.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "content/tutorials/01-models_mapping/plot_3_tree_models.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_01-models_mapping_plot_3_tree_models.py: Tree Meshes =========== Here we demonstrate various ways that models can be defined and mapped to OcTree meshes. Some things we consider are: - Mesh refinement near surface topography - Adding structures of various shape to the model - Parameterized models - Models with 2 or more physical properties .. GENERATED FROM PYTHON SOURCE LINES 17-20 Import modules -------------- .. GENERATED FROM PYTHON SOURCE LINES 20-31 .. code-block:: Python from discretize import TreeMesh from discretize.utils import refine_tree_xyz, active_from_xyz from simpeg.utils import mkvc, model_builder from simpeg import maps import numpy as np import matplotlib.pyplot as plt # sphinx_gallery_thumbnail_number = 3 .. GENERATED FROM PYTHON SOURCE LINES 32-37 Defining the mesh ----------------- Here, we create the OcTree mesh that will be used for all examples. .. GENERATED FROM PYTHON SOURCE LINES 37-76 .. code-block:: Python def make_example_mesh(): # Base mesh parameters dh = 5.0 # base cell size nbc = 32 # total width of mesh in terms of number of base mesh cells h = dh * np.ones(nbc) mesh = TreeMesh([h, h, h], x0="CCC") # Refine to largest possible cell size mesh.refine(3, finalize=False) return mesh def refine_topography(mesh): # Define topography and refine [xx, yy] = np.meshgrid(mesh.nodes_x, mesh.nodes_y) zz = -3 * np.exp((xx**2 + yy**2) / 60**2) + 45.0 topo = np.c_[mkvc(xx), mkvc(yy), mkvc(zz)] mesh = refine_tree_xyz( mesh, topo, octree_levels=[3, 2], method="surface", finalize=False ) return mesh def refine_box(mesh): # Refine for sphere xp, yp, zp = np.meshgrid([-55.0, 50.0], [-50.0, 50.0], [-40.0, 20.0]) xyz = np.c_[mkvc(xp), mkvc(yp), mkvc(zp)] mesh = refine_tree_xyz(mesh, xyz, octree_levels=[2], method="box", finalize=False) return mesh .. GENERATED FROM PYTHON SOURCE LINES 77-85 Topography, a block and a vertical dyke --------------------------------------- In this example we create a model containing a block and a vertical dyke that strikes along the y direction. The utility *active_from_xyz* is used to find the cells which lie below a set of xyz points defining a surface. The model consists of all cells which lie below the surface. .. GENERATED FROM PYTHON SOURCE LINES 85-131 .. code-block:: Python mesh = make_example_mesh() mesh = refine_topography(mesh) mesh = refine_box(mesh) mesh.finalize() background_value = 100.0 dyke_value = 40.0 block_value = 70.0 # Define surface topography as an (N, 3) np.array. You could also load a file # containing the xyz points [xx, yy] = np.meshgrid(mesh.nodes_x, mesh.nodes_y) zz = -3 * np.exp((xx**2 + yy**2) / 60**2) + 45.0 topo = np.c_[mkvc(xx), mkvc(yy), mkvc(zz)] # Find cells below topography and define mapping air_value = 0.0 ind_active = active_from_xyz(mesh, topo) model_map = maps.InjectActiveCells(mesh, ind_active, air_value) # Define the model on subsurface cells model = background_value * np.ones(ind_active.sum()) ind_dyke = (mesh.gridCC[ind_active, 0] > 20.0) & (mesh.gridCC[ind_active, 0] < 40.0) model[ind_dyke] = dyke_value ind_block = ( (mesh.gridCC[ind_active, 0] > -40.0) & (mesh.gridCC[ind_active, 0] < -10.0) & (mesh.gridCC[ind_active, 1] > -30.0) & (mesh.gridCC[ind_active, 1] < 30.0) & (mesh.gridCC[ind_active, 2] > -40.0) & (mesh.gridCC[ind_active, 