.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "content/user-guide/examples/10-pgi/plot_inv_0_PGI_Linear_1D.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_user-guide_examples_10-pgi_plot_inv_0_PGI_Linear_1D.py: Petrophysically guided inversion (PGI): Linear example ====================================================== We do a comparison between the classic least-squares inversion and our formulation of a petrophysically constrained inversion. We explore it through the UBC linear example. .. GENERATED FROM PYTHON SOURCE LINES 12-14 Tikhonov Inversion# #################### .. GENERATED FROM PYTHON SOURCE LINES 14-90 .. code-block:: Python import discretize as Mesh import matplotlib.pyplot as plt import numpy as np from simpeg import ( data_misfit, directives, inverse_problem, inversion, maps, optimization, regularization, simulation, utils, ) # Random seed for reproductibility np.random.seed(1) # Mesh N = 100 mesh = Mesh.TensorMesh([N]) # Survey design parameters nk = 20 jk = np.linspace(1.0, 60.0, nk) p = -0.25 q = 0.25 # Physics def g(k): return np.exp(p * jk[k] * mesh.cell_centers_x) * np.cos( np.pi * q * jk[k] * mesh.cell_centers_x ) G = np.empty((nk, mesh.nC)) for i in range(nk): G[i, :] = g(i) # True model mtrue = np.zeros(mesh.nC) mtrue[mesh.cell_centers_x > 0.2] = 1.0 mtrue[mesh.cell_centers_x > 0.35] = 0.0 t = (mesh.cell_centers_x - 0.65) / 0.25 indx = np.abs(t) < 1 mtrue[indx] = -(((1 - t**2.0) ** 2.0)[indx]) mtrue = np.zeros(mesh.nC) mtrue[mesh.cell_centers_x > 0.3] = 1.0 mtrue[mesh.cell_centers_x > 0.45] = -0.5 mtrue[mesh.cell_centers_x > 0.6] = 0 # simpeg problem and survey prob = simulation.LinearSimulation(mesh, G=G, model_map=maps.IdentityMap()) std = 0.01 survey = prob.make_synthetic_data(mtrue, relative_error=std, add_noise=True) # Setup the inverse problem reg = regularization.WeightedLeastSquares(mesh, alpha_s=1.0, alpha_x=1.0) dmis = data_misfit.L2DataMisfit(data=survey, simulation=prob) opt = optimization.ProjectedGNCG(maxIter=10, cg_maxiter=50, cg_rtol=1e-3) invProb = inverse_problem.BaseInvProblem(dmis, reg, opt) directiveslist = [ directives.BetaEstimate_ByEig(beta0_ratio=1e-5), directives.BetaSchedule(coolingFactor=10.0, coolingRate=2), directives.TargetMisfit(), ] inv = inversion.BaseInversion(invProb, directiveList=directiveslist) m0 = np.zeros_like(mtrue) mnormal = inv.run(m0) .. rst-class:: sphx-glr-script-out .. code-block:: none Running inversion with SimPEG v0.25.1 ================================================= Projected GNCG ================================================= # beta phi_d phi_m f |proj(x-g)-x| LS iter_CG CG |Ax-b|/|b| CG |Ax-b| Comment ----------------------------------------------------------------------------------------------------------------- 0 1.83e+01 2.00e+05 0.00e+00 2.00e+05 0 inf inf 1 1.83e+01 3.31e+02 4.27e+01 1.11e+03 2.60e+06 0 13 6.68e-04 1.73e+03 2 1.83e+01 3.27e+01 4.62e+01 8.76e+02 1.73e+03 0 22 7.49e-04 1.30e+00 Skip BFGS 3 1.83e+00 1.86e+00 4.97e+01 9.25e+01 5.08e+02 0 32 3.23e-04 1.64e-01 Skip BFGS ------------------------- STOP! ------------------------- 1 : |fc-fOld| = 2.4468e+01 <= tolF*(1+|f0|) = 2.0000e+04 0 : |xc-x_last| = 2.2562e-01 <= tolX*(1+|x0|) = 1.0000e-01 0 : |proj(x-g)-x| = 5.0806e+02 <= tolG = 1.0000e-01 0 : |proj(x-g)-x| = 