def test_update_of_sparse_norms(self): mesh = Mesh.TensorMesh([8, 7, 6]) m = np.random.rand(mesh.nC) v = np.random.rand(mesh.nC) cell_weights = np.random.rand(mesh.nC) reg = Regularization.Sparse(mesh, cell_weights=cell_weights) reg.norms = np.c_[2., 2., 2., 2.] self.assertTrue( np.all(reg.norms == np.kron(np.ones((reg.regmesh.Pac.shape[1], 1)), np.c_[2., 2., 2., 2.]))) self.assertTrue(np.all(reg.objfcts[0].norm == 2. * np.ones(mesh.nC))) self.assertTrue(np.all(reg.objfcts[1].norm == 2. * np.ones(mesh.nFx))) self.assertTrue(np.all(reg.objfcts[2].norm == 2. * np.ones(mesh.nFy))) self.assertTrue(np.all(reg.objfcts[3].norm == 2. * np.ones(mesh.nFz))) reg.norms = np.c_[0., 1., 1., 1.] self.assertTrue( np.all(reg.norms == np.kron(np.ones((reg.regmesh.Pac.shape[1], 1)), np.c_[0., 1., 1., 1.]))) self.assertTrue(np.all(reg.objfcts[0].norm == 0. * np.ones(mesh.nC))) self.assertTrue(np.all(reg.objfcts[1].norm == 1. * np.ones(mesh.nFx))) self.assertTrue(np.all(reg.objfcts[2].norm == 1. * np.ones(mesh.nFy))) self.assertTrue(np.all(reg.objfcts[3].norm == 1. * np.ones(mesh.nFz)))
def setUp(self): mesh = Mesh.TensorMesh([4, 4, 4]) # Magnetic inducing field parameter (A,I,D) B = [50000, 90, 0] # Create a MAGsurvey rx = PF.BaseMag.RxObs( np.vstack([[0.25, 0.25, 0.25], [-0.25, -0.25, 0.25]])) srcField = PF.BaseMag.SrcField([rx], param=(B[0], B[1], B[2])) survey = PF.BaseMag.LinearSurvey(srcField) # Create the forward model operator prob = PF.Magnetics.MagneticIntegral(mesh, chiMap=Maps.IdentityMap(mesh)) # Pair the survey and problem survey.pair(prob) # Compute forward model some data m = np.random.rand(mesh.nC) survey.makeSyntheticData(m) reg = Regularization.Sparse(mesh) reg.mref = np.zeros(mesh.nC) wr = np.sum(prob.G**2., axis=0)**0.5 reg.cell_weights = wr reg.norms = [0, 1, 1, 1] reg.eps_p, reg.eps_q = 1e-3, 1e-3 # Data misfit function dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = 1. / survey.std # Add directives to the inversion opt = Optimization.ProjectedGNCG(maxIter=2, lower=-10., upper=10., maxIterCG=2) invProb = InvProblem.BaseInvProblem(dmis, reg, opt) self.mesh = mesh self.invProb = invProb
def test_update_of_sparse_norms(self): mesh = Mesh.TensorMesh([8, 7, 6]) m = np.random.rand(mesh.nC) v = np.random.rand(mesh.nC) cell_weights = np.random.rand(mesh.nC) reg = Regularization.Sparse(mesh, cell_weights=cell_weights) self.assertTrue(np.all(reg.norms == [2., 2., 2., 2.])) self.assertTrue(reg.objfcts[0].norm == 2.) self.assertTrue(reg.objfcts[1].norm == 2.) self.assertTrue(reg.objfcts[2].norm == 2.) self.assertTrue(reg.objfcts[3].norm == 2.) reg.norms = [0., 1., 1., 1.] self.assertTrue(np.all(reg.norms == [0., 1., 1., 1.])) self.assertTrue(reg.objfcts[0].norm == 0.) self.assertTrue(reg.objfcts[1].norm == 1.) self.assertTrue(reg.objfcts[2].norm == 1.) self.assertTrue(reg.objfcts[3].norm == 1.)
def run(plotIt=True): # Define the inducing field parameter H0 = (50000, 90, 0) # Create a mesh dx = 5. 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 = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC') # Get index of the center midx = int(mesh.nCx / 2) midy = int(mesh.nCy / 2) # Lets create a simple Gaussian topo and set the active cells [xx, yy] = np.meshgrid(mesh.vectorNx, mesh.vectorNy) zz = -np.exp((xx**2 + yy**2) / 75**2) + mesh.vectorNz[-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 = Utils.surface2ind_topo(mesh, topo, 'N') actv = np.where(actv)[0] nC = len(actv) # Create and array of observation points xr = np.linspace(-20., 20., 20) yr = np.linspace(-20., 20., 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.vectorNz[-1] + 5. # Create a MAGsurvey rxLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)] rxLoc = PF.BaseMag.RxObs(rxLoc) srcField = PF.BaseMag.SrcField([rxLoc], param=H0) survey = PF.BaseMag.LinearSurvey(srcField) # We can now create a susceptibility model and generate data # Here a simple block in half-space model = np.zeros((mesh.nCx, mesh.nCy, mesh.nCz)) model[(midx - 2):(midx + 2), (midy - 2):(midy + 2), -6:-2] = 0.02 model = Utils.mkvc(model) model = model[actv] # Create active map to go from reduce set to full actvMap = Maps.InjectActiveCells(mesh, actv, -100) # Create reduced identity map idenMap = Maps.IdentityMap(nP=nC) # Create the forward model operator prob = PF.Magnetics.MagneticIntegral(mesh, chiMap=idenMap, actInd=actv) # Pair the survey and problem survey.pair(prob) # Compute linear forward operator and compute some data d = prob.fields(model) # Add noise and uncertainties # We add some random Gaussian noise (1nT) data = d + np.random.randn(len(d)) wd = np.ones(len(data)) * 1. # Assign flat uncertainties survey.dobs = data survey.std = wd survey.mtrue = model # Create sensitivity weights from our linear forward operator rxLoc = survey.srcField.rxList[0].locs wr = np.sum(prob.G**2., axis=0)**0.5 wr = (wr / np.max(wr)) # Create a regularization reg = Regularization.Sparse(mesh, indActive=actv, mapping=idenMap) reg.cell_weights = wr reg.mref = np.zeros(nC) reg.norms = np.c_[0, 0, 0, 0] # reg.eps_p, reg.eps_q = 1e-0, 1e-0 # Data misfit function dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = 1 / wd # Add directives to the inversion opt = Optimization.ProjectedGNCG(maxIter=100, lower=0., upper=1., maxIterLS=20, maxIterCG=20, tolCG=1e-3) invProb = InvProblem.BaseInvProblem(dmis, reg, opt) betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1e-1) # 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-4, maxIRLSiter=40) saveDict = Directives.SaveOutputEveryIteration(save_txt=False) update_Jacobi = Directives.UpdatePreconditioner() inv = Inversion.BaseInversion( invProb, directiveList=[IRLS, betaest, update_Jacobi, saveDict]) # Run the inversion m0 = np.ones(nC) * 1e-4 # Starting model mrec = inv.run(m0) if plotIt: # Here is the recovered susceptibility model ypanel = midx zpanel = -5 m_l2 = actvMap * invProb.l2model m_l2[m_l2 == -100] = np.nan m_lp = actvMap * mrec m_lp[m_lp == -100] = np.nan m_true = actvMap * model m_true[m_true == -100] = np.nan # Plot the data Utils.PlotUtils.plot2Ddata(rxLoc, d) plt.figure() # Plot L2 model ax = plt.subplot(321) mesh.plotSlice(m_l2, ax=ax, normal='Z', ind=zpanel, grid=True, clim=(model.min(), model.max())) plt.plot(([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), ([mesh.vectorCCy[ypanel], mesh.vectorCCy[ypanel]]), color='w') plt.title('Plan l2-model.') plt.gca().set_aspect('equal') plt.ylabel('y') ax.xaxis.set_visible(False) plt.gca().set_aspect('equal', adjustable='box') # Vertica section ax = plt.subplot(322) mesh.plotSlice(m_l2, ax=ax, normal='Y', ind=midx, grid=True, clim=(model.min(), model.max())) plt.plot(([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), ([mesh.vectorCCz[zpanel], mesh.vectorCCz[zpanel]]), color='w') plt.title('E-W l2-model.') plt.gca().set_aspect('equal') ax.xaxis.set_visible(False) plt.ylabel('z') plt.gca().set_aspect('equal', adjustable='box') # Plot Lp model ax = plt.subplot(323) mesh.plotSlice(m_lp, ax=ax, normal='Z', ind=zpanel, grid=True, clim=(model.min(), model.max())) plt.plot(([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), ([mesh.vectorCCy[ypanel], mesh.vectorCCy[ypanel]]), color='w') plt.title('Plan lp-model.') plt.gca().set_aspect('equal') ax.xaxis.set_visible(False) plt.ylabel('y') plt.gca().set_aspect('equal', adjustable='box') # Vertical section ax = plt.subplot(324) mesh.plotSlice(m_lp, ax=ax, normal='Y', ind=midx, grid=True, clim=(model.min(), model.max())) plt.plot(([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), ([mesh.vectorCCz[zpanel], mesh.vectorCCz[zpanel]]), color='w') plt.title('E-W lp-model.') plt.gca().set_aspect('equal') ax.xaxis.set_visible(False) plt.ylabel('z') plt.gca().set_aspect('equal', adjustable='box') # Plot True model ax = plt.subplot(325) mesh.plotSlice(m_true, ax=ax, normal='Z', ind=zpanel, grid=True, clim=(model.min(), model.max())) plt.plot(([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), ([mesh.vectorCCy[ypanel], mesh.vectorCCy[ypanel]]), color='w') plt.title('Plan true model.') plt.gca().set_aspect('equal') plt.xlabel('x') plt.ylabel('y') plt.gca().set_aspect('equal', adjustable='box') # Vertical section ax = plt.subplot(326) mesh.plotSlice(m_true, ax=ax, normal='Y', ind=midx, grid=True, clim=(model.min(), model.max())) plt.plot(([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), ([mesh.vectorCCz[zpanel], mesh.vectorCCz[zpanel]]), color='w') plt.title('E-W true model.') plt.gca().set_aspect('equal') plt.xlabel('x') plt.ylabel('z') plt.gca().set_aspect('equal', adjustable='box') # Plot convergence curves fig, axs = plt.figure(), plt.subplot() axs.plot(saveDict.phi_d, 'k', lw=2) axs.plot(np.r_[IRLS.iterStart, IRLS.iterStart], np.r_[0, np.max(saveDict.phi_d)], 'k:') twin = axs.twinx() twin.plot(saveDict.phi_m, 'k--', lw=2) axs.text(IRLS.iterStart, 0, 'IRLS Steps', va='bottom', ha='center', rotation='vertical', size=12, bbox={'facecolor': 'white'}) axs.set_ylabel('$\phi_d$', size=16, rotation=0) axs.set_xlabel('Iterations', size=14) twin.set_ylabel('$\phi_m$', size=16, rotation=0)
# Load weighting file dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = 1./survey.std wr = prob.getJtJdiag(np.ones(int(nC + len(actv))), W=dmis.W) wr[wires.h**o.index] /= (np.max((wires.h**o*wr))) wr[wires.hetero.index] /= (np.max(wires.hetero*wr)) wr = wr**0.5 ## Create a regularization # For the homogeneous model regMesh = Mesh.TensorMesh([nC]) reg_m1 = Regularization.Sparse(regMesh, mapping=wires.h**o) reg_m1.cell_weights = wires.h**o*wr*2. reg_m1.mref = mref # Regularization for the voxel model reg_m2 = Regularization.Sparse(mesh, indActive=actv, mapping=wires.hetero) reg_m2.cell_weights = wires.hetero*wr reg_m2.norms = np.c_[driver.lpnorms].T reg_m2.mref = mref reg = reg_m1 + reg_m2 opt = Optimization.ProjectedGNCG(maxIter=30, lower=driver.bounds[0], upper=driver.bounds[1], maxIterLS = 20, maxIterCG= 30, tolCG = 1e-4)
# wr = np.sum((dmis.W*prob.F)**2., axis=0)**0.5 wr = np.zeros(nC) for ii in range(survey.nD): wr += (prob.F[ii, :] / survey.std[ii])**2. wr = (wr / np.max(wr)) else: wr = Mesh.TensorMesh.readModelUBC(mesh, work_dir + dsep + wgtfile) wr = wr[actv] wr = wr**2. # % Create inversion objects # Starting with regularization reg = Regularization.Sparse(mesh, indActive=actv, mapping=staticCells, gradientType='total') reg.mref = driver.mref[dynamic] reg.cell_weights = wr reg.norms = driver.lpnorms if driver.eps is not None: reg.eps_p = driver.eps[0] reg.eps_q = driver.eps[1] # Optimization function opt = Optimization.ProjectedGNCG(maxIter=100, lower=driver.bounds[0], upper=driver.bounds[1], maxIterLS=50, maxIterCG=10, tolCG=1e-3)
def run(plotIt=True, cleanAfterRun=True): # Start by downloading files from the remote repository # directory where the downloaded files are url = "https://storage.googleapis.com/simpeg/Chile_GRAV_4_Miller/Chile_GRAV_4_Miller.tar.gz" downloads = download(url, overwrite=True) basePath = downloads.split(".")[0] # unzip the tarfile tar = tarfile.open(downloads, "r") tar.extractall() tar.close() input_file = basePath + os.path.sep + 'LdM_input_file.inp' # %% User input # Plotting parameters, max and min densities in g/cc vmin = -0.6 vmax = 0.6 # weight exponent for default weighting wgtexp = 3. # %% # Read in the input file which included all parameters at once # (mesh, topo, model, survey, inv param, etc.) driver = PF.GravityDriver.GravityDriver_Inv(input_file) # %% # Now we need to create the survey and model information. # Access the mesh and survey information mesh = driver.mesh survey = driver.survey # define gravity survey locations rxLoc = survey.srcField.rxList[0].locs # define gravity data and errors d = survey.dobs wd = survey.std # Get the active cells active = driver.activeCells nC = len(active) # Number of active cells # Create active map to go from reduce set to full activeMap = Maps.InjectActiveCells(mesh, active, -100) # Create static map static = driver.staticCells dynamic = driver.dynamicCells staticCells = Maps.InjectActiveCells(None, dynamic, driver.m0[static], nC=nC) mstart = driver.m0[dynamic] # Get index of the center midx = int(mesh.nCx / 2) # %% # Now that we have a model and a survey we can build the linear system ... # Create the forward model operator prob = PF.Gravity.GravityIntegral(mesh, rhoMap=staticCells, actInd=active) prob.solverOpts['accuracyTol'] = 1e-4 # Pair the survey and problem survey.pair(prob) # Apply depth weighting wr = PF.Magnetics.get_dist_wgt(mesh, rxLoc, active, wgtexp, np.min(mesh.hx) / 4.) wr = wr**2. # %% Create inversion objects reg = Regularization.Sparse(mesh, indActive=active, mapping=staticCells, gradientType='total') reg.mref = driver.mref[dynamic] reg.cell_weights = wr * mesh.vol[active] reg.norms = np.c_[0., 1., 1., 1.] # reg.norms = driver.lpnorms # Specify how the optimization will proceed opt = Optimization.ProjectedGNCG(maxIter=20, lower=driver.bounds[0], upper=driver.bounds[1], maxIterLS=10, maxIterCG=20, tolCG=1e-3) # Define misfit function (obs-calc) dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = 1. / wd # create the default L2 inverse problem from the above objects invProb = InvProblem.BaseInvProblem(dmis, reg, opt) # Specify how the initial beta is found betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1e-2) # IRLS sets up the Lp inversion problem # Set the eps parameter parameter in Line 11 of the # input file based on the distribution of model (DEFAULT = 95th %ile) IRLS = Directives.Update_IRLS(f_min_change=1e-4, maxIRLSiter=40, beta_tol=5e-1) # Preconditioning refreshing for each IRLS iteration update_Jacobi = Directives.UpdatePreconditioner() # Create combined the L2 and Lp problem inv = Inversion.BaseInversion(invProb, directiveList=[IRLS, update_Jacobi, betaest]) # %% # Run L2 and Lp inversion mrec = inv.run(mstart) if cleanAfterRun: os.remove(downloads) shutil.rmtree(basePath) # %% if plotIt: # Plot observed data PF.Magnetics.plot_obs_2D(rxLoc, d, 'Observed Data') # %% # Write output model and data files and print misft stats. # reconstructing