2] < 0.0) ) model[ind_block] = block_value # We can plot a slice of the model at Y=-2.5 fig = plt.figure(figsize=(5, 5)) ax = fig.add_subplot(111) ind_slice = int(mesh.h[1].size / 2) mesh.plot_slice(model_map * model, normal="Y", ax=ax, ind=ind_slice, grid=True) ax.set_title( "Model slice at y = {} m".format(mesh.x0[1] + np.sum(mesh.h[1][0:ind_slice])) ) plt.show() .. image-sg:: /content/tutorials/01-models_mapping/images/sphx_glr_plot_3_tree_models_001.png :alt: Model slice at y = 0.0 m :srcset: /content/tutorials/01-models_mapping/images/sphx_glr_plot_3_tree_models_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/vsts/work/1/s/tutorials/01-models_mapping/plot_3_tree_models.py:59: 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/01-models_mapping/plot_3_tree_models.py:71: 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 132-141 Combo Maps ---------- Here we demonstrate how combo maps can be used to create a single mapping from the model to the mesh. In this case, our model consists of log-conductivity values but we want to plot the resistivity. To accomplish this we must take the exponent of our model values, then take the reciprocal, then map from below surface cell to the mesh. .. GENERATED FROM PYTHON SOURCE LINES 141-191 .. code-block:: Python mesh = make_example_mesh() mesh = refine_topography(mesh) mesh = refine_box(mesh) mesh.finalize() background_value = np.log(1.0 / 100.0) dyke_value = np.log(1.0 / 40.0) block_value = np.log(1.0 / 70.0) # Define surface topography [xx, yy] = np.meshgrid(mesh.nodes_x, mesh.nodes_y) zz = -3 * np.exp((xx**2 + yy**2) / 60**2) + 45.0 topo = np.c_[mkvc(xx), mkvc(yy), mkvc(zz)] # Find cells below topography air_value = 0.0 ind_active = active_from_xyz(mesh, topo) active_map = maps.InjectActiveCells(mesh, ind_active, air_value) # Define the model on subsurface cells model = background_value * np.ones(ind_active.sum()) ind_dyke = (mesh.gridCC[ind_active, 0] > 20.0) & (mesh.gridCC[ind_active, 0] < 40.0) model[ind_dyke] = dyke_value ind_block = ( (mesh.gridCC[ind_active, 0] > -40.0) & (mesh.gridCC[ind_active, 0] < -10.0) & (mesh.gridCC[ind_active, 1] > -30.0) & (mesh.gridCC[ind_active, 1] < 30.0) & (mesh.gridCC[ind_active, 2] > -40.0) & (mesh.gridCC[ind_active, 2] < 0.0) ) model[ind_block] = block_value # Define a single mapping from model to mesh exponential_map = maps.ExpMap() reciprocal_map = maps.ReciprocalMap() model_map = active_map * reciprocal_map * exponential_map # Plot fig = plt.figure(figsize=(5, 5)) ax = fig.add_subplot(111) ind_slice = int(mesh.h[1].size / 2) mesh.plot_slice(model_map * model, normal="Y", ax=ax, ind=ind_slice, grid=True) ax.set_title( "Model slice at y = {} m".format(mesh.x0[1] + np.sum(mesh.h[1][0:ind_slice])) ) plt.show() .. image-sg:: /content/tutorials/01-models_mapping/images/sphx_glr_plot_3_tree_models_002.png :alt: Model slice at y = 0.0 m :srcset: /content/tutorials/01-models_mapping/images/sphx_glr_plot_3_tree_models_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 192-200 Models with arbitrary shapes ---------------------------- Here we show how model building utilities are used to make more complicated structural models. The process of adding a new unit is twofold: 1) we must find the indicies for mesh cells that lie within the new unit, 2) we replace the prexisting physical property value for those cells. .. GENERATED FROM PYTHON SOURCE LINES 200-250 .. code-block:: Python mesh = make_example_mesh() mesh = refine_topography(mesh) mesh = refine_box(mesh) mesh.finalize() background_value = 100.0 dyke_value = 40.0 sphere_value = 70.0 # Define surface topography [xx, yy] = np.meshgrid(mesh.nodes_x, mesh.nodes_y) zz = -3 * np.exp((xx**2 + yy**2) / 60**2) + 45.0 topo = np.c_[mkvc(xx), mkvc(yy), mkvc(zz)] # Set active cells and define unit values air_value = 0.0 ind_active = active_from_xyz(mesh, topo) model_map = maps.InjectActiveCells(mesh, ind_active, air_value) # Define model for cells under the surface topography model = background_value * np.ones(ind_active.sum()) # Add a sphere ind_sphere = model_builder.get_indices_sphere( np.r_[-25.0, 0.0, -15.0], 20.0, mesh.gridCC ) ind_sphere = ind_sphere[ind_active] # So same size and order as model model[ind_sphere] = sphere_value # Add dyke defined by a set of points xp = np.kron(np.ones((2)), [-10.0, 10.0, 55.0, 35.0]) yp = np.kron([-1000.0, 1000.0], np.ones((4))) zp = np.kron(np.ones((2)), [-120.0, -120.0, 45.0, 45.0]) xyz_pts = np.c_[mkvc(xp), mkvc(yp), mkvc(zp)] ind_polygon = model_builder.get_indices_polygon(mesh, xyz_pts) ind_polygon = ind_polygon[ind_active] # So same size and order as model model[ind_polygon] = dyke_value # Plot fig = plt.figure(figsize=(5, 5)) ax = fig.add_subplot(111) ind_slice = int(mesh.h[1].size / 2) mesh.plot_slice(model_map * model, normal="Y", ax=ax, ind=ind_slice, grid=True) ax.set_title( "Model slice at y = {} m".format(mesh.x0[1] + np.sum(mesh.h[1][0:ind_slice])) ) plt.show() .. image-sg:: /content/tutorials/01-models_mapping/images/sphx_glr_plot_3_tree_models_003.png :alt: Model slice at y = 0.0 m :srcset: /content/tutorials/01-models_mapping/images/sphx_glr_plot_3_tree_models_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 251-259 Parameterized block model ------------------------- Instead of defining a model value for each sub-surface cell, we can define the model in terms of a small number of parameters. Here we parameterize the model as a block in a half-space. We then create a mapping which projects this model onto the mesh. .. GENERATED FROM PYTHON SOURCE LINES 259-300 .. code-block:: Python mesh = make_example_mesh() mesh = refine_topography(mesh) mesh = refine_box(mesh) mesh.finalize() background_value = 100.0 # background value block_value = 40.0 # block value xc, yc, zc = -20.0, 0.0, -20.0 # center of block dx, dy, dz = 25.0, 40.0, 30.0 # dimensions in x,y,z # Define surface topography [xx, yy] = np.meshgrid(mesh.nodes_x, mesh.nodes_y) zz = -3 * np.exp((xx**2 + yy**2) / 60**2) + 45.0 topo = np.c_[mkvc(xx), mkvc(yy), mkvc(zz)] # Set active cells and define unit values air_value = 0.0 ind_active = active_from_xyz(mesh, topo) active_map = maps.InjectActiveCells(mesh, ind_active, air_value) # Define the model on subsurface cells model = np.r_[background_value, block_value, xc, dx, yc, dy, zc, dz] parametric_map = maps.ParametricBlock( mesh, active_cells=ind_active, epsilon=1e-10, p=5.0 ) # Define a single mapping from model to mesh model_map = active_map * parametric_map # Plot fig = plt.figure(figsize=(5, 5)) ax = fig.add_subplot(111) ind_slice = int(mesh.h[1].size / 2) mesh.plot_slice(model_map * model, normal="Y", ax=ax, ind=ind_slice, grid=True) ax.set_title( "Model slice at y = {} m".format(mesh.x0[1] + np.sum(mesh.h[1][0:ind_slice])) ) plt.show() .. image-sg:: /content/tutorials/01-models_mapping/images/sphx_glr_plot_3_tree_models_004.png :alt: Model slice at y = 0.0 m :srcset: /content/tutorials/01-models_mapping/images/sphx_glr_plot_3_tree_models_004.