5.0806e+02 <= 1e3*eps = 1.0000e-02 0 : maxIter = 10 <= iter = 3 ------------------------- DONE! ------------------------- .. GENERATED FROM PYTHON SOURCE LINES 91-93 Petrophysically constrained inversion # ######################################## .. GENERATED FROM PYTHON SOURCE LINES 93-168 .. code-block:: Python # fit a Gaussian Mixture Model with n components # on the true model to simulate the laboratory # petrophysical measurements n = 3 clf = utils.WeightedGaussianMixture( mesh=mesh, n_components=n, covariance_type="full", max_iter=100, n_init=3, reg_covar=5e-4, ) clf.fit(mtrue.reshape(-1, 1)) # Petrophyically constrained regularization reg = regularization.PGI( gmmref=clf, mesh=mesh, alpha_pgi=1.0, alpha_x=1.0, ) # Optimization opt = optimization.ProjectedGNCG(maxIter=20, cg_maxiter=50, cg_rtol=1e-3) opt.remember("xc") # Setup new inverse problem invProb = inverse_problem.BaseInvProblem(dmis, reg, opt) # directives Alphas = directives.AlphasSmoothEstimate_ByEig(alpha0_ratio=10.0, verbose=True) beta = directives.BetaEstimate_ByEig(beta0_ratio=1e-8) betaIt = directives.PGI_BetaAlphaSchedule( verbose=True, coolingFactor=2.0, warmingFactor=1.0, tolerance=0.1, update_rate=1, progress=0.2, ) targets = directives.MultiTargetMisfits(verbose=True) petrodir = directives.PGI_UpdateParameters() addmref = directives.PGI_AddMrefInSmooth(verbose=True) # Setup Inversion inv = inversion.BaseInversion( invProb, directiveList=[Alphas, beta, petrodir, targets, addmref, betaIt] ) # Initial model same as for WeightedLeastSquares mcluster = inv.run(m0) # Final Plot fig, axes = plt.subplots(1, 3, figsize=(12 * 1.2, 4 * 1.2)) for i in range(prob.G.shape[0]): axes[0].plot(prob.G[i, :]) axes[0].set_title("Columns of matrix G") axes[1].hist(mtrue, bins=20, linewidth=3.0, density=True, color="k") axes[1].set_xlabel("Model value") axes[1].set_xlabel("Occurence") axes[1].hist(mnormal, bins=20, density=True, color="b") axes[1].hist(mcluster, bins=20, density=True, color="r") axes[1].legend(["Mtrue Hist.", "L2 Model Hist.", "PGI Model Hist."]) axes[2].plot(mesh.cell_centers_x, mtrue, color="black", linewidth=3) axes[2].plot(mesh.cell_centers_x, mnormal, color="blue") axes[2].plot(mesh.cell_centers_x, mcluster, "r-") axes[2].plot(mesh.cell_centers_x, invProb.reg.objfcts[0].reference_model, "r--") axes[2].legend(("True Model", "L2 Model", "PGI Model", "Learned Mref")) axes[2].set_ylim([-2, 2]) plt.show() .. image-sg:: /content/user-guide/examples/10-pgi/images/sphx_glr_plot_inv_0_PGI_Linear_1D_001.png :alt: Columns of matrix G :srcset: /content/user-guide/examples/10-pgi/images/sphx_glr_plot_inv_0_PGI_Linear_1D_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Running inversion with SimPEG v0.25.1 Alpha scales: [np.float64(0.529383217372966), np.float64(0.0)] ================================================= Projected GNCG ================================================= # beta phi_d phi_m f |proj(x-g)-x| LS iter_CG CG |Ax-b|/|b| CG |Ax-b| Comment ----------------------------------------------------------------------------------------------------------------- 0 3.19e-02 2.00e+05 0.00e+00 2.00e+05 0 inf inf 1 3.19e-02 3.57e+02 3.44e+02 3.68e+02 2.60e+06 0 13 9.96e-04 2.59e+03 geophys. misfits: 357.0 (target 20.0 [False]) | smallness misfit: 3122.3 (target: 100.0 [False]) mref changed in 25 places Beta cooling evaluation: progress: [357.]; minimum progress targets: [160000.] 