l2 model mesh with air cells and active dynamic cells L2out = activeMap * invProb.l2model # reconstructing lp model mesh with air cells and active dynamic cells Lpout = activeMap * mrec # %% # Plot out sections and histograms of the smooth l2 model. # The ind= parameter is the slice of the model from top down. yslice = midx + 1 L2out[L2out == -100] = np.nan # set "air" to nan plt.figure(figsize=(10, 7)) plt.suptitle('Smooth Inversion: Depth weight = ' + str(wgtexp)) ax = plt.subplot(221) dat1 = mesh.plotSlice(L2out, ax=ax, normal='Z', ind=-16, clim=(vmin, vmax), pcolorOpts={'cmap': 'bwr'}) plt.plot(np.array([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice], mesh.vectorCCy[yslice]]), c='gray', linestyle='--') plt.scatter(rxLoc[0:, 0], rxLoc[0:, 1], color='k', s=1) plt.title('Z: ' + str(mesh.vectorCCz[-16]) + ' m') plt.xlabel('Easting (m)') plt.ylabel('Northing (m)') plt.gca().set_aspect('equal', adjustable='box') cb = plt.colorbar(dat1[0], orientation="vertical", ticks=np.linspace(vmin, vmax, 4)) cb.set_label('Density (g/cc$^3$)') ax = plt.subplot(222) dat = mesh.plotSlice(L2out, ax=ax, normal='Z', ind=-27, clim=(vmin, vmax), pcolorOpts={'cmap': 'bwr'}) plt.plot(np.array([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice], mesh.vectorCCy[yslice]]), c='gray', linestyle='--') plt.scatter(rxLoc[0:, 0], rxLoc[0:, 1], color='k', s=1) plt.title('Z: ' + str(mesh.vectorCCz[-27]) + ' m') plt.xlabel('Easting (m)') plt.ylabel('Northing (m)') plt.gca().set_aspect('equal', adjustable='box') cb = plt.colorbar(dat1[0], orientation="vertical", ticks=np.linspace(vmin, vmax, 4)) cb.set_label('Density (g/cc$^3$)') ax = plt.subplot(212) mesh.plotSlice(L2out, ax=ax, normal='Y', ind=yslice, clim=(vmin, vmax), pcolorOpts={'cmap': 'bwr'}) plt.title('Cross Section') plt.xlabel('Easting(m)') plt.ylabel('Elevation') plt.gca().set_aspect('equal', adjustable='box') cb = plt.colorbar(dat1[0], orientation="vertical", ticks=np.linspace(vmin, vmax, 4), cmap='bwr') cb.set_label('Density (g/cc$^3$)') # %% # Make plots of Lp model yslice = midx + 1 Lpout[Lpout == -100] = np.nan # set "air" to nan plt.figure(figsize=(10, 7)) plt.suptitle('Compact Inversion: Depth weight = ' + str(wgtexp) + ': $\epsilon_p$ = ' + str(round(reg.eps_p, 1)) + ': $\epsilon_q$ = ' + str(round(reg.eps_q, 2))) ax = plt.subplot(221) dat = mesh.plotSlice(Lpout, ax=ax, normal='Z', ind=-16, clim=(vmin, vmax), pcolorOpts={'cmap': 'bwr'}) plt.plot(np.array([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice], mesh.vectorCCy[yslice]]), c='gray', linestyle='--') plt.scatter(rxLoc[0:, 0], rxLoc[0:, 1], color='k', s=1) plt.title('Z: ' + str(mesh.vectorCCz[-16]) + ' m') plt.xlabel('Easting (m)') plt.ylabel('Northing (m)') plt.gca().set_aspect('equal', adjustable='box') cb = plt.colorbar(dat[0], orientation="vertical", ticks=np.linspace(vmin, vmax, 4)) cb.set_label('Density (g/cc$^3$)') ax = plt.subplot(222) dat = mesh.plotSlice(Lpout, ax=ax, normal='Z', ind=-27, clim=(vmin, vmax), pcolorOpts={'cmap': 'bwr'}) plt.plot(np.array([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), np.array([mesh.vectorCCy[yslice], mesh.vectorCCy[yslice]]), c='gray', linestyle='--') plt.scatter(rxLoc[0:, 0], rxLoc[0:, 1], color='k', s=1) plt.title('Z: ' + str(mesh.vectorCCz[-27]) + ' m') plt.xlabel('Easting (m)') plt.ylabel('Northing (m)') plt.gca().set_aspect('equal', adjustable='box') cb = plt.colorbar(dat[0], orientation="vertical", ticks=np.linspace(vmin, vmax, 4)) cb.set_label('Density (g/cc$^3$)') ax = plt.subplot(212) dat = mesh.plotSlice(Lpout, ax=ax, normal='Y', ind=yslice, clim=(vmin, vmax), pcolorOpts={'cmap': 'bwr'}) plt.title('Cross Section') plt.xlabel('Easting (m)') plt.ylabel('Elevation (m)') plt.gca().set_aspect('equal', adjustable='box') cb = plt.colorbar(dat[0], orientation="vertical", ticks=np.linspace(vmin, vmax, 4)) cb.set_label('Density (g/cc$^3$)')
wrGlobal = wrGlobal[actvGlobal]**0.5 wrGlobal = (wrGlobal / np.max(wrGlobal)) #%% Create a regularization actv = np.all([actv, actvMap * actvMeshGlobal], axis=0) actvMap = Maps.InjectActiveCells(mesh, actv, 0) actvMapAmp = Maps.InjectActiveCells(mesh, actv, -100) nC = int(np.sum(actv)) mstart = np.ones(nC) * 1e-4 mref = np.zeros(nC) # Create a regularization reg = Regularization.Sparse(mesh, indActive=actv, mapping=Maps.IdentityMap(nP=nC)) reg.cell_weights = wrGlobal reg.norms = np.c_[driver.lpnorms].T reg.mref = mref # Add directives to the inversion opt = Optimization.ProjectedGNCG(maxIter=100, lower=-10., upper=10., maxIterCG=20, tolCG=1e-3) invProb = InvProblem.BaseInvProblem(ComboMisfit, reg, opt) betaest = Directives.BetaEstimate_ByEig()
prob = PF.Magnetics.MagneticIntegral(mesh, chiMap=idenMap, actInd=actv) # Pair the survey and problem survey.pair(prob) # Create sensitivity weights from our linear forward operator rxLoc = survey.srcField.rxList[0].locs wr = np.zeros(prob.F.shape[1]) for ii in range(survey.nD): wr += (prob.F[ii, :] / survey.std[ii])**2. wr = (wr / np.max(wr)) wr = wr**0.5 # Create a regularization reg = Regularization.Sparse(mesh, indActive=actv, mapping=idenMap) reg.norms = driver.lpnorms if driver.eps is not None: reg.eps_p = driver.eps[0] reg.eps_q = driver.eps[1] reg.cell_weights = wr reg.mref = driver.mref # Data misfit function dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = 1. / survey.std # Add directives to the inversion opt = Optimization.ProjectedGNCG(maxIter=20,
def setUp(self): ndv = -100 # Create a mesh dx = 5. hxind = [(dx, 5, -1.3), (dx, 5), (dx, 5, 1.3)] hyind = [(dx, 5, -1.3), (dx, 5), (dx, 5, 1.3)] hzind = [(dx, 5, -1.3), (dx, 6)] mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC') # Get index of the center midx = int(mesh.nCx/2) midy = int(mesh.nCy/2) # Lets create a simple Gaussian topo and set the active cells [xx, yy] = np.meshgrid(mesh.vectorNx, mesh.vectorNy) zz = -np.exp((xx**2 + yy**2) / 75**2) + mesh.vectorNz[-1] # Go from topo to actv cells topo = np.c_[Utils.mkvc(xx), Utils.mkvc(yy), Utils.mkvc(zz)] actv = Utils.surface2ind_topo(mesh, topo, 'N') actv = np.asarray([inds for inds, elem in enumerate(actv, 1) if elem], dtype=int) - 1 # Create active map to go from reduce space to full actvMap = Maps.InjectActiveCells(mesh, actv, -100) nC = len(actv) # Create and array of observation points xr = np.linspace(-20., 20., 20) yr = np.linspace(-20., 20., 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.vectorNz[-1] + 5. # Create a MAGsurvey locXYZ = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)] rxLoc = PF.BaseGrav.RxObs(locXYZ) srcField = PF.BaseGrav.SrcField([rxLoc]) survey = PF.BaseGrav.LinearSurvey(srcField) # We can now create a density model and generate data # Here a simple block in half-space model = np.zeros((mesh.nCx, mesh.nCy, mesh.nCz)) model[(midx-2):(midx+2), (midy-2):(midy+2), -6:-2] = 0.5 model = Utils.mkvc(model) self.model = model[actv] # Create active map to go from reduce set to full actvMap = Maps.InjectActiveCells(mesh, actv, ndv) # Create reduced identity map idenMap = Maps.IdentityMap(nP=nC) # Create