.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "content/tutorials/04-magnetics/plot_2b_magnetics_mvi.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_04-magnetics_plot_2b_magnetics_mvi.py: Forward Simulation of Gradiometry Data for Magnetic Vector Models ================================================================= Here we use the module *simpeg.potential_fields.magnetics* to predict magnetic gradiometry data for magnetic vector models. The simulation is performed on a Tree mesh. For this tutorial, we focus on the following: - How to define the survey when we want to measured multiple field components - How to predict magnetic data in the case of remanence - How to include surface topography - How to construct tree meshes based on topography and survey geometry - The units of the physical property model and resulting data .. GENERATED FROM PYTHON SOURCE LINES 19-22 Import Modules -------------- .. GENERATED FROM PYTHON SOURCE LINES 22-37 .. code-block:: Python import numpy as np from scipy.interpolate import LinearNDInterpolator 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.utils import plot2Ddata, model_builder, mat_utils from simpeg import maps from simpeg.potential_fields import magnetics # sphinx_gallery_thumbnail_number = 2 .. GENERATED FROM PYTHON SOURCE LINES 38-44 Topography ---------- Here we define surface topography as an (N, 3) numpy array. Topography could also be loaded from a file. .. GENERATED FROM PYTHON SOURCE LINES 44-50 .. code-block:: Python [x_topo, y_topo] = np.meshgrid(np.linspace(-200, 200, 41), np.linspace(-200, 200, 41)) z_topo = -15 * np.exp(-(x_topo**2 + y_topo**2) / 80**2) x_topo, y_topo, z_topo = mkvc(x_topo), mkvc(y_topo), mkvc(z_topo) xyz_topo = np.c_[x_topo, y_topo, z_topo] .. GENERATED FROM PYTHON SOURCE LINES 51-59 Defining the Survey ------------------- Here, we define survey that will be used for the simulation. Magnetic surveys are simple to create. The user only needs an (N, 3) array to define the xyz locations of the observation locations, the list of field components which are to be modeled and the properties of the Earth's field. .. GENERATED FROM PYTHON SOURCE LINES 59-96 .. code-block:: Python # Define the observation locations as an (N, 3) numpy array or load them. x = np.linspace(-80.0, 80.0, 17) y = np.linspace(-80.0, 80.0, 17) x, y = np.meshgrid(x, y) x, y = mkvc(x.T), mkvc(y.T) fun_interp = LinearNDInterpolator(np.c_[x_topo, y_topo], z_topo) z = fun_interp(np.c_[x, y]) + 10 # Flight height 10 m above surface. receiver_locations = np.c_[x, y, z] # Define the component(s) of the field we want to simulate as strings within # a list. Here we measure the x, y and z derivatives of the Bz anomaly at # each observation location. components = ["bxz", "byz", "bzz"] # Use the observation locations and components to define the receivers. To # simulate data, the receivers must be defined as a list. receiver_list = magnetics.receivers.Point(receiver_locations, components=components) receiver_list = [receiver_list] # Define the inducing field H0 = (intensity [nT], inclination [deg], declination [deg]) field_inclination = 60 field_declination = 30 field_strength = 50000 source_field = magnetics.sources.UniformBackgroundField( receiver_list=receiver_list, amplitude=field_strength, inclination=field_inclination, declination=field_declination, ) # Define the survey survey = magnetics.survey.Survey(source_field) .. GENERATED FROM PYTHON SOURCE LINES 97-103 Defining an OcTree Mesh ----------------------- Here, we create the OcTree mesh that will be used to predict magnetic gradiometry data for the forward simuulation. .. GENERATED FROM PYTHON SOURCE LINES 103-136 .. code-block:: Python dx = 5 # minimum cell width (base mesh cell width) in x dy = 5 # minimum