.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "content/examples/03-magnetics/plot_inv_mag_MVI_VectorAmplitude.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_03-magnetics_plot_inv_mag_MVI_VectorAmplitude.py: Magnetic inversion on a TreeMesh ================================ In this example, we demonstrate the use of a Magnetic Vector Inversion on 3D TreeMesh for the inversion of magnetic data. The inverse problem uses the :class:`simpeg.regularization.VectorAmplitude` regularization. .. GENERATED FROM PYTHON SOURCE LINES 12-35 .. code-block:: Python from simpeg import ( data, data_misfit, directives, maps, inverse_problem, optimization, inversion, regularization, ) from simpeg import utils from simpeg.utils import mkvc, sdiag from discretize.utils import mesh_builder_xyz, refine_tree_xyz, active_from_xyz from simpeg.potential_fields import magnetics import numpy as np import matplotlib.pyplot as plt # sphinx_gallery_thumbnail_number = 3 .. GENERATED FROM PYTHON SOURCE LINES 36-45 Setup ----- Define the survey and model parameters First we need to define the direction of the inducing field As a simple case, we pick a vertical inducing field of magnitude 50,000 nT. .. GENERATED FROM PYTHON SOURCE LINES 45-74 .. code-block:: Python np.random.seed(1) # We will assume a vertical inducing field h0_amplitude, h0_inclination, h0_declination = (50000.0, 90.0, 0.0) # Create grid of points for topography # Lets create a simple Gaussian topo and set the active cells [xx, yy] = np.meshgrid(np.linspace(-200, 200, 50), np.linspace(-200, 200, 50)) b = 100 A = 50 zz = A * np.exp(-0.5 * ((xx / b) ** 2.0 + (yy / b) ** 2.0)) topo = np.c_[utils.mkvc(xx), utils.mkvc(yy), utils.mkvc(zz)] # Create an array of observation points xr = np.linspace(-100.0, 100.0, 20) yr = np.linspace(-100.0, 100.0, 20) X, Y = np.meshgrid(xr, yr) Z = A * np.exp(-0.5 * ((X / b) ** 2.0 + (Y / b) ** 2.0)) + 5 # Create a MAGsurvey xyzLoc = np.c_[mkvc(X.T), mkvc(Y.T), mkvc(Z.T)] rxLoc = magnetics.receivers.Point(xyzLoc) srcField = magnetics.sources.UniformBackgroundField( receiver_list=[rxLoc], amplitude=h0_amplitude, inclination=h0_inclination, declination=h0_declination, ) survey = magnetics.survey.Survey(srcField) .. GENERATED FROM PYTHON SOURCE LINES 75-80 Inversion Mesh -------------- Here, we create a TreeMesh with base cell size of 5 m. .. GENERATED FROM PYTHON SOURCE LINES 80-97 .. code-block:: Python # Create a mesh h = [5, 5, 5] padDist = np.ones((3, 2)) * 100 mesh = mesh_builder_xyz( xyzLoc, h, padding_distance=padDist, depth_core=100, mesh_type="tree" ) mesh = refine_tree_xyz( mesh, topo, method="surface", octree_levels=[2, 6], finalize=True ) # Define an active cells from topo actv = active_from_xyz(mesh, topo) nC = int(actv.sum()) .. rst-class:: sphx-glr-script-out .. code-block:: none /home/vsts/work/1/s/examples/03-magnetics/plot_inv_mag_MVI_VectorAmplitude.py:88: 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. .. GENERATED FROM PYTHON SOURCE LINES 98-103 Forward modeling data --------------------- We can now create a magnetization model and generate data. .. GENERATED FROM PYTHON SOURCE LINES 103-142 .. code-block:: Python model_azm_dip = np.zeros((mesh.nC, 2)) model_amp = np.ones(mesh.nC) * 1e-8 ind = utils.model_builder.get_indices_block( np.r_[-30, -20, -10], np.r_[30, 20, 