the forward model operator prob = PF.Gravity.GravityIntegral( mesh, rhoMap=idenMap, actInd=actv ) # Pair the survey and problem survey.pair(prob) # Compute linear forward operator and compute some data d = prob.fields(self.model) # Add noise and uncertainties (1nT) data = d + np.random.randn(len(d))*0.001 wd = np.ones(len(data))*.001 survey.dobs = data survey.std = wd # PF.Gravity.plot_obs_2D(survey.srcField.rxList[0].locs, d=data) # Create sensitivity weights from our linear forward operator wr = PF.Magnetics.get_dist_wgt(mesh, locXYZ, actv, 2., 2.) wr = wr**2. # Create a regularization reg = Regularization.Sparse(mesh, indActive=actv, mapping=idenMap) reg.cell_weights = wr reg.norms = [0, 1, 1, 1] reg.eps_p, reg.eps_q = 5e-2, 1e-2 # Data misfit function dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = 1/wd # Add directives to the inversion opt = Optimization.ProjectedGNCG(maxIter=100, lower=-1., upper=1., maxIterLS=20, maxIterCG=10, tolCG=1e-3) invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta=1e+8) # Here is where the norms are applied IRLS = Directives.Update_IRLS(f_min_change=1e-3, minGNiter=3) update_Jacobi = Directives.Update_lin_PreCond(mapping=idenMap) self.inv = Inversion.BaseInversion(invProb, directiveList=[IRLS, update_Jacobi])
def run_inversion( m0, survey, actind, mesh, std, eps, maxIter=15, beta0_ratio=1e0, coolingFactor=5, coolingRate=2, upper=np.inf, lower=-np.inf, use_sensitivity_weight=False, alpha_s=1e-4, alpha_x=1., alpha_y=1., alpha_z=1., ): """ Run IP inversion """ dmisfit = DataMisfit.l2_DataMisfit(survey) uncert = abs(survey.dobs) * std + eps dmisfit.W = 1./uncert # Map for a regularization regmap = Maps.IdentityMap(nP=int(actind.sum())) # Related to inversion if use_sensitivity_weight: reg = Regularization.Sparse( mesh, indActive=actind, mapping=regmap ) reg.alpha_s = alpha_s reg.alpha_x = alpha_x reg.alpha_y = alpha_y reg.alpha_z = alpha_z else: reg = Regularization.Sparse( mesh, indActive=actind, mapping=regmap, cell_weights=mesh.vol[actind] ) reg.alpha_s = alpha_s reg.alpha_x = alpha_x reg.alpha_y = alpha_y reg.alpha_z = alpha_z opt = Optimization.ProjectedGNCG(maxIter=maxIter, upper=upper, lower=lower) invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt) beta = Directives.BetaSchedule( coolingFactor=coolingFactor, coolingRate=coolingRate ) betaest = Directives.BetaEstimate_ByEig(beta0_ratio=beta0_ratio) target = Directives.TargetMisfit() # Need to have basice saving function if use_sensitivity_weight: updateSensW = Directives.UpdateSensitivityWeights() update_Jacobi = Directives.UpdatePreconditioner() directiveList = [ beta, betaest, target, update_Jacobi ] else: directiveList = [ beta, betaest, target ] inv = Inversion.BaseInversion( invProb, directiveList=directiveList ) opt.LSshorten = 0.5 opt.remember('xc') # Run inversion mopt = inv.run(m0) return mopt, invProb.dpred
wr = (wr / np.max(wr)) wr = wr else: wr = Mesh.TensorMesh.readModelUBC(mesh, work_dir + dsep + wgtfile) wr = wr[actv] wr = wr**2. Mesh.TensorMesh.writeModelUBC(mesh, work_dir + out_dir + 'SensWeights.den', actvMap * (homogMap.P * wr)) idenMap = Maps.IdentityMap(nP=nC) regMesh = Mesh.TensorMesh([nC]) # Create a regularization reg = Regularization.Sparse(regMesh, mapping=idenMap) reg.norms = driver.lpnorms if driver.eps is not None: reg.eps_p = driver.eps[0] reg.eps_q = driver.eps[1] reg.cell_weights = wr #driver.cell_weights*mesh.vol**0.5 reg.mref = mstart dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = 1. / survey.std # Write out the predicted file and generate the forward operator #pred = prob.fields(m0) #
def run(N=100, plotIt=True): np.random.seed(1) std_noise = 1e-2 mesh = Mesh.TensorMesh([N]) m0 = np.ones(mesh.nC) * 1e-4 mref = np.zeros(mesh.nC) nk = 20 jk = np.linspace(1., 60., nk) p = -0.25 q = 0.25 def g(k): return (np.exp(p * jk[k] * mesh.vectorCCx) * np.cos(np.pi * q * jk[k] * mesh.vectorCCx)) G = np.empty((nk, mesh.nC)) for i in range(nk): G[i, :] = g(i) mtrue = np.zeros(mesh.nC) mtrue[mesh.vectorCCx > 0.3] = 1. mtrue[mesh.vectorCCx > 0.45] = -0.5 mtrue[mesh.vectorCCx > 0.6] = 0 prob = Problem.LinearProblem(mesh, G=G) survey = Survey.LinearSurvey() survey.pair(prob) survey.dobs = prob.fields(mtrue) + std_noise * np.random.randn(nk) wd = np.ones(nk) * std_noise # Distance weighting wr = np.sum(prob.G**2., axis=0)**0.5 wr = wr / np.max(wr) dmis = DataMisfit.l2_DataMisfit(survey) dmis.Wd = 1. / wd betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1e-2) reg = Regularization.Sparse(mesh) reg.mref = mref reg.cell_weights = wr reg.mref = np.zeros(mesh.nC) opt = Optimization.ProjectedGNCG(maxIter=100, lower=-2., upper=2., maxIterLS=20, maxIterCG=10, tolCG=1e-3) invProb = InvProblem.BaseInvProblem(dmis, reg, opt) update_Jacobi = Directives.Update_lin_PreCond() # Set the IRLS directive, penalize the lowest 25 percentile of model values # Start with an l2-l2, then switch to lp-norms norms = [0., 0., 2., 2.] IRLS = Directives.Update_IRLS(norms=norms, prctile=25, maxIRLSiter=15, minGNiter=3) inv = Inversion.BaseInversion(invProb, directiveList=[IRLS, betaest, update_Jacobi]) # Run inversion mrec = inv.run(m0) print("Final misfit:" + str(invProb.dmisfit.eval(mrec))) if plotIt: fig, axes = plt.subplots(1, 2, 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].plot(mesh.vectorCCx, mtrue, 'b-') axes[1].plot(mesh.vectorCCx, reg.l2model, 'r-') # axes[1].legend(('True Model', 'Recovered Model')) axes[1].set_ylim(-1.0, 1.25) axes[1].plot(mesh.vectorCCx, mrec, 'k-', lw=2) axes[1].legend(('True Model', 'Smooth l2-l2', 'Sparse lp: {0}, lqx: {1}'.format(*reg.norms)), fontsize=12) return prob, survey, mesh, mrec
wr = (wr / np.max(wr)) wr = wr**0.5 #Mesh.TensorMesh.writeModelUBC(mesh, work_dir + out_dir + 'SensWeights.sus', # actvMap*(homogMap.P*wr)) # wr = PF.Magnetics.get_dist_wgt(mesh, rxLoc, actv, 3, 1) # Create a block diagonal regularization wires = Maps.Wires(('p', nC), ('s', nC), ('t', nC)) idenMap = Maps.IdentityMap(nP=nC) regMesh = Mesh.TensorMesh([nC]) # Create a regularization reg_p = Regularization.Sparse(regMesh, mapping=wires.p) reg_p.cell_weights = wires.p * wr reg_p.norms = [2, 2, 2, 2] reg_s = Regularization.Sparse(regMesh, mapping=wires.s) reg_s.cell_weights = wires.s * wr reg_s.norms = [2, 2, 2, 2] reg_t = Regularization.Sparse(regMesh, mapping=wires.t) reg_t.cell_weights = wires.t * wr reg_t.norms = [2, 2, 2, 2] reg = reg_p + reg_s + reg_t reg.mref = np.zeros(3 * nC) # Data misfit function
