.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "content/examples/08-vrm/plot_fwd_vrm.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_08-vrm_plot_fwd_vrm.py: Predict Response from a Conductive and Magnetically Viscous Earth ================================================================= Here, we predict the vertical db/dt response over a conductive and magnetically viscous Earth for a small coincident loop system. Following the theory, the total response is approximately equal to the sum of the inductive and VRM responses modelled separately. The SimPEG.VRM module is used to model the VRM response while an analytic solution for a conductive half-space is used to model the inductive response. .. GENERATED FROM PYTHON SOURCE LINES 14-17 Import modules -------------- .. GENERATED FROM PYTHON SOURCE LINES 17-26 .. code-block:: default from SimPEG.electromagnetics import viscous_remanent_magnetization as VRM import numpy as np import discretize from SimPEG import mkvc, maps import matplotlib.pyplot as plt import matplotlib as mpl .. GENERATED FROM PYTHON SOURCE LINES 27-30 Defining the mesh ----------------- .. GENERATED FROM PYTHON SOURCE LINES 30-37 .. code-block:: default cs, ncx, ncy, ncz, npad = 2.0, 35, 35, 20, 5 hx = [(cs, npad, -1.3), (cs, ncx), (cs, npad, 1.3)] hy = [(cs, npad, -1.3), (cs, ncy), (cs, npad, 1.3)] hz = [(cs, npad, -1.3), (cs, ncz), (cs, npad, 1.3)] mesh = discretize.TensorMesh([hx, hy, hz], "CCC") .. GENERATED FROM PYTHON SOURCE LINES 38-45 Defining the model ------------------ Create xi model (amalgamated magnetic property). Here the model is made by summing a set of 3D Gaussian distributions. And only active cells have a model value. .. GENERATED FROM PYTHON SOURCE LINES 45-70 .. code-block:: default topoCells = mesh.gridCC[:, 2] < 0.0 # Define topography xyzc = mesh.gridCC[topoCells, :] c = 2 * np.pi * 8**2 pc = np.r_[4e-4, 4e-4, 4e-4, 6e-4, 8e-4, 6e-4, 8e-4, 8e-4] x_0 = np.r_[50.0, -50.0, -40.0, -20.0, -15.0, 20.0, -10.0, 25.0] y_0 = np.r_[0.0, 0.0, 40.0, 10.0, -20.0, 15.0, 0.0, 0.0] z_0 = np.r_[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] var_x = c * np.r_[3.0, 3.0, 3.0, 1.0, 3.0, 0.5, 0.1, 0.1] var_y = c * np.r_[20.0, 20.0, 1.0, 1.0, 0.4, 0.5, 0.1, 0.4] var_z = c * np.r_[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] xi_true = np.zeros(np.shape(xyzc[:, 0])) for ii in range(0, 8): xi_true += ( pc[ii] * np.exp(-((xyzc[:, 0] - x_0[ii]) ** 2) / var_x[ii]) * np.exp(-((xyzc[:, 1] - y_0[ii]) ** 2) / var_y[ii]) * np.exp(-((xyzc[:, 2] - z_0[ii]) ** 2) / var_z[ii]) ) xi_true += 1e-5 .. GENERATED FROM PYTHON SOURCE LINES 71-78 Survey ------ Here we must set the transmitter waveform, which defines the off-time decay of the VRM response. Next we define the sources, receivers and time channels for the survey. Our example is similar to an EM-63 survey. .. GENERATED FROM PYTHON SOURCE LINES 78-100 .. code-block:: default waveform = VRM.waveforms.StepOff() times = np.logspace(-5, -2, 31) # Observation times x, y = np.meshgrid(np.linspace(-30, 30, 21), np.linspace(-30, 