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 301-314 Using Wire Maps --------------- Wire maps are needed when the model is comprised of two or more parameter types (e.g. conductivity and magnetic permeability). Because the model vector contains all values for all parameter types, we need to use "wires" to extract the values for a particular parameter type. Here we will define a model consisting of log-conductivity values and magnetic permeability values. We wish to plot the conductivity and permeability on the mesh. Wires are used to keep track of the mapping between the model vector and a particular physical property type. .. GENERATED FROM PYTHON SOURCE LINES 314-373 .. code-block:: Python mesh = make_example_mesh() mesh = refine_topography(mesh) mesh = refine_box(mesh) mesh.finalize() background_sigma_value = np.log(100.0) sphere_sigma_value = np.log(70.0) dyke_sigma_value = np.log(40.0) background_mu_value = 1.0 sphere_mu_value = 1.25 # Define surface topography [xx, yy] = np.meshgrid(mesh.nodes_x, mesh.nodes_y) zz = -3 * np.exp((xx**2 + yy**2) / 60**2) + 45.0 topo = np.c_[mkvc(xx), mkvc(yy), mkvc(zz)] # Set active cells air_value = 0.0 ind_active = active_from_xyz(mesh, topo) active_map = maps.InjectActiveCells(mesh, ind_active, air_value) # Define model for cells under the surface topography N = int(ind_active.sum()) model = np.kron(np.ones((N, 1)), np.c_[background_sigma_value, background_mu_value]) # Add a conductive and permeable sphere ind_sphere = model_builder.get_indices_sphere( np.r_[-20.0, 0.0, -15.0], 20.0, mesh.gridCC ) ind_sphere = ind_sphere[ind_active] # So same size and order as model model[ind_sphere, :] = np.c_[sphere_sigma_value, sphere_mu_value] # Add a conductive and non-permeable dyke xp = np.kron(np.ones((2)), [-10.0, 10.0, 55.0, 35.0]) yp = np.kron([-1000.0, 1000.0], np.ones((4))) zp = np.kron(np.ones((2)), [-120.0, -120.0, 45.0, 45.0]) xyz_pts = np.c_[mkvc(xp), mkvc(yp), mkvc(zp)] ind_polygon = model_builder.get_indices_polygon(mesh, xyz_pts) ind_polygon = ind_polygon[ind_active] # So same size and order as model model[ind_polygon, 0] = dyke_sigma_value # Create model vector and wires model = mkvc(model) wire_map = maps.Wires(("log_sigma", N), ("mu", N)) # Use combo maps to map from model to mesh sigma_map = active_map * maps.ExpMap() * wire_map.log_sigma mu_map = active_map * wire_map.mu # Plot fig = plt.figure(figsize=(5, 5)) ax = fig.add_subplot(111) ind_slice = int(mesh.h[1].size / 2) mesh.plot_slice(sigma_map * model, normal="Y", ax=ax, ind=ind_slice, grid=True) ax.set_title( "Model slice at y = {} m".format(mesh.x0[1] + np.sum(mesh.h[1][0:ind_slice])) ) plt.show() .. image-sg:: /content/tutorials/01-models_mapping/images/sphx_glr_plot_3_tree_models_005.png :alt: Model slice at y = 0.0 m :srcset: /content/tutorials/01-models_mapping/images/sphx_glr_plot_3_tree_models_005.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 6.178 seconds) **Estimated memory usage:** 288 MB .. _sphx_glr_download_content_tutorials_01-models_mapping_plot_3_tree_models.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_3_tree_models.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_3_tree_models.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_3_tree_models.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_