25], mesh.gridCC, )[0] model_amp[ind] = 0.05 model_azm_dip[ind, 0] = 45.0 model_azm_dip[ind, 1] = 90.0 # Remove air cells model_azm_dip = model_azm_dip[actv, :] model_amp = model_amp[actv] model = sdiag(model_amp) * utils.mat_utils.dip_azimuth2cartesian( model_azm_dip[:, 0], model_azm_dip[:, 1] ) # Create reduced identity map idenMap = maps.IdentityMap(nP=nC * 3) # Create the simulation simulation = magnetics.simulation.Simulation3DIntegral( survey=survey, mesh=mesh, chiMap=idenMap, active_cells=actv, model_type="vector" ) # Compute some data and add some random noise d = simulation.dpred(mkvc(model)) std = 10 # nT synthetic_data = d + np.random.randn(len(d)) * std wd = np.ones(len(d)) * std # Assign data and uncertainties to the survey data_object = data.Data(survey, dobs=synthetic_data, standard_deviation=wd) # Create a projection matrix for plotting later actv_plot = maps.InjectActiveCells(mesh, actv, np.nan) .. GENERATED FROM PYTHON SOURCE LINES 143-148 Inversion --------- We can now attempt the inverse calculations. .. GENERATED FROM PYTHON SOURCE LINES 148-202 .. code-block:: Python # Create sensitivity weights from our linear forward operator rxLoc = survey.source_field.receiver_list[0].locations # This Mapping connects the regularizations for the three-component # vector model wires = maps.Wires(("p", nC), ("s", nC), ("t", nC)) m0 = np.ones(3 * nC) * 1e-4 # Starting model # Create the regularization on the amplitude of magnetization reg = regularization.VectorAmplitude( mesh, mapping=idenMap, active_cells=actv, reference_model_in_smooth=True, norms=[0.0, 2.0, 2.0, 2.0], gradient_type="total", ) # Data misfit function dmis = data_misfit.L2DataMisfit(simulation=simulation, data=data_object) dmis.W = 1.0 / data_object.standard_deviation # The optimization scheme opt = optimization.ProjectedGNCG( maxIter=20, lower=-10, upper=10.0, maxIterLS=20, maxIterCG=20, tolCG=1e-4 ) # The inverse problem invProb = inverse_problem.BaseInvProblem(dmis, reg, opt) # Estimate the initial beta factor betaest = directives.BetaEstimate_ByEig(beta0_ratio=1e1) # Add sensitivity weights sensitivity_weights = directives.UpdateSensitivityWeights() # Here is where the norms are applied IRLS = directives.UpdateIRLS( f_min_change=1e-3, max_irls_iterations=10, misfit_tolerance=5e-1 ) # Pre-conditioner update_Jacobi = directives.UpdatePreconditioner() inv = inversion.BaseInversion( invProb, directiveList=[sensitivity_weights, IRLS, update_Jacobi, betaest] ) # Run the inversion mrec = inv.run(m0) .. rst-class:: sphx-glr-script-out .. code-block:: none Running inversion with SimPEG v0.23.0 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 will set Regularization.reference_model to m0. simpeg.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv. ***Done using the default solver Pardiso and no solver_opts.