30, 21)) z = 0.5 * np.ones(x.shape) loc = np.c_[mkvc(x), mkvc(y), mkvc(z)] # Src and Rx Locations src_list_vrm = [] for pp in range(0, loc.shape[0]): loc_pp = np.reshape(loc[pp, :], (1, 3)) rx_list_vrm = [ VRM.Rx.Point(loc_pp, times=times, field_type="dbdt", orientation="z") ] src_list_vrm.append( VRM.Src.MagDipole(rx_list_vrm, mkvc(loc[pp, :]), [0.0, 0.0, 0.01], waveform) ) survey_vrm = VRM.Survey(src_list_vrm) .. GENERATED FROM PYTHON SOURCE LINES 101-108 Simulation ---------- For the VRM problem, we used a sensitivity refinement strategy for cells that are proximal to transmitters. This is controlled through the *refinement_factor* and *refinement_distance* properties. .. GENERATED FROM PYTHON SOURCE LINES 108-147 .. code-block:: default # Defining the problem problem_vrm = VRM.Simulation3DLinear( mesh, survey=survey_vrm, indActive=topoCells, refinement_factor=3, refinement_distance=[1.25, 2.5, 3.75], ) # Predict VRM response fields_vrm = problem_vrm.fields(xi_true) n_times = len(times) n_loc = loc.shape[0] fields_vrm = np.reshape(fields_vrm, (n_loc, n_times)) # Add an artificial TEM response. An analytic solution for the response near # the surface of a conductive half-space (Nabighian, 1979) is scaled at each # location to provide lateral variability in the TEM response. sig = 1e-1 mu0 = 4 * np.pi * 1e-7 fields_tem = -(sig**1.5) * mu0**2.5 * times**-2.5 / (20 * np.pi**1.5) fields_tem = np.kron(np.ones((n_loc, 1)), np.reshape(fields_tem, (1, n_times))) c = ( np.exp(-((loc[:, 0] - 10) ** 2) / (25**2)) * np.exp(-((loc[:, 1] - 20) ** 2) / (35**2)) + np.exp(-((loc[:, 0] + 20) ** 2) / (20**2)) * np.exp(-((loc[:, 1] + 20) ** 2) / (40**2)) + 1.5 * np.exp(-((loc[:, 0] - 25) ** 2) / (10**2)) * np.exp(-((loc[:, 1] + 25) ** 2) / (10**2)) + 0.25 ) c = np.kron(np.reshape(c, (len(c), 1)), np.ones((1, n_times))) fields_tem = c * fields_tem .. rst-class:: sphx-glr-script-out .. code-block:: none CREATING T MATRIX CREATING A MATRIX .. GENERATED FROM PYTHON SOURCE LINES 148-151 Plotting -------- .. GENERATED FROM PYTHON SOURCE LINES 151-258 .. code-block:: default # Plotting the model Fig = plt.figure(figsize=(10, 10)) font_size = 12 plotMap = maps.InjectActiveCells(mesh, topoCells, 0.0) # Maps to mesh ax1 = 4 * [None] cplot1 = 3 * [None] view_str = ["X", "Y", "Z"] param_1 = [ncx, ncy, ncz] param_2 = [6, 0, 1] param_3 = [-12, 0, 0] for qq in range(0, 3): ax1[qq] = Fig.add_axes([0.07 + qq * 0.29, 0.7, 0.23, 0.23]) cplot1[qq] = mesh.plot_slice( plotMap * xi_true, normal=view_str[qq], ind=int((param_1[qq] + 2 * npad) / 2 - param_2[qq]), ax=ax1[qq], grid=True, pcolor_opts={"cmap": "gist_heat_r"}, ) cplot1[qq][0].set_clim((0.0, np.max(xi_true))) ax1[qq].set_xlabel("Y [m]", fontsize=font_size) ax1[qq].set_ylabel("Z [m]", fontsize=font_size, labelpad=-10) ax1[qq].tick_params(labelsize=font_size - 2) ax1[qq].set_title( "True Model (x = {} m)".format(param_3[qq]), fontsize=font_size + 2 ) ax1[3] = Fig.add_axes([0.89, 0.7, 0.01, 0.24]) norm = mpl.colors.Normalize(vmin=0.0, vmax=np.max(xi_true)) cbar14 = mpl.colorbar.ColorbarBase( ax1[3], cmap=mpl.cm.gist_heat_r, norm=norm, orientation="vertical" ) cbar14.set_label( r"$\Delta \chi /$ln$(\lambda_2 / \lambda_1 )$ [SI]", rotation=270, labelpad=15, size=font_size, ) # Plotting the decay ax2 = 2 * [None] n = x.shape[0] for qq in range(0, 2): ax2[qq] = Fig.add_axes([0.1 + 0.47 * qq, 0.335, 0.38, 0.29]) k = int((n**2 - 1) / 2 - 3 * n * (-1) ** qq) di_vrm = mkvc(np.abs(fields_vrm[k, :])) di_tem = mkvc(np.abs(fields_tem[k, :])) ax2[qq].loglog(times, di_tem, "r.-") ax2[qq].loglog(times, di_vrm, "b.-") ax2[qq].loglog(times, di_tem + di_vrm, "k.-") ax2[qq].set_xlabel("t [s]", fontsize=font_size) if qq == 0: ax2[qq].set_ylabel("|dBz/dt| [T/s]", fontsize=font_size) else: ax2[qq].axes.get_yaxis().set_visible(False) ax2[qq].tick_params(labelsize=font_size - 2) ax2[qq].set_xbound(np.min(times), np.max(times)) ax2[qq].set_ybound(1.2 * np.max(di_tem + di_vrm), 1e-5 * np.max(di_tem + di_vrm)) titlestr2 = ( "Decay at X = " + "{:.2f}".format(loc[k, 0]) + " m and Y = " + "{:.2f}".format(loc[k, 1]) + " m" ) ax2[qq].set_title(titlestr2, fontsize=font_size + 2) if qq == 0: ax2[qq].text( 1.2e-5, 18 * np.max(di_tem) / 1e5, "TEM", fontsize=font_size, color="r" ) ax2[qq].text( 1.2e-5, 6 * np.max(di_tem) / 1e5, "VRM", fontsize=font_size, color="b" ) ax2[qq].text( 1.2e-5, 2 * np.max(di_tem) / 1e5, "TEM + VRM", fontsize=font_size, color="k" ) # Plotting the TEM anomalies ax3 = 3 * [None] cplot3 = 3 * [None] cbar3 = 3 * [None] for qq in range(0, 3): ax3[qq] = Fig.add_axes([0.07 + 0.31 * qq, 0.05, 0.24, 0.21]) d = np.reshape(np.abs(fields_tem[:, 10 * qq] + fields_vrm[:, 10 * qq]), (n, n)) cplot3[qq] = ax3[qq].contourf(x, y, d.T, 40, cmap="magma_r") cbar3[qq] = plt.colorbar(cplot3[qq], ax=ax3[qq], pad=0.02, format="%.2e") cbar3[qq].set_label("[T/s]", rotation=270, labelpad=12, size=font_size) cbar3[qq].ax.tick_params(labelsize=font_size - 2) ax3[qq].set_xlabel("X [m]", fontsize=font_size) if qq == 0: ax3[qq].scatter(x, y, color=(0, 0, 0), s=4) ax3[qq].set_ylabel("Y [m]", fontsize=font_size, labelpad=-8) else: ax3[qq].axes.get_yaxis().set_visible(False) ax3[qq].tick_params(labelsize=font_size - 2) ax3[qq].set_xbound(np.min(x), np.max(x)) ax3[qq].set_ybound(np.min(y), np.max(y)) titlestr3 = "dBz/dt at t=" + "{:.1e}".format(times[10 * qq]) + " s" ax3[qq].set_title(titlestr3, fontsize=font_size + 2) plt.show() .. image-sg:: /content/examples/08-vrm/images/sphx_glr_plot_fwd_vrm_001.png :alt: True Model (x = -12 m), True Model (x = 0 m), True Model (x = 0 m), Decay at X = -9.00 m and Y = 0.00 m, Decay at X = 9.00 m and Y = 0.00 m, dBz/dt at t=1.0e-05 s, dBz/dt at t=1.0e-04 s, dBz/dt at t=1.0e-03 s :srcset: /content/examples/08-vrm/images/sphx_glr_plot_fwd_vrm_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 11.229 seconds) **Estimated memory usage:** 110 MB .. _sphx_glr_download_content_examples_08-vrm_plot_fwd_vrm.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_fwd_vrm.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_fwd_vrm.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_