.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "content/tutorials/05-dcr/plot_fwd_3_dcr3d.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_tutorials_05-dcr_plot_fwd_3_dcr3d.py: DC Resistivity Forward Simulation in 3D ======================================= Here we use the module *simpeg.electromagnetics.static.resistivity* to predict DC resistivity data on an OcTree mesh. In this tutorial, we focus on the following: - How to define the survey - How to definine a tree mesh based on the survey geometry - How to define the forward simulations - How to predict DC data for a synthetic conductivity model - How to include surface topography - The units of the model and resulting data - Plotting DC data in 3D In this case, we simulate dipole-dipole data for three East-West lines and two North-South lines. .. GENERATED FROM PYTHON SOURCE LINES 25-29 Import modules -------------- .. GENERATED FROM PYTHON SOURCE LINES 29-67 .. code-block:: Python import os import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from discretize import TreeMesh from discretize.utils import mkvc, refine_tree_xyz, active_from_xyz from simpeg import maps, data from simpeg.utils import model_builder from simpeg.utils.io_utils.io_utils_electromagnetics import write_dcip_xyz from simpeg.electromagnetics.static import resistivity as dc from simpeg.electromagnetics.static.utils.static_utils import ( generate_dcip_sources_line, apparent_resistivity_from_voltage, ) # To plot DC data in 3D, the user must have the plotly package try: import plotly from simpeg.electromagnetics.static.utils.static_utils import plot_3d_pseudosection has_plotly = True except ImportError: has_plotly = False pass try: from pymatsolver import Pardiso as Solver except ImportError: from simpeg import SolverLU as Solver mpl.rcParams.update({"font.size": 16}) write_output = False # sphinx_gallery_thumbnail_number = 2 .. GENERATED FROM PYTHON SOURCE LINES 68-75 Defining Topography ------------------- Here we define surface topography as an (N, 3) numpy array. Topography could also be loaded from a file. In our case, our survey takes place within a circular depression. .. GENERATED FROM PYTHON SOURCE LINES 75-84 .. code-block:: Python x_topo, y_topo = np.meshgrid( np.linspace(-2100, 2100, 141), np.linspace(-2000, 2000, 141) ) s = np.sqrt(x_topo**2 + y_topo**2) z_topo = 10 + (1 / np.pi) * 140 * (-np.pi / 2 + np.arctan((s - 600.0) / 160.0)) x_topo, y_topo, z_topo = mkvc(x_topo), mkvc(y_topo), mkvc(z_topo) topo_xyz = np.c_[x_topo, y_topo, z_topo] .. GENERATED FROM PYTHON SOURCE LINES 85-96 Construct the DC Survey ----------------------- Here we define 5 DC lines that use a dipole-dipole electrode configuration; three lines along the East-West direction and 2 lines along the North-South direction. For each source, we must define the AB electrode locations. For each receiver we must define the MN electrode locations. Instead of creating the survey from scratch (see 1D example), we will use the *generat_dcip_sources_line* utility. This utility will give us the source list for a given DC/IP line. We can append the sources for multiple lines to create the survey. .. GENERATED FROM PYTHON SOURCE LINES 96-126 .. code-block:: Python # Define the parameters for each survey line survey_type = "dipole-dipole" data_type = "volt" dimension_type = "3D" end_locations_list = [ np.r_[-1000.0, 1000.0, 0.0, 0.0], np.r_[-350.0, -350.0, -1000.0, 1000.0], np.r_[350.0, 350.0, -1000.0, 1000.0], ] station_separation = 100.0 num_rx_per_src = 8 # The source lists for each line can be appended to create the source # list for the whole survey. source_list = [] for ii in range(0, len(end_locations_list)): source_list += generate_dcip_sources_line( survey_type, data_type, dimension_type, end_locations_list[ii], topo_xyz, num_rx_per_src, station_separation, ) # Define the survey survey = dc.survey.Survey(source_list) .. GENERATED FROM PYTHON SOURCE LINES 127-133 Create OcTree Mesh ------------------ Here, we create the OcTree mesh that will be used to predict DC data. .. GENERATED FROM PYTHON SOURCE LINES 133-167 .. code-block:: Python # Defining domain side and minimum cell size dh = 25.0 # base cell width dom_width_x = 6000.0 # domain width x dom_width_y = 6000.0 # domain width y dom_width_z = 4000.0 # domain width z nbcx = 2 ** int(np.round(np.log(dom_width_x / dh) / np.log(2.0))) # num. base cells x nbcy = 2 ** int(np.round(np.log(dom_width_y / dh) / np.log(2.0))) # num. base cells y nbcz = 2 ** int(np.round(np.log(dom_width_z / dh) / np.log(2.0))) # num. base cells z # Define the base mesh hx = [(dh, nbcx)] hy = [(dh, nbcy)] hz = [(dh, nbcz)] mesh = TreeMesh([hx, hy, hz], x0="CCN") # Mesh refinement based on topography k = np.sqrt(np.sum(topo_xyz[:, 0:2] ** 2, axis=1)) < 1200 mesh = refine_tree_xyz( mesh, topo_xyz[k, :], octree_levels=[0, 6, 8], method="surface", finalize=False ) # Mesh refinement near sources and receivers. electrode_locations = np.r_[ survey.locations_a, survey.locations_b, survey.locations_m, survey.locations_n ] unique_locations = np.unique(electrode_locations, axis=0) mesh = refine_tree_xyz( mesh, unique_locations, octree_levels=[4, 6, 4], method="radial", finalize=False ) # Finalize the mesh mesh.finalize() .. rst-class:: sphx-glr-script-out .. code-block:: none /home/vsts/work/1/s/tutorials/05-dcr/plot_fwd_3_dcr3d.py:151: DeprecationWarning: The surface option is deprecated as of `0.9.0` please update your code to use the `TreeMesh.refine_surface` functionality. It will be removed in a future version of discretize. /home/vsts/work/1/s/tutorials/05-dcr/plot_fwd_3_dcr3d.py:160: DeprecationWarning: The radial option is deprecated as of `0.9.0` please update your code to use the `TreeMesh.refine_points` functionality. It will be removed in a future version of discretize. .. GENERATED FROM PYTHON SOURCE LINES 168-176 Create Conductivity Model and Mapping for OcTree Mesh ----------------------------------------------------- Here we define the conductivity model that will be used to predict DC resistivity data. The model consists of a conductive sphere and a resistive sphere within a moderately conductive background. Note that you can carry through this work flow with a resistivity model if desired. .. GENERATED FROM PYTHON SOURCE LINES 176-234 .. code-block:: Python # Define conductivity model in S/m (or resistivity model in Ohm m) air_value = 1e-8 background_value = 1e-2 conductor_value = 1e-1 resistor_value = 1e-3 # Find active cells in forward modeling (cell below surface) ind_active = active_from_xyz(mesh, topo_xyz) # Define mapping from model to active cells nC = int(ind_active.sum()) conductivity_map = maps.InjectActiveCells(mesh, ind_active, air_value) # Define model conductivity_model = background_value * np.ones(nC) ind_conductor = model_builder.get_indices_sphere( np.r_[-350.0, 0.0, -300.0], 160.0, mesh.cell_centers[ind_active, :] ) conductivity_model[ind_conductor] = conductor_value ind_resistor = model_builder.get_indices_sphere( np.r_[350.0, 0.0, -300.0], 160.0, mesh.cell_centers[ind_active, :] ) conductivity_model[ind_resistor] = resistor_value # Plot Conductivity Model fig = plt.figure(figsize=(10, 4)) plotting_map = maps.InjectActiveCells(mesh, ind_active, np.nan) log_mod = np.log10(conductivity_model) ax1 = fig.add_axes([0.15, 0.15, 0.68, 0.75]) mesh.plot_slice( plotting_map * log_mod, ax=ax1, normal="Y", ind=int(len(mesh.h[1]) / 2), grid=True, clim=(np.log10(resistor_value), np.log10(conductor_value)), pcolor_opts={"cmap": mpl.cm.viridis}, ) ax1.set_title("Conductivity Model") ax1.set_xlabel("x (m)") ax1.set_ylabel("z (m)") ax1.set_xlim([-1000, 1000]) ax1.set_ylim([-1000, 0]) ax2 = fig.add_axes([0.84, 0.15, 0.03, 0.75]) norm = mpl.colors.Normalize( vmin=np.log10(resistor_value), vmax=np.log10(conductor_value) ) cbar = mpl.colorbar.ColorbarBase( ax2, cmap=mpl.cm.viridis, norm=norm, orientation="vertical", format="$10^{%.1f}$" ) cbar.set_label("Conductivity [S/m]", rotation=270, labelpad=15, size=12) .. image-sg:: /content/tutorials/05-dcr/images/sphx_glr_plot_fwd_3_dcr3d_001.png :alt: Conductivity Model :srcset: /content/tutorials/05-dcr/images/sphx_glr_plot_fwd_3_dcr3d_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 235-244 Project Survey to Discretized Topography ---------------------------------------- It is important that electrodes are not modeled as being in the air. Even if the electrodes are properly located along surface topography, they may lie above the *discretized* topography. This step is carried out to ensure all electrodes lie on the discretized surface. .. GENERATED FROM PYTHON SOURCE LINES 244-247 .. code-block:: Python survey.drape_electrodes_on_topography(mesh, ind_active, option="top") .. GENERATED FROM PYTHON SOURCE LINES 248-257 Predict DC Resistivity