2 3.19e-02 7.87e-01 6.28e+01 2.79e+00 2.59e+03 0 29 6.30e-04 1.63e+00 Skip BFGS geophys. misfits: 0.8 (target 20.0 [True]) | smallness misfit: 3263.2 (target: 100.0 [False]) mref changed in 2 places Beta cooling evaluation: progress: [0.8]; minimum progress targets: [285.6] Warming alpha_pgi to favor clustering: 25.42709789430108 3 3.19e-02 1.66e+00 1.87e+02 7.61e+00 4.11e+01 0 50 3.21e+00 1.32e+02 geophys. misfits: 1.7 (target 20.0 [True]) | smallness misfit: 352.8 (target: 100.0 [False]) mref changed in 0 places Add mref to Smoothness. Changes in mref happened in 0.0 % of the cells Beta cooling evaluation: progress: [1.7]; minimum progress targets: [22.] Warming alpha_pgi to favor clustering: 306.25574914432264 4 3.19e-02 4.75e+00 6.57e+02 2.57e+01 2.01e+02 0 50 1.73e-01 3.46e+01 geophys. misfits: 4.7 (target 20.0 [True]) | smallness misfit: 172.1 (target: 100.0 [False]) mref changed in 0 places Add mref to Smoothness. Changes in mref happened in 0.0 % of the cells Beta cooling evaluation: progress: [4.7]; minimum progress targets: [22.] Warming alpha_pgi to favor clustering: 1289.6256152665558 5 3.19e-02 1.34e+01 1.84e+03 7.22e+01 3.64e+02 0 50 7.46e-02 2.72e+01 geophys. misfits: 13.4 (target 20.0 [True]) | smallness misfit: 132.6 (target: 100.0 [False]) mref changed in 0 places Add mref to Smoothness. Changes in mref happened in 0.0 % of the cells Beta cooling evaluation: progress: [13.4]; minimum progress targets: [22.] Warming alpha_pgi to favor clustering: 1925.9739036322549 6 3.19e-02 2.00e+01 2.43e+03 9.77e+01 2.04e+02 0 50 3.84e-02 7.84e+00 Skip BFGS geophys. misfits: 20.0 (target 20.0 [False]) | smallness misfit: 119.4 (target: 100.0 [False]) mref changed in 0 places Beta cooling evaluation: progress: [20.]; minimum progress targets: [22.] 7 3.19e-02 2.00e+01 2.43e+03 9.77e+01 7.84e+00 0 50 4.89e-02 3.83e-01 geophys. misfits: 20.0 (target 20.0 [False]) | smallness misfit: 119.5 (target: 100.0 [False]) mref changed in 0 places Beta cooling evaluation: progress: [20.]; minimum progress targets: [22.] 8 3.19e-02 2.00e+01 2.43e+03 9.77e+01 3.83e-01 0 50 2.58e-01 9.89e-02 geophys. misfits: 20.0 (target 20.0 [False]) | smallness misfit: 119.4 (target: 100.0 [False]) mref changed in 0 places Beta cooling evaluation: progress: [20.]; minimum progress targets: [22.] ------------------------- STOP! ------------------------- 1 : |fc-fOld| = 1.5078e-05 <= tolF*(1+|f0|) = 2.0000e+04 1 : |xc-x_last| = 1.0205e-04 <= tolX*(1+|x0|) = 1.0000e-01 1 : |proj(x-g)-x| = 9.8912e-02 <= tolG = 1.0000e-01 0 : |proj(x-g)-x| = 9.8912e-02 <= 1e3*eps = 1.0000e-02 0 : maxIter = 20 <= iter = 8 ------------------------- DONE! ------------------------- .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 7.296 seconds) **Estimated memory usage:** 322 MB .. _sphx_glr_download_content_user-guide_examples_10-pgi_plot_inv_0_PGI_Linear_1D.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_0_PGI_Linear_1D.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_inv_0_PGI_Linear_1D.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_inv_0_PGI_Linear_1D.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_