg.alpha_x = 1.0 prbTD.counter = opt.counter = utils.Counter() opt.remember("xc") moptTD = inv.run(m0) # Plot the results if plotIt: plt.figure(figsize=(10, 8)) ax0 = plt.subplot2grid((2, 2), (0, 0), rowspan=2) ax1 = plt.subplot2grid((2, 2), (0, 1)) ax2 = plt.subplot2grid((2, 2), (1, 1)) fs = 13 # fontsize matplotlib.rcParams["font.size"] = fs # Plot the model # z_true = np.repeat(mesh.cell_centers_z[active][1:], 2, axis=0) # z_true = np.r_[mesh.cell_centers_z[active][0], z_true, mesh.cell_centers_z[active][-1]] activeN = mesh.nodes_z <= 0.0 + cs / 2.0 z_true = np.repeat(mesh.nodes_z[activeN][1:-1], 2, axis=0) z_true = np.r_[mesh.nodes_z[activeN][0], z_true, mesh.nodes_z[activeN][-1]] sigma_true = np.repeat(sigma[active], 2, axis=0) ax0.semilogx(sigma_true, z_true, "k-", lw=2, label="True") ax0.semilogx( np.exp(moptFD), mesh.cell_centers_z[active], "bo", ms=6, markeredgecolor="k", markeredgewidth=0.5, label="FDEM", ) ax0.semilogx( np.exp(moptTD), mesh.cell_centers_z[active], "r*", ms=10, markeredgecolor="k", markeredgewidth=0.5, label="TDEM", ) ax0.set_ylim(-700, 0) ax0.set_xlim(5e-3, 1e-1) ax0.set_xlabel("Conductivity (S/m)", fontsize=fs) ax0.set_ylabel("Depth (m)", fontsize=fs) ax0.grid(which="both", color="k", alpha=0.5, linestyle="-", linewidth=0.2) ax0.legend(fontsize=fs, loc=4) # plot the data misfits - negative b/c we choose positive to be in the # direction of primary ax1.plot(freqs, -dataFD.dobs[::2], "k-", lw=2, label="Obs (real)") ax1.plot(freqs, -dataFD.dobs[1::2], "k--", lw=2, label="Obs (imag)") dpredFD = prbFD.dpred(moptTD) ax1.loglog( freqs, -dpredFD[::2], "bo", ms=6, markeredgecolor="k", markeredgewidth=0.5, label="Pred (real)", ) ax1.loglog( freqs, -dpredFD[1::2], "b+", ms=10, markeredgewidth=2.0, label="Pred (imag)" ) ax2.loglog(times, dataTD.dobs, "k-", lw=2, label="Obs") ax2.loglog( times, prbTD.dpred(moptTD), "r*", ms=10, markeredgecolor="k", markeredgewidth=0.5, label="Pred", ) ax2.set_xlim(times.min() - 1e-5, times.max() + 1e-4) # Labels, gridlines, etc ax2.grid(which="both", alpha=0.5, linestyle="-", linewidth=0.2) ax1.grid(which="both", alpha=0.5, linestyle="-", linewidth=0.2) ax1.set_xlabel("Frequency (Hz)", fontsize=fs) ax1.set_ylabel("Vertical magnetic field (-T)", fontsize=fs) ax2.set_xlabel("Time (s)", fontsize=fs) ax2.set_ylabel("Vertical magnetic field (T)", fontsize=fs) ax2.legend(fontsize=fs, loc=3) ax1.legend(fontsize=fs, loc=3) ax1.set_xlim(freqs.max() + 1e2, freqs.min() - 1e1) ax0.set_title("(a) Recovered Models", fontsize=fs) ax1.set_title("(b) FDEM observed vs. predicted", fontsize=fs) ax2.set_title("(c) TDEM observed vs. predicted", fontsize=fs) plt.tight_layout(pad=1.5) if saveFig is True: plt.savefig("example1.png", dpi=600) if __name__ == "__main__": run(plotIt=True, saveFig=True) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 41.779 seconds) **Estimated memory usage:** 119 MB .. _sphx_glr_download_content_examples_20-published_plot_heagyetal2017_cyl_inversions.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_heagyetal2017_cyl_inversions.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_heagyetal2017_cyl_inversions.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_