Data --------------------------- Here we predict DC resistivity data. If the keyword argument *sigmaMap* is defined, the simulation will expect a conductivity model. If the keyword argument *rhoMap* is defined, the simulation will expect a resistivity model. .. GENERATED FROM PYTHON SOURCE LINES 257-267 .. code-block:: Python # Define the DC simulation simulation = dc.simulation.Simulation3DNodal( mesh, survey=survey, sigmaMap=conductivity_map, solver=Solver ) # Predict the data by running the simulation. The data are the measured voltage # normalized by the source current in units of V/A. dpred = simulation.dpred(conductivity_model) .. GENERATED FROM PYTHON SOURCE LINES 268-279 Plot DC Data in 3D Pseudosection -------------------------------- Here we demonstrate how 3D DC resistivity data can be represented on a 3D pseudosection plot. To use this utility, you must have Python's *plotly* package. Here, we represent the data as apparent conductivities. The *plot_3d_pseudosection* utility allows the user to plot all pseudosection points, or plot the pseudosection plots that lie within some distance of one or more planes. .. GENERATED FROM PYTHON SOURCE LINES 279-332 .. code-block:: Python # Since the data are normalized voltage, we must convert predicted # to apparent conductivities. apparent_conductivity = 1 / apparent_resistivity_from_voltage( survey, dpred, ) # For large datasets or for surveys with unconventional electrode geometry, # interpretation can be challenging if we plot every datum. Here, we plot # 3 out of the 5 survey lines to better image anomalous structures. # To plot ALL of the data, simply remove the keyword argument *plane_points* # when calling *plot_3d_pseudosection*. plane_points = [] p1, p2, p3 = np.array([-1000, 0, 0]), np.array([1000, 0, 0]), np.array([0, 0, -1000]) plane_points.append([p1, p2, p3]) p1, p2, p3 = ( np.array([-350, -1000, 0]), np.array([-350, 1000, 0]), np.array([-350, 0, -1000]), ) plane_points.append([p1, p2, p3]) p1, p2, p3 = ( np.array([350, -1000, 0]), np.array([350, 1000, 0]), np.array([350, 0, -1000]), ) plane_points.append([p1, p2, p3]) if has_plotly: fig = plot_3d_pseudosection( survey, apparent_conductivity, scale="log", units="S/m", plane_points=plane_points, plane_distance=15, ) fig.update_layout( title_text="Apparent Conductivity", title_x=0.5, title_font_size=24, width=650, height=500, scene_camera=dict(center=dict(x=0.05, y=0, z=-0.4)), ) plotly.io.show(fig) else: print("INSTALL 'PLOTLY' TO VISUALIZE 3D PSEUDOSECTIONS") .. raw:: html :file: images/sphx_glr_plot_fwd_3_dcr3d_002.html .. GENERATED FROM PYTHON SOURCE LINES 333-338 Optional: Write Predicted DC Data --------------------------------- Write DC resistivity data, topography and true model .. GENERATED FROM PYTHON SOURCE LINES 338-387 .. code-block:: Python if write_output: dir_path = os.path.dirname(__file__).split(os.path.sep) dir_path.extend(["outputs"]) dir_path = os.path.sep.join(dir_path) + os.path.sep if not os.path.exists(dir_path): os.mkdir(dir_path) # Add 5% Gaussian noise to each datum np.random.seed(433) std = 0.1 * np.abs(dpred) noise = std * np.random.randn(len(dpred)) dobs = dpred + noise # Create dictionary that stores line IDs N = int(survey.nD / len(end_locations_list)) lineID = np.r_[np.ones(N), 2 * np.ones(N), 3 * np.ones(N)] out_dict = {"LINEID": lineID} # Create a survey with the original electrode locations # and not the shifted ones source_list = [] for ii in range(0, len(end_locations_list)): source_list += generate_dcip_sources_line( survey_type, data_type, dimension_type, end_locations_list[ii], topo_xyz, num_rx_per_src, station_separation, ) survey_original = dc.survey.Survey(source_list) # Write out data at their original electrode locations (not shifted) data_obj = data.Data(survey_original, dobs=dobs, standard_deviation=std) fname = dir_path + "dc_data.xyz" write_dcip_xyz( fname, data_obj, data_header="V/A", uncertainties_header="UNCERT", out_dict=out_dict, ) fname = dir_path + "topo_xyz.txt" np.savetxt(fname, topo_xyz, fmt="%.4e") .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 12.986 seconds) **Estimated memory usage:** 9 MB .. _sphx_glr_download_content_tutorials_05-dcr_plot_fwd_3_dcr3d.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_fwd_3_dcr3d.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_fwd_3_dcr3d.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_