def setUp(self): np.random.seed(0) # First we need to define the direction of the inducing field # As a simple case, we pick a vertical inducing field of magnitude # 50,000nT. # From old convention, field orientation is given as an # azimuth from North (positive clockwise) # and dip from the horizontal (positive downward). H0 = (50000., 90., 0.) # Create a mesh h = [5, 5, 5] padDist = np.ones((3, 2)) * 100 nCpad = [2, 4, 2] # 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. + (yy / b)**2.)) # We would usually load a topofile topo = np.c_[Utils.mkvc(xx), Utils.mkvc(yy), Utils.mkvc(zz)] # Create and array of observation points xr = np.linspace(-100., 100., 20) yr = np.linspace(-100., 100., 20) X, Y = np.meshgrid(xr, yr) Z = A * np.exp(-0.5 * ((X / b)**2. + (Y / b)**2.)) + 5 # Create a MAGsurvey xyzLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)] rxLoc = PF.BaseMag.RxObs(xyzLoc) srcField = PF.BaseMag.SrcField([rxLoc], param=H0) survey = PF.BaseMag.LinearSurvey(srcField) # self.mesh.finalize() self.mesh = meshutils.mesh_builder_xyz( xyzLoc, h, padding_distance=padDist, mesh_type='TREE', ) self.mesh = meshutils.refine_tree_xyz( self.mesh, topo, method='surface', octree_levels=nCpad, octree_levels_padding=nCpad, finalize=True, ) # Define an active cells from topo actv = Utils.surface2ind_topo(self.mesh, topo) nC = int(actv.sum()) # We can now create a susceptibility model and generate data # Lets start with a simple block in half-space self.model = Utils.ModelBuilder.addBlock(self.mesh.gridCC, np.zeros(self.mesh.nC), np.r_[-20, -20, -15], np.r_[20, 20, 20], 0.05)[actv] # Create active map to go from reduce set to full self.actvMap = Maps.InjectActiveCells(self.mesh, actv, np.nan) # Creat reduced identity map idenMap = Maps.IdentityMap(nP=nC) # Create the forward model operator prob = PF.Magnetics.MagneticIntegral(self.mesh, chiMap=idenMap, actInd=actv) # Pair the survey and problem survey.pair(prob) # Compute linear forward operator and compute some data data = prob.fields(self.model) # Add noise and uncertainties (1nT) noise = np.random.randn(len(data)) data += noise wd = np.ones(len(data)) * 1. survey.dobs = data survey.std = wd # Create sensitivity weights from our linear forward operator rxLoc = survey.srcField.rxList[0].locs wr = prob.getJtJdiag(self.model)**0.5 wr /= np.max(wr) # Create a regularization reg = Regularization.Sparse(self.mesh, indActive=actv, mapping=idenMap) reg.norms = np.c_[0, 0, 0, 0] reg.cell_weights = wr reg.mref = np.zeros(nC) # Data misfit function dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = 1. / survey.std # Add directives to the inversion opt = Optimization.ProjectedGNCG(maxIter=20, lower=0., upper=10., maxIterLS=20, maxIterCG=20, tolCG=1e-4, stepOffBoundsFact=1e-4) invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta=1e+6) # Here is where the norms are applied # Use pick a treshold parameter empirically based on the distribution of # model parameters IRLS = Directives.Update_IRLS(f_min_change=1e-3, maxIRLSiter=20, beta_tol=1e-1, betaSearch=False) update_Jacobi = Directives.UpdatePreconditioner() # saveOuput = Directives.SaveOutputEveryIteration() # saveModel.fileName = work_dir + out_dir + 'ModelSus' self.inv = Inversion.BaseInversion(invProb, directiveList=[IRLS, update_Jacobi])
wr = prob.getJtJdiag(m0, W=dmis.W) wr = (wr / np.max(wr)) wr = wr**0.5 #Mesh.TensorMesh.writeModelUBC(mesh, work_dir + out_dir + 'SensWeights.sus', # actvMap*(homogMap.P*wr)) # Create a block diagonal regularization wires = Maps.Wires(('p', nC), ('s', nC), ('t', nC)) idenMap = Maps.IdentityMap(nP=nC) regMesh = Mesh.TensorMesh([nC]) # Create a regularization reg_p = Regularization.Sparse(regMesh, mapping=wires.p) #reg_p.cell_weights = wires.p * wr #reg_p.norms = [2, 2, 2, 2] reg_s = Regularization.Sparse(regMesh, mapping=wires.s) #reg_s.cell_weights = wires.s * wr #reg_s.norms = [2, 2, 2, 2] reg_t = Regularization.Sparse(regMesh, mapping=wires.t) #reg_t.cell_weights = wires.t * wr #reg_t.norms = [2, 2, 2, 2] reg = reg_p + reg_s + reg_t reg.mref = np.zeros(3 * nC) # Add directives to the inversion
# actv = np.all([actv, actvMap*actvMeshGlobal], axis=0) actvMap = Maps.InjectActiveCells(mesh, actv, 0) # For re-projection actvMapAmp = Maps.InjectActiveCells(mesh, actv, -100) # For final output nC = int(np.sum(actv)) mstart = np.ones(3 * nC) * 1e-4 # Assumes amplitude reference, distributed on 3 components mref = np.ones(3 * nC) * (np.mean(driver.mref)**2. / 3)**0.5 # Create a block diagonal regularization wires = Maps.Wires(('p', nC), ('s', nC), ('t', nC)) # Create a regularization reg_p = Regularization.Sparse(mesh, indActive=actv, mapping=wires.p) reg_p.cell_weights = (wires.p * wrGlobal) reg_p.norms = np.c_[2, 2, 2, 2] reg_p.mref = mref reg_s = Regularization.Sparse(mesh, indActive=actv, mapping=wires.s) reg_s.cell_weights = (wires.s * wrGlobal) reg_s.norms = np.c_[2, 2, 2, 2] reg_s.mref = mref reg_t = Regularization.Sparse(mesh, indActive=actv, mapping=wires.t) reg_t.cell_weights = (wires.t * wrGlobal) reg_t.norms = np.c_[2, 2, 2, 2] reg_t.mref = mref # Assemble the 3-component regularizations
# Create the forward model operator prob = PF.Magnetics.MagneticVector(mesh, chiMap=idenMap, actInd=actv) # Pair the survey and problem survey.pair(prob) # Data misfit function dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = 1. / survey.std wr = np.sum(prob.F**2., axis=0)**0.5 wr = (wr / np.max(wr)) * np.r_[mamp, mamp, mamp] # Create a regularization reg_p = Regularization.Sparse(mesh, indActive=actv, mapping=wires.prim) reg_p.cell_weights = wires.prim * wr reg_s = Regularization.Sparse(mesh, indActive=actv, mapping=wires.second) reg_s.cell_weights = wires.second * wr reg_t = Regularization.Sparse(mesh, indActive=actv, mapping=wires.third) reg_t.cell_weights = wires.third * wr reg = reg_p + reg_s + reg_t # Add directives to the inversion opt = Optimization.ProjectedGNCG(maxIter=30, lower=-10., upper=10., maxIterCG=20,
def run(plotIt=True, survey_type="dipole-dipole", p=0., qx=2., qz=2.): np.random.seed(1) # Initiate I/O class for DC IO = DC.IO() # Obtain ABMN locations xmin, xmax = 0., 200. ymin, ymax = 0., 0. zmin, zmax = 0, 0 endl = np.array([[xmin, ymin, zmin], [xmax, ymax, zmax]]) # Generate DC survey object survey = DC.Utils.gen_DCIPsurvey(endl, survey_type=survey_type, dim=2, a=10, b=10, n=10) survey.getABMN_locations() survey = IO.from_ambn_locations_to_survey(survey.a_locations, survey.b_locations, survey.m_locations, survey.n_locations, survey_type, data_dc_type='volt') # Obtain 2D TensorMesh mesh, actind = IO.set_mesh() topo, mesh1D = DC.Utils.genTopography(mesh, -10, 0, its=100) actind = Utils.surface2ind_topo(mesh, np.c_[mesh1D.vectorCCx, topo]) survey.drapeTopo(mesh, actind, option="top") # Build a conductivity model blk_inds_c = Utils.ModelBuilder.getIndicesSphere(np.r_[60., -25.], 12.5, mesh.gridCC) blk_inds_r = Utils.ModelBuilder.getIndicesSphere(np.r_[140., -25.], 12.5, mesh.gridCC) layer_inds = mesh.gridCC[:, 1] > -5. sigma = np.ones(mesh.nC) * 1. / 100. sigma[blk_inds_c] = 1. / 10. sigma[blk_inds_r] = 1. / 1000. sigma[~actind] = 1. / 1e8 rho = 1. / sigma # Show the true conductivity model if plotIt: fig = plt.figure(figsize=(12, 3)) ax = plt.subplot(111) temp = rho.copy() temp[~actind] = np.nan out = mesh.plotImage(temp, grid=True, ax=ax, gridOpts={'alpha': 0.2}, clim=(10, 1000), pcolorOpts={ "cmap": "viridis", "norm": colors.LogNorm() }) ax.plot(survey.electrode_locations[:, 0], survey.electrode_locations[:, 1], 'k.') ax.set_xlim(IO.grids[:, 0].min(), IO.grids[:, 