cell width (base mesh cell width) in y dz = 5 # minimum cell width (base mesh cell width) in z x_length = 240.0 # domain width in x y_length = 240.0 # domain width in y z_length = 120.0 # domain width in y # Compute number of base mesh cells required in x and y nbcx = 2 ** int(np.round(np.log(x_length / dx) / np.log(2.0))) nbcy = 2 ** int(np.round(np.log(y_length / dy) / np.log(2.0))) nbcz = 2 ** int(np.round(np.log(z_length / dz) / np.log(2.0))) # Define the base mesh hx = [(dx, nbcx)] hy = [(dy, nbcy)] hz = [(dz, nbcz)] mesh = TreeMesh([hx, hy, hz], x0="CCN") # Refine based on surface topography mesh = refine_tree_xyz( mesh, xyz_topo, octree_levels=[2, 2], method="surface", finalize=False ) # Refine box base on region of interest xp, yp, zp = np.meshgrid([-100.0, 100.0], [-100.0, 100.0], [-80.0, 0.0]) xyz = np.c_[mkvc(xp), mkvc(yp), mkvc(zp)] mesh = refine_tree_xyz(mesh, xyz, octree_levels=[2, 2], method="box", finalize=False) mesh.finalize() .. rst-class:: sphx-glr-script-out .. code-block:: none /home/vsts/work/1/s/tutorials/04-magnetics/plot_2b_magnetics_mvi.py:124: 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/04-magnetics/plot_2b_magnetics_mvi.py:132: DeprecationWarning: The box option is deprecated as of `0.9.0` please update your code to use the `TreeMesh.refine_bounding_box` functionality. It will be removed in a future version of discretize. .. GENERATED FROM PYTHON SOURCE LINES 137-152 Create Magnetic Vector Intensity Model (MVI) -------------------------------------------- Magnetic vector models are defined by three-component effective susceptibilities. To create a magnetic vector model, we must 1) Define the magnetic susceptibility for each cell. Then multiply by the unit vector direction of the inducing field. (induced contribution) 2) Define the remanent magnetization vector for each cell and normalize by the magnitude of the Earth's field (remanent contribution) 3) Sum the induced and remanent contributions 4) Define as a vector np.r_[chi_1, chi_2, chi_3] .. GENERATED FROM PYTHON SOURCE LINES 152-220 .. code-block:: Python # Define susceptibility values for each unit in SI background_susceptibility = 0.0001 sphere_susceptibility = 0.01 # Find cells active in the forward modeling (cells below surface) ind_active = active_from_xyz(mesh, xyz_topo) # Define mapping from model to active cells nC = int(ind_active.sum()) model_map = maps.IdentityMap(nP=3 * nC) # model has 3 parameters for each cell # Define susceptibility for each cell susceptibility_model = background_susceptibility * np.ones(ind_active.sum()) ind_sphere = model_builder.get_indices_sphere(np.r_[0.0, 0.0, -45.0], 15.0, mesh.gridCC) ind_sphere = ind_sphere[ind_active] susceptibility_model[ind_sphere] = sphere_susceptibility # Compute the unit direction of the inducing field in Cartesian coordinates field_direction = mat_utils.dip_azimuth2cartesian(field_inclination, field_declination) # Multiply susceptibility model to obtain the x, y, z components of the # effective susceptibility contribution from induced magnetization. susceptibility_model = np.outer(susceptibility_model, field_direction) # Define the effective susceptibility contribution for remanent magnetization to have a # magnitude of 0.006 SI, with inclination -45 and declination 90 remanence_inclination = -45.0 remanence_declination = 90.0 remanence_susceptibility = 0.01 remanence_model = np.zeros(np.shape(susceptibility_model)) effective_susceptibility_sphere = ( remanence_susceptibility * mat_utils.dip_azimuth2cartesian(remanence_inclination, remanence_declination) ) remanence_model[ind_sphere, :] = effective_susceptibility_sphere # Define effective susceptibility model as a vector np.r_[chi_x, chi_y, chi_z] plotting_model = susceptibility_model + remanence_model