.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "content/examples/01-maps/plot_sumMap.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_01-maps_plot_sumMap.py: Maps: ComboMaps =============== Invert synthetic magnetic data with variable background values and a single block anomaly buried at depth. We will use the Sum Map to invert for both the background values and an heterogeneous susceptibiilty model. .. code-block:: python :linenos: .. GENERATED FROM PYTHON SOURCE LINES 15-207 .. rst-class:: sphx-glr-horizontal * .. image-sg:: /content/examples/01-maps/images/sphx_glr_plot_sumMap_001.png :alt: plot sumMap :srcset: /content/examples/01-maps/images/sphx_glr_plot_sumMap_001.png :class: sphx-glr-multi-img * .. image-sg:: /content/examples/01-maps/images/sphx_glr_plot_sumMap_002.png :alt: plot sumMap :srcset: /content/examples/01-maps/images/sphx_glr_plot_sumMap_002.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none Running inversion with SimPEG v0.22.1 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 5.94e+05 9.12e+06 4.27e-04 9.12e+06 6.29e+01 0 1 2.97e+05 1.32e+05 7.36e-02 1.54e+05 3.48e+01 0 2 1.49e+05 4.83e+04 2.25e-01 8.18e+04 4.96e+01 0 3 7.43e+04 2.08e+04 3.55e-01 4.71e+04 5.48e+01 0 Skip BFGS 4 3.71e+04 6.79e+03 4.70e-01 2.42e+04 4.84e+01 0 Skip BFGS 5 1.86e+04 4.81e+03 5.05e-01 1.42e+04 3.52e+01 1 Skip BFGS 6 9.28e+03 9.79e+02 5.99e-01 6.54e+03 4.79e+01 0 7 4.64e+03 9.56e+02 6.01e-01 3.75e+03 5.60e+01 3 Skip BFGS 8 2.32e+03 4.25e+02 6.47e-01 1.93e+03 4.63e+01 0 9 1.16e+03 4.25e+02 6.47e-01 1.18e+03 4.21e+01 8 Skip BFGS Reached starting chifact with l2-norm regularization: Start IRLS steps... irls_threshold 0.010204147636778873 irls_threshold 0.012229643438880118 10 5.80e+02 3.62e+02 9.20e-01 8.96e+02 3.55e+01 0 11 5.80e+02 3.61e+02 9.89e-01 9.35e+02 6.16e+01 1 12 5.80e+02 3.61e+02 1.04e+00 9.62e+02 6.16e+01 15 Skip BFGS 13 5.80e+02 3.63e+02 1.06e+00 9.79e+02 4.45e+01 4 14 5.80e+02 3.70e+02 1.06e+00 9.86e+02 3.71e+01 1 Skip BFGS 15 9.04e+02 3.58e+02 1.06e+00 1.31e+03 5.75e+01 0 16 1.41e+03 3.59e+02 1.03e+00 1.81e+03 5.59e+01 8 Skip BFGS 17 1.41e+03 3.89e+02 9.78e-01 1.77e+03 3.75e+01 2 18 1.41e+03 4.37e+02 8.81e-01 1.68e+03 3.61e+01 0 19 1.15e+03 4.54e+02 8.21e-01 1.40e+03 6.03e+01 2 Skip BFGS 20 1.15e+03 4.05e+02 7.92e-01 1.31e+03 3.56e+01 0 21 1.15e+03 4.12e+02 7.32e-01 1.25e+03 3.56e+01 3 Skip BFGS 22 1.15e+03 4.28e+02 6.66e-01 1.19e+03 5.59e+01 0 23 1.15e+03 4.33e+02 6.03e-01 1.13e+03 6.02e+01 3 Skip BFGS 24 9.33e+02 4.58e+02 5.23e-01 9.46e+02 3.68e+01 0 25 9.33e+02 4.36e+02 4.65e-01 8.69e+02 3.65e+01 0 26 9.33e+02 4.33e+02 4.05e-01 8.11e+02 6.10e+01 1 Skip BFGS 27 9.33e+02 4.17e+02 3.34e-01 7.29e+02 3.07e+01 0 28 9.33e+02 4.20e+02 2.86e-01 6.87e+02 3.63e+01 1 Skip BFGS 29 9.33e+02 4.11e+02 2.41e-01 6.36e+02 3.32e+01 0 Reach maximum number of IRLS cycles: 20 ------------------------- STOP! ------------------------- 1 : |fc-fOld| = 0.0000e+00 <= tolF*(1+|f0|) = 9.1165e+05 1 : |xc-x_last| = 3.0115e-03 <= tolX*(1+|x0|) = 1.0075e-01 0 : |proj(x-g)-x| = 3.3213e+01 <= tolG = 1.0000e-03 0 : |proj(x-g)-x| = 3.3213e+01 <= 1e3*eps = 1.0000e-03 0 : maxIter = 100 <= iter = 30 ------------------------- DONE! ------------------------- | .. code-block:: Python from discretize import TensorMesh from discretize.utils import active_from_xyz from simpeg import ( utils, maps, regularization, data_misfit, optimization, inverse_problem, directives, inversion, ) from simpeg.potential_fields import magnetics import numpy as np import matplotlib.pyplot as plt def run(plotIt=True): h0_amplitude, h0_inclination, h0_declination = (50000.0, 90.0, 0.0) # Create a mesh dx = 5.0 hxind = [(dx, 5, -1.3), (dx, 10), (dx, 5, 1.3)] hyind = [(dx, 5, -1.3), (dx, 10), (dx, 5, 1.3)] hzind = [(dx, 5, -1.3), (dx, 10)] mesh = TensorMesh([hxind, hyind, hzind], "CCC") # Lets create a simple Gaussian topo and set the active cells [xx, yy] = np.meshgrid(mesh.nodes_x, mesh.nodes_y) zz = -np.exp((xx**2 + yy**2) / 75**2) + mesh.nodes_z[-1] # We would usually load a topofile topo = np.c_[utils.mkvc(xx), utils.mkvc(yy), utils.mkvc(zz)] # Go from topo to array of indices of active cells actv = active_from_xyz(mesh, topo, "N") nC = int(actv.sum()) # Create and array of observation points xr = np.linspace(-20.0, 