0].max()) ax.set_ylim(-IO.grids[:, 1].max(), IO.grids[:, 1].min()) cb = plt.colorbar(out[0]) cb.set_label("Resistivity (ohm-m)") ax.set_aspect('equal') plt.show() # Use Exponential Map: m = log(rho) actmap = Maps.InjectActiveCells(mesh, indActive=actind, valInactive=np.log(1e8)) mapping = Maps.ExpMap(mesh) * actmap # Generate mtrue mtrue = np.log(rho[actind]) # Generate 2.5D DC problem # "N" means potential is defined at nodes prb = DC.Problem2D_N(mesh, rhoMap=mapping, storeJ=True, Solver=Solver, verbose=True) # Pair problem with survey try: prb.pair(survey) except: survey.unpair() prb.pair(survey) # Make synthetic DC data with 5% Gaussian noise dtrue = survey.makeSyntheticData(mtrue, std=0.05, force=True) IO.data_dc = dtrue # Show apparent resisitivty pseudo-section if plotIt: IO.plotPseudoSection(data=survey.dobs / IO.G, data_type='apparent_resistivity') # Show apparent resisitivty histogram if plotIt: fig = plt.figure() out = hist(survey.dobs / IO.G, bins=20) plt.xlabel("Apparent Resisitivty ($\Omega$m)") plt.show() # Set initial model based upon histogram m0 = np.ones(actmap.nP) * np.log(100.) # Set uncertainty # floor eps = 10**(-3.2) # percentage std = 0.05 dmisfit = DataMisfit.l2_DataMisfit(survey) uncert = abs(survey.dobs) * std + eps dmisfit.W = 1. / uncert # Map for a regularization regmap = Maps.IdentityMap(nP=int(actind.sum())) # Related to inversion reg = Regularization.Sparse(mesh, indActive=actind, mapping=regmap, gradientType='components') # gradientType = 'components' reg.norms = np.c_[p, qx, qz, 0.] IRLS = Directives.Update_IRLS(maxIRLSiter=20, minGNiter=1, betaSearch=False, fix_Jmatrix=True) opt = Optimization.InexactGaussNewton(maxIter=40) invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt) beta = Directives.BetaSchedule(coolingFactor=5, coolingRate=2) betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1e0) target = Directives.TargetMisfit() update_Jacobi = Directives.UpdatePreconditioner() inv = Inversion.BaseInversion(invProb, directiveList=[betaest, IRLS]) prb.counter = opt.counter = Utils.Counter() opt.LSshorten = 0.5 opt.remember('xc') # Run inversion mopt = inv.run(m0) rho_est = mapping * mopt rho_est_l2 = mapping * invProb.l2model rho_est[~actind] = np.nan rho_est_l2[~actind] = np.nan rho_true = rho.copy() rho_true[~actind] = np.nan # show recovered conductivity if plotIt: vmin, vmax = rho.min(), rho.max() fig, ax = plt.subplots(3, 1, figsize=(20, 9)) out1 = mesh.plotImage(rho_true, clim=(10, 1000), pcolorOpts={ "cmap": "viridis", "norm": colors.LogNorm() }, ax=ax[0]) out2 = mesh.plotImage(rho_est_l2, clim=(10, 1000), pcolorOpts={ "cmap": "viridis", "norm": colors.LogNorm() }, ax=ax[1]) out3 = mesh.plotImage(rho_est, clim=(10, 1000), pcolorOpts={ "cmap": "viridis", "norm": colors.LogNorm() }, ax=ax[2]) out = [out1, out2, out3] titles = ["True", "L2", ("L%d, Lx%d, Lz%d") % (p, qx, qz)] for i in range(3): ax[i].plot(survey.electrode_locations[:, 0], survey.electrode_locations[:, 1], 'kv') ax[i].set_xlim(IO.grids[:, 0].min(), IO.grids[:, 0].max()) ax[i].set_ylim(-IO.grids[:, 1].max(), IO.grids[:, 1].min()) cb = plt.colorbar(out[i][0], ax=ax[i]) cb.set_label("Resistivity ($\Omega$m)") ax[i].set_xlabel("Northing (m)") ax[i].set_ylabel("Elevation (m)") ax[i].set_aspect('equal') ax[i].set_title(titles[i]) plt.tight_layout() plt.show()
## Create a regularization # For the homogeneous model #regMesh = Mesh.TensorMesh([nC]) # #reg_m1 = Regularization.Sparse(regMesh, mapping=wires.h**o) #reg_m1.cell_weights = wires.h**o*wr*2. #if driver.eps is not None: # reg_m1.eps_p = driver.eps[0] # reg_m1.eps_q = driver.eps[1] #reg_m1.norms = [2, 2, 2, 2] #reg_m1.mref = mref # Regularization for the voxel model reg_m2 = Regularization.Sparse(mesh, indActive=actv, mapping=Maps.IdentityMap(nP=int(dynamic.sum())), gradientType=gradientType) reg_m2.cell_weights = wr reg_m2.norms = driver.lpnorms if driver.eps is not None: reg_m2.eps_p = driver.eps[0] reg_m2.eps_q = driver.eps[1] reg_m2.mref = mref[dynamic] reg = reg_m2 dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = 1./survey.std opt = Optimization.ProjectedGNCG(maxIter=30, lower=driver.bounds[0], upper=driver.bounds[1], maxIterLS = 20, maxIterCG= 30,
global_misfit += local_misfit else: global_misfit, global_weights = createLocalProb(mesh, survey, global_weights, 0) # Global sensitivity weights (linear) global_weights = global_weights**0.5 global_weights = (global_weights / np.max(global_weights)) if input_dict["inversion_type"].lower() in ['grav', 'mag']: # Create a regularization function reg = Regularization.Sparse(mesh, indActive=activeCells, mapping=idenMap, alpha_s=alphas[0], alpha_x=alphas[1], alpha_y=alphas[2], alpha_z=alphas[3]) reg.norms = np.c_[model_norms].T reg.cell_weights = global_weights if isinstance(model_reference, str): mref = mesh.readModelUBC(workDir + model_reference) reg.mref = mref[activeCells] else: reg.mref = np.ones(nC) * model_reference[0] if isinstance(model_start, str): mstart = mesh.readModelUBC(workDir + model_start) mstart = mstart[activeCells]
def run(plotIt=True): # Create a mesh dx = 5. hxind = [(dx, 5, -1.3), (dx, 15), (dx, 5, 1.3)] hyind = [(dx, 5, -1.3), (dx, 15), (dx, 5, 1.3)] hzind = [(dx, 5, -1.3), (dx, 7), (3.5, 1), (2, 5)] mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC') # Get index of the center midx = int(mesh.nCx/2) midy = int(mesh.nCy/2) # Lets create a simple Gaussian topo and set the active cells [xx, yy] = np.meshgrid(mesh.vectorNx, mesh.vectorNy) zz = -np.exp((xx**2 + yy**2) / 75**2) + mesh.vectorNz[-1] # We would usually load a topofile topo = np.c_[Utils.mkvc(xx), Utils.mkvc(yy), Utils.mkvc(zz)] # Go from topo to actv cells actv = Utils.surface2ind_topo(mesh, topo, 'N') actv = np.asarray([inds for inds, elem in enumerate(actv, 1) if elem], dtype=int) - 1 # Create active map to go from reduce space to full actvMap = Maps.InjectActiveCells(mesh, actv, -100) nC = len(actv) # Create and array of observation points xr = np.linspace(-30., 30., 20) yr = np.linspace(-30., 30., 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.vectorNz[-1] + 0.1 # Create a MAGsurvey rxLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)] rxLoc = PF.BaseGrav.RxObs(rxLoc) srcField = PF.BaseGrav.SrcField([rxLoc]) survey = PF.BaseGrav.LinearSurvey(srcField) # We can now create a susceptibility model and generate data # Here a simple block in half-space model = np.zeros((mesh.nCx, mesh.nCy, mesh.nCz)) model[(midx-5):(midx-1), (midy-2):(midy+2), -10:-6] = 0.5 model[(midx+1):(midx+5), (midy-2):(midy+2), -10:-6] = -0.5 model = Utils.mkvc(model) model = model[actv] # Create active map to go from reduce set to full actvMap = Maps.InjectActiveCells(mesh, actv, -100) # Create reduced identity map idenMap = Maps.IdentityMap(nP=nC) # Create the forward model operator prob = PF.Gravity.GravityIntegral(mesh, rhoMap=idenMap, actInd=actv) # Pair the survey and problem survey.pair(prob) # Compute linear forward operator and compute some data d = prob.fields(model) # Add noise and