*** model has any nan: 0 =============================== Projected GNCG =============================== # beta phi_d phi_m f |proj(x-g)-x| LS Comment ----------------------------------------------------------------------------- x0 has any nan: 0 0 1.31e+05 1.19e+04 0.00e+00 1.19e+04 2.43e+03 0 1 6.53e+04 6.93e+03 1.56e-02 7.95e+03 2.02e+03 0 2 3.27e+04 4.70e+03 3.94e-02 5.98e+03 1.86e+03 0 Skip BFGS 3 1.63e+04 2.73e+03 8.22e-02 4.08e+03 1.74e+03 0 Skip BFGS 4 8.17e+03 1.35e+03 1.42e-01 2.51e+03 1.61e+03 0 Skip BFGS 5 4.08e+03 6.01e+02 2.05e-01 1.44e+03 1.48e+03 0 Skip BFGS Reached starting chifact with l2-norm regularization: Start IRLS steps... irls_threshold 0.008924951681186372 6 4.08e+03 2.59e+02 4.66e-01 2.16e+03 1.56e+03 0 Skip BFGS 7 4.08e+03 4.79e+02 5.52e-01 2.73e+03 1.55e+03 0 8 2.14e+03 7.28e+02 6.79e-01 2.18e+03 1.04e+03 0 9 1.12e+03 5.02e+02 1.14e+00 1.77e+03 1.15e+03 0 10 5.86e+02 3.72e+02 1.54e+00 1.28e+03 1.14e+03 0 11 3.07e+02 3.05e+02 1.65e+00 8.11e+02 8.89e+02 0 Skip BFGS 12 1.61e+02 2.53e+02 1.76e+00 5.36e+02 1.21e+03 0 Skip BFGS 13 2.60e+02 1.94e+02 2.08e+00 7.36e+02 1.50e+03 0 Skip BFGS 14 4.21e+02 2.89e+02 1.53e+00 9.32e+02 1.55e+03 0 15 6.81e+02 3.81e+02 1.20e+00 1.20e+03 1.64e+03 0 Reach maximum number of IRLS cycles: 10 ------------------------- STOP! ------------------------- 1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 1.1929e+03 1 : |xc-x_last| = 2.3551e-02 <= tolX*(1+|x0|) = 1.0290e-01 0 : |proj(x-g)-x| = 1.6355e+03 <= tolG = 1.0000e-01 0 : |proj(x-g)-x| = 1.6355e+03 <= 1e3*eps = 1.0000e-02 0 : maxIter = 20 <= iter = 16 ------------------------- DONE! ------------------------- .. GENERATED FROM PYTHON SOURCE LINES 203-210 Final Plot ---------- Let's compare the smooth and compact model .. GENERATED FROM PYTHON SOURCE LINES 210-256 .. code-block:: Python plt.figure(figsize=(12, 6)) ax = plt.subplot(2, 2, 1) im = utils.plot_utils.plot2Ddata(xyzLoc, synthetic_data, ax=ax) plt.colorbar(im[0]) ax.set_title("Predicted data.") plt.gca().set_aspect("equal", adjustable="box") for ii, (title, mvec) in enumerate( [("True model", model), ("Smooth model", invProb.l2model), ("Sparse model", mrec)] ): ax = plt.subplot(2, 2, ii + 2) mesh.plot_slice( actv_plot * mvec.reshape((-1, 3), order="F"), v_type="CCv", view="vec", ax=ax, normal="Y", grid=True, quiver_opts={ "pivot": "mid", "scale": 8 * np.abs(mvec).max(), "scale_units": "inches", }, ) ax.set_xlim([-200, 200]) ax.set_ylim([-100, 75]) ax.set_title(title) ax.set_xlabel("x") ax.set_ylabel("z") plt.gca().set_aspect("equal", adjustable="box") plt.show() print("END") # Plot the final predicted data and the residual # plt.figure() # ax = plt.subplot(1, 2, 1) # utils.plot_utils.plot2Ddata(xyzLoc, invProb.dpred, ax=ax) # ax.set_title("Predicted data.") # plt.gca().set_aspect("equal", adjustable="box") # # ax = plt.subplot(1, 2, 2) # utils.plot_utils.plot2Ddata(xyzLoc, synthetic_data - invProb.dpred, ax=ax) # ax.set_title("Data residual.") # plt.gca().set_aspect("equal", adjustable="box") .. image-sg:: /content/examples/03-magnetics/images/sphx_glr_plot_inv_mag_MVI_VectorAmplitude_001.png :alt: Predicted data., True model, Smooth model, Sparse model :srcset: /content/examples/03-magnetics/images/sphx_glr_plot_inv_mag_MVI_VectorAmplitude_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none END .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 35.798 seconds) **Estimated memory usage:** 416 MB .. _sphx_glr_download_content_examples_03-magnetics_plot_inv_mag_MVI_VectorAmplitude.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_inv_mag_MVI_VectorAmplitude.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_inv_mag_MVI_VectorAmplitude.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_inv_mag_MVI_VectorAmplitude.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_