20.0, 20) yr = np.linspace(-20.0, 20.0, 20) X, Y = np.meshgrid(xr, yr) # Move the observation points 5m above the topo Z = -np.exp((X**2 + Y**2) / 75**2) + mesh.nodes_z[-1] + 5.0 # Create a MAGsurvey rxLoc = np.c_[utils.mkvc(X.T), utils.mkvc(Y.T), utils.mkvc(Z.T)] rxLoc = magnetics.Point(rxLoc) srcField = magnetics.UniformBackgroundField( receiver_list=[rxLoc], amplitude=h0_amplitude, inclination=h0_inclination, declination=h0_declination, ) survey = magnetics.Survey(srcField) # We can now create a susceptibility model and generate data model = np.zeros(mesh.nC) # Change values in half the domain model[mesh.gridCC[:, 0] < 0] = 0.01 # Add a block in half-space model = utils.model_builder.add_block( mesh.gridCC, model, np.r_[-10, -10, 20], np.r_[10, 10, 40], 0.05 ) model = utils.mkvc(model) model = model[actv] # Create active map to go from reduce set to full actvMap = maps.InjectActiveCells(mesh, actv, np.nan) # Create reduced identity map idenMap = maps.IdentityMap(nP=nC) # Create the forward model operator prob = magnetics.Simulation3DIntegral( mesh, survey=survey, chiMap=idenMap, ind_active=actv, store_sensitivities="forward_only", ) # Compute linear forward operator and compute some data data = prob.make_synthetic_data( model, relative_error=0.0, noise_floor=1, add_noise=True ) # Create a homogenous maps for the two domains domains = [mesh.gridCC[actv, 0] < 0, mesh.gridCC[actv, 0] >= 0] homogMap = maps.SurjectUnits(domains) # Create a wire map for a second model space, voxel based wires = maps.Wires(("homo", len(domains)), ("hetero", nC)) # Create Sum map sumMap = maps.SumMap([homogMap * wires.homo, wires.hetero]) # Create the forward model operator prob = magnetics.Simulation3DIntegral( mesh, survey=survey, chiMap=sumMap, ind_active=actv, store_sensitivities="ram" ) # Make sensitivity weighting # Take the cell number out of the scaling. # Want to keep high sens for large volumes wr = ( prob.getJtJdiag(np.ones(sumMap.shape[1])) / np.r_[homogMap.P.T * mesh.cell_volumes[actv], mesh.cell_volumes[actv]] ** 2.0 ) # Scale the model spaces independently wr[wires.homo.index] /= np.max((wires.homo * wr)) * utils.mkvc( homogMap.P.sum(axis=0).flatten() ) wr[wires.hetero.index] /= np.max(wires.hetero * wr) wr = wr**0.5 ## Create a regularization # For the homogeneous model regMesh = TensorMesh([len(domains)]) reg_m1 = regularization.Sparse(regMesh, mapping=wires.homo) reg_m1.set_weights(weights=wires.homo * wr) reg_m1.norms = [0, 2] reg_m1.reference_model = np.zeros(sumMap.shape[1]) # Regularization for the voxel model reg_m2 = regularization.Sparse( mesh, active_cells=actv, mapping=wires.hetero, gradient_type="components" ) reg_m2.set_weights(weights=wires.hetero * wr) reg_m2.norms = [0, 0, 0, 0] reg_m2.reference_model = np.zeros(sumMap.shape[1]) reg = reg_m1 + reg_m2 # Data misfit function dmis = data_misfit.L2DataMisfit(simulation=prob, data=data) # Add directives to the inversion opt = optimization.ProjectedGNCG( maxIter=100, lower=0.0, upper=1.0, maxIterLS=20, maxIterCG=10, tolCG=1e-3, tolG=1e-3, eps=1e-6, ) invProb = inverse_problem.BaseInvProblem(dmis, reg, opt) betaest = directives.BetaEstimate_ByEig(beta0_ratio=1e-2) # Here is where the norms are applied # Use pick a threshold parameter empirically based on the distribution of # model parameters IRLS = directives.Update_IRLS(f_min_change=1e-3, minGNiter=1) update_Jacobi = directives.UpdatePreconditioner() inv = inversion.BaseInversion(invProb, directiveList=[IRLS, betaest, update_Jacobi]) # Run the inversion m0 = np.ones(sumMap.shape[1]) * 1e-4 # Starting model prob.model = m0 mrecSum = inv.run(m0) if plotIt: mesh.plot_3d_slicer( actvMap * model, aspect="equal", zslice=30, pcolor_opts={"cmap": "inferno_r"}, transparent="slider", ) mesh.plot_3d_slicer( actvMap * sumMap * mrecSum, aspect="equal", zslice=30, pcolor_opts={"cmap": "inferno_r"}, transparent="slider", ) if __name__ == "__main__": run() plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 18.981 seconds) **Estimated memory usage:** 45 MB .. _sphx_glr_download_content_examples_01-maps_plot_sumMap.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_sumMap.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_sumMap.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_