uncertainties # We add some random Gaussian noise (1nT) data = d + np.random.randn(len(d))*1e-3 wd = np.ones(len(data))*1e-3 # Assign flat uncertainties survey.dobs = data survey.std = wd survey.mtrue = model # Create sensitivity weights from our linear forward operator rxLoc = survey.srcField.rxList[0].locs wr = np.sum(prob.G**2., axis=0)**0.5 wr = (wr/np.max(wr)) # Create a regularization reg = Regularization.Sparse(mesh, indActive=actv, mapping=idenMap) reg.cell_weights = wr reg.norms = [0, 1, 1, 1] # Data misfit function dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = Utils.sdiag(1/wd) # Add directives to the inversion opt = Optimization.ProjectedGNCG(maxIter=100, lower=-1., upper=1., maxIterLS=20, maxIterCG=10, tolCG=1e-3) invProb = InvProblem.BaseInvProblem(dmis, reg, opt) betaest = Directives.BetaEstimate_ByEig() # Here is where the norms are applied # Use pick a treshold parameter empirically based on the distribution of # model parameters IRLS = Directives.Update_IRLS(f_min_change=1e-2, minGNiter=2) update_Jacobi = Directives.UpdatePreconditioner() inv = Inversion.BaseInversion(invProb, directiveList=[IRLS, betaest, update_Jacobi]) # Run the inversion m0 = np.ones(nC)*1e-4 # Starting model mrec = inv.run(m0) if plotIt: # Here is the recovered susceptibility model ypanel = midx zpanel = -7 m_l2 = actvMap * invProb.l2model m_l2[m_l2 == -100] = np.nan m_lp = actvMap * mrec m_lp[m_lp == -100] = np.nan m_true = actvMap * model m_true[m_true == -100] = np.nan vmin, vmax = mrec.min(), mrec.max() # Plot the data PF.Gravity.plot_obs_2D(rxLoc, d=data) plt.figure() # Plot L2 model ax = plt.subplot(321) mesh.plotSlice(m_l2, ax=ax, normal='Z', ind=zpanel, grid=True, clim=(vmin, vmax)) plt.plot(([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), ([mesh.vectorCCy[ypanel], mesh.vectorCCy[ypanel]]), color='w') plt.title('Plan l2-model.') plt.gca().set_aspect('equal') plt.ylabel('y') ax.xaxis.set_visible(False) plt.gca().set_aspect('equal', adjustable='box') # Vertica section ax = plt.subplot(322) mesh.plotSlice(m_l2, ax=ax, normal='Y', ind=midx, grid=True, clim=(vmin, vmax)) plt.plot(([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), ([mesh.vectorCCz[zpanel], mesh.vectorCCz[zpanel]]), color='w') plt.title('E-W l2-model.') plt.gca().set_aspect('equal') ax.xaxis.set_visible(False) plt.ylabel('z') plt.gca().set_aspect('equal', adjustable='box') # Plot Lp model ax = plt.subplot(323) mesh.plotSlice(m_lp, ax=ax, normal='Z', ind=zpanel, grid=True, clim=(vmin, vmax)) plt.plot(([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), ([mesh.vectorCCy[ypanel], mesh.vectorCCy[ypanel]]), color='w') plt.title('Plan lp-model.') plt.gca().set_aspect('equal') ax.xaxis.set_visible(False) plt.ylabel('y') plt.gca().set_aspect('equal', adjustable='box') # Vertical section ax = plt.subplot(324) mesh.plotSlice(m_lp, ax=ax, normal='Y', ind=midx, grid=True, clim=(vmin, vmax)) plt.plot(([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), ([mesh.vectorCCz[zpanel], mesh.vectorCCz[zpanel]]), color='w') plt.title('E-W lp-model.') plt.gca().set_aspect('equal') ax.xaxis.set_visible(False) plt.ylabel('z') plt.gca().set_aspect('equal', adjustable='box') # Plot True model ax = plt.subplot(325) mesh.plotSlice(m_true, ax=ax, normal='Z', ind=zpanel, grid=True, clim=(vmin, vmax)) plt.plot(([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), ([mesh.vectorCCy[ypanel], mesh.vectorCCy[ypanel]]), color='w') plt.title('Plan true model.') plt.gca().set_aspect('equal') plt.xlabel('x') plt.ylabel('y') plt.gca().set_aspect('equal', adjustable='box') # Vertical section ax = plt.subplot(326) mesh.plotSlice(m_true, ax=ax, normal='Y', ind=midx, grid=True, clim=(vmin, vmax)) plt.plot(([mesh.vectorCCx[0], mesh.vectorCCx[-1]]), ([mesh.vectorCCz[zpanel], mesh.vectorCCz[zpanel]]), color='w') plt.title('E-W true model.') plt.gca().set_aspect('equal') plt.xlabel('x') plt.ylabel('z') plt.gca().set_aspect('equal', adjustable='box')
def setUp(self): np.random.seed(0) H0 = (50000., 90., 0.) # The magnetization is set along a different # direction (induced + remanence) M = np.array([45., 90.]) # 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. + (yy/b)**2.)) # We would usually load a topofile topo = np.c_[Utils.mkvc(xx), Utils.mkvc(yy), Utils.mkvc(zz)] # Create and array of observation points xr = np.linspace(-100., 100., 20) yr = np.linspace(-100., 100., 20) X, Y = np.meshgrid(xr, yr) Z = A*np.exp(-0.5*((X/b)**2. + (Y/b)**2.)) + 5 # Create a MAGsurvey xyzLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)] rxLoc = PF.BaseMag.RxObs(xyzLoc) srcField = PF.BaseMag.SrcField([rxLoc], param=H0) survey = PF.BaseMag.LinearSurvey(srcField) # Create a mesh h = [5, 5, 5] padDist = np.ones((3, 2)) * 100 nCpad = [2, 4, 2] # Get extent of points limx = np.r_[topo[:, 0].max(), topo[:, 0].min()] limy = np.r_[topo[:, 1].max(), topo[:, 1].min()] limz = np.r_[topo[:, 2].max(), topo[:, 2].min()] # Get center of the mesh midX = np.mean(limx) midY = np.mean(limy) midZ = np.mean(limz) nCx = int(limx[0]-limx[1]) / h[0] nCy = int(limy[0]-limy[1]) / h[1] nCz = int(limz[0]-limz[1]+int(np.min(np.r_[nCx, nCy])/3)) / h[2] # Figure out full extent required from input extent = np.max(np.r_[nCx * h[0] + padDist[0, :].sum(), nCy * h[1] + padDist[1, :].sum(), nCz * h[2] + padDist[2, :].sum()]) maxLevel = int(np.log2(extent/h[0]))+1 # Number of cells at the small octree level nCx, nCy, nCz = 2**(maxLevel), 2**(maxLevel), 2**(maxLevel) # Define the mesh and origin # For now cubic cells mesh = Mesh.TreeMesh([np.ones(nCx)*h[0], np.ones(nCx)*h[1], np.ones(nCx)*h[2]]) # Set origin mesh.x0 = np.r_[ -nCx*h[0]/2.+midX, -nCy*h[1]/2.+midY, -nCz*h[2]/2.+midZ ] # Refine the mesh around topography # Get extent of points F = NearestNDInterpolator(topo[:, :2], topo[:, 2]) zOffset = 0 # Cycle through the first 3 octree levels for ii in range(3): dx = mesh.hx.min()*2**ii nCx = int((limx[0]-limx[1]) / dx) nCy = int((limy[0]-limy[1]) / dx) # Create a grid at the octree level in xy CCx, CCy = np.meshgrid( np.linspace(limx[1], limx[0], nCx), np.linspace(limy[1], limy[0], nCy) ) z = F(mkvc(CCx), mkvc(CCy)) # level means number of layers in current OcTree level for level in range(int(nCpad[ii])): mesh.insert_cells( np.c_[ mkvc(CCx), mkvc(CCy), z-zOffset ], np.ones_like(z)*maxLevel-ii, finalize=False ) zOffset += dx mesh.finalize() self.mesh = mesh # Define an active cells from topo actv = Utils.surface2ind_topo(mesh, topo) nC = int(actv.sum()) model = np.zeros((mesh.nC, 3)) # Convert the inclination declination to vector in Cartesian M_xyz = Utils.matutils.dip_azimuth2cartesian(M[0], M[1]) # Get the indicies of the magnetized block ind = Utils.ModelBuilder.getIndicesBlock( np.r_[-20, -20, -10], np.r_[20, 20, 25], mesh.gridCC, )[0] # Assign magnetization values model[ind, :] = np.kron( np.ones((ind.shape[0], 1)), M_xyz*0.05 ) # Remove air cells self.model = model[actv, :] # Create active map to go from reduce set to full self.actvMap = Maps.InjectActiveCells(mesh, actv, np.nan) # Creat