model = mkvc(plotting_model) # Plot Effective Susceptibility Model fig = plt.figure(figsize=(9, 4)) plotting_map = maps.InjectActiveCells(mesh, ind_active, np.nan) plotting_model = np.sqrt(np.sum(plotting_model, axis=1) ** 2) ax1 = fig.add_axes([0.1, 0.12, 0.73, 0.78]) mesh.plot_slice( plotting_map * plotting_model, normal="Y", ax=ax1, ind=int(mesh.h[1].size / 2), grid=True, clim=(np.min(plotting_model), np.max(plotting_model)), ) ax1.set_title("MVI Model at y = 0 m") ax1.set_xlabel("x (m)") ax1.set_ylabel("z (m)") ax2 = fig.add_axes([0.85, 0.12, 0.05, 0.78]) norm = mpl.colors.Normalize(vmin=np.min(plotting_model), vmax=np.max(plotting_model)) cbar = mpl.colorbar.ColorbarBase(ax2, norm=norm, orientation="vertical") cbar.set_label( "Effective Susceptibility Amplitude (SI)", rotation=270, labelpad=15, size=12 ) .. image-sg:: /content/tutorials/04-magnetics/images/sphx_glr_plot_2b_magnetics_mvi_001.png :alt: MVI Model at y = 0 m :srcset: /content/tutorials/04-magnetics/images/sphx_glr_plot_2b_magnetics_mvi_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 221-227 Simulation: Gradiometry Data for an MVI Model --------------------------------------------- Here we predict magnetic gradiometry data for an effective susceptibility model in the case of remanent magnetization. .. GENERATED FROM PYTHON SOURCE LINES 229-231 Define the forward simulation. By setting the 'store_sensitivities' keyword argument to "forward_only", we simulate the data without storing the sensitivities .. GENERATED FROM PYTHON SOURCE LINES 231-242 .. code-block:: Python simulation = magnetics.simulation.Simulation3DIntegral( survey=survey, mesh=mesh, chiMap=model_map, active_cells=ind_active, model_type="vector", store_sensitivities="forward_only", ) .. GENERATED FROM PYTHON SOURCE LINES 243-244 Compute predicted data for some model .. GENERATED FROM PYTHON SOURCE LINES 244-301 .. code-block:: Python dpred = simulation.dpred(model) n_data = len(dpred) # Plot fig = plt.figure(figsize=(13, 4)) v_max = np.max(np.abs(dpred)) ax1 = fig.add_axes([0.1, 0.15, 0.25, 0.78]) plot2Ddata( receiver_list[0].locations, dpred[0:n_data:3], ax=ax1, ncontour=30, clim=(-v_max, v_max), contourOpts={"cmap": "bwr"}, ) ax1.set_title("$dBz/dx$") ax1.set_xlabel("x (m)") ax1.set_ylabel("y (m)") ax2 = fig.add_axes([0.36, 0.15, 0.25, 0.78]) cplot2 = plot2Ddata( receiver_list[0].locations, dpred[1:n_data:3], ax=ax2, ncontour=30, clim=(-v_max, v_max), contourOpts={"cmap": "bwr"}, ) cplot2[0].set_clim((-v_max, v_max)) ax2.set_title("$dBz/dy$") ax2.set_xlabel("x (m)") ax2.set_yticks([]) ax3 = fig.add_axes([0.62, 0.15, 0.25, 0.78]) cplot3 = plot2Ddata( receiver_list[0].locations, dpred[2:n_data:3], ax=ax3, ncontour=30, clim=(-v_max, v_max), contourOpts={"cmap": "bwr"}, ) cplot3[0].set_clim((-v_max, v_max)) ax3.set_title("$dBz/dz$") ax3.set_xlabel("x (m)") ax3.set_yticks([]) ax4 = fig.add_axes([0.88, 0.15, 0.02, 0.79]) norm = mpl.colors.Normalize(vmin=-v_max, vmax=v_max) cbar = mpl.colorbar.ColorbarBase( ax4, norm=norm, orientation="vertical", cmap=mpl.cm.bwr ) cbar.set_label("$nT/m$", rotation=270, labelpad=15, size=12) plt.show() .. image-sg:: /content/tutorials/04-magnetics/images/sphx_glr_plot_2b_magnetics_mvi_002.png :alt: $dBz/dx$, $dBz/dy$, $dBz/dz$ :srcset: /content/tutorials/04-magnetics/images/sphx_glr_plot_2b_magnetics_mvi_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 7.377 seconds) **Estimated memory usage:** 288 MB .. _sphx_glr_download_content_tutorials_04-magnetics_plot_2b_magnetics_mvi.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_2b_magnetics_mvi.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_2b_magnetics_mvi.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_2b_magnetics_mvi.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_