reduced identity map idenMap = Maps.IdentityMap(nP=nC*3) # Create the forward model operator prob = PF.Magnetics.MagneticIntegral( mesh, chiMap=idenMap, actInd=actv, modelType='vector' ) # Pair the survey and problem survey.pair(prob) # Compute some data and add some random noise data = prob.fields(Utils.mkvc(self.model)) std = 5 # nT data += np.random.randn(len(data))*std wd = np.ones(len(data))*std # Assigne data and uncertainties to the survey survey.dobs = data survey.std = wd # Create an projection matrix for plotting later actvPlot = Maps.InjectActiveCells(mesh, actv, np.nan) # Create sensitivity weights from our linear forward operator rxLoc = survey.srcField.rxList[0].locs # This Mapping connects the regularizations for the three-component # vector model wires = Maps.Wires(('p', nC), ('s', nC), ('t', nC)) # Create sensitivity weights from our linear forward operator # so that all cells get equal chance to contribute to the solution wr = np.sum(prob.G**2., axis=0)**0.5 wr = (wr/np.max(wr)) # Create three regularization for the different components # of magnetization reg_p = Regularization.Sparse(mesh, indActive=actv, mapping=wires.p) reg_p.mref = np.zeros(3*nC) reg_p.cell_weights = (wires.p * wr) reg_s = Regularization.Sparse(mesh, indActive=actv, mapping=wires.s) reg_s.mref = np.zeros(3*nC) reg_s.cell_weights = (wires.s * wr) reg_t = Regularization.Sparse(mesh, indActive=actv, mapping=wires.t) reg_t.mref = np.zeros(3*nC) reg_t.cell_weights = (wires.t * wr) reg = reg_p + reg_s + reg_t reg.mref = np.zeros(3*nC) # Data misfit function dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = 1./survey.std # Add directives to the inversion opt = Optimization.ProjectedGNCG(maxIter=30, lower=-10, upper=10., maxIterLS=20, maxIterCG=20, tolCG=1e-4) invProb = InvProblem.BaseInvProblem(dmis, reg, opt) # A list of directive to control the inverson betaest = Directives.BetaEstimate_ByEig() # Here is where the norms are applied # Use pick a treshold parameter empirically based on the distribution of # model parameters IRLS = Directives.Update_IRLS( f_min_change=1e-3, maxIRLSiter=0, beta_tol=5e-1 ) # Pre-conditioner update_Jacobi = Directives.UpdatePreconditioner() inv = Inversion.BaseInversion(invProb, directiveList=[IRLS, update_Jacobi, betaest]) # Run the inversion m0 = np.ones(3*nC) * 1e-4 # Starting model mrec_MVIC = inv.run(m0) self.mstart = Utils.matutils.cartesian2spherical(mrec_MVIC.reshape((nC, 3), order='F')) beta = invProb.beta dmis.prob.coordinate_system = 'spherical' dmis.prob.model = self.mstart # Create a block diagonal regularization wires = Maps.Wires(('amp', nC), ('theta', nC), ('phi', nC)) # Create a Combo Regularization # Regularize the amplitude of the vectors reg_a = Regularization.Sparse(mesh, indActive=actv, mapping=wires.amp) reg_a.norms = np.c_[0., 0., 0., 0.] # Sparse on the model and its gradients reg_a.mref = np.zeros(3*nC) # Regularize the vertical angle of the vectors reg_t = Regularization.Sparse(mesh, indActive=actv, mapping=wires.theta) reg_t.alpha_s = 0. # No reference angle reg_t.space = 'spherical' reg_t.norms = np.c_[2., 0., 0., 0.] # Only norm on gradients used # Regularize the horizontal angle of the vectors reg_p = Regularization.Sparse(mesh, indActive=actv, mapping=wires.phi) reg_p.alpha_s = 0. # No reference angle reg_p.space = 'spherical' reg_p.norms = np.c_[2., 0., 0., 0.] # Only norm on gradients used reg = reg_a + reg_t + reg_p reg.mref = np.zeros(3*nC) Lbound = np.kron(np.asarray([0, -np.inf, -np.inf]), np.ones(nC)) Ubound = np.kron(np.asarray([10, np.inf, np.inf]), np.ones(nC)) # Add directives to the inversion opt = Optimization.ProjectedGNCG(maxIter=20, lower=Lbound, upper=Ubound, maxIterLS=20, maxIterCG=30, tolCG=1e-3, stepOffBoundsFact=1e-3, ) opt.approxHinv = None invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta=beta*10.) # Here is where the norms are applied IRLS = Directives.Update_IRLS(f_min_change=1e-4, maxIRLSiter=20, minGNiter=1, beta_tol=0.5, coolingRate=1, coolEps_q=True, betaSearch=False) # Special directive specific to the mag amplitude problem. The sensitivity # weights are update between each iteration. ProjSpherical = Directives.ProjectSphericalBounds() update_SensWeight = Directives.UpdateSensitivityWeights() update_Jacobi = Directives.UpdatePreconditioner() self.inv = Inversion.BaseInversion( invProb, directiveList=[ ProjSpherical, IRLS, update_SensWeight, update_Jacobi ] )
def run_inversion( self, maxIter=60, m0=0.0, mref=0.0, percentage=5, floor=0.1, chifact=1, beta0_ratio=1.0, coolingFactor=1, n_iter_per_beta=1, alpha_s=1.0, alpha_x=1.0, alpha_z=1.0, use_target=False, use_tikhonov=True, use_irls=False, p_s=2, p_x=2, p_y=2, p_z=2, beta_start=None, ): self.uncertainty = percentage * abs(self.survey.dobs) * 0.01 + floor m0 = np.ones(self.mesh.nC) * m0 mref = np.ones(self.mesh.nC) * mref if ~use_tikhonov: reg = Regularization.Sparse( self.mesh, alpha_s=alpha_s, alpha_x=alpha_x, alpha_y=alpha_z, mref=mref, mapping=Maps.IdentityMap(self.mesh), cell_weights=self.mesh.vol, ) else: reg = Regularization.Tikhonov( self.mesh, alpha_s=alpha_s, alpha_x=alpha_x, alpha_y=alpha_z, mref=mref, mapping=Maps.IdentityMap(self.mesh), ) dmis = DataMisfit.l2_DataMisfit(self.survey) dmis.W = 1.0 / self.uncertainty opt = Optimization.ProjectedGNCG(maxIter=maxIter, maxIterCG=20) opt.lower = 0.0 opt.remember("xc") opt.tolG = 1e-10 opt.eps = 1e-10 invProb = InvProblem.BaseInvProblem(dmis, reg, opt) save = Directives.SaveOutputEveryIteration() beta_schedule = Directives.BetaSchedule(coolingFactor=coolingFactor, coolingRate=n_iter_per_beta) if use_irls: IRLS = Directives.Update_IRLS( f_min_change=1e-4, minGNiter=1, silent=False, maxIRLSiter=40, beta_tol=5e-1, coolEpsFact=1.3, chifact_start=chifact, ) if beta_start is None: directives = [ Directives.BetaEstimate_ByEig(beta0_ratio=beta0_ratio), IRLS, save, ] else: directives = [IRLS, save] invProb.beta = beta_start reg.norms = np.c_[p_s, p_x, p_z, 2] else: target = Directives.TargetMisfit(chifact=chifact) directives = [ Directives.BetaEstimate_ByEig(beta0_ratio=beta0_ratio), beta_schedule, save, ] if use_target: directives.append(target) inv = Inversion.BaseInversion(invProb, directiveList=directives) mopt = inv.run(m0) model = opt.recall("xc") model.append(mopt) pred = [] for m in model: pred.append(self.survey.dpred(m)) return model, pred, save
(activeCellsMap*model_map*global_weights)[:mesh.nC]} ) else: mesh.writeModelUBC( 'SensWeights.mod', (activeCellsMap*model_map*global_weights)[:mesh.nC] ) if not vector_property: # Create a regularization function reg = Regularization.Sparse( regularization_mesh, indActive=regularization_actv, mapping=regularization_map, alpha_s=alphas[0], alpha_x=alphas[1], alpha_y=alphas[2], alpha_z=alphas[3] ) reg.norms = np.c_[model_norms].T reg.cell_weights = global_weights reg.mref = mref else: # Create a regularization reg_p = Regularization.Sparse( mesh, indActive=activeCells, mapping=regularization_map.p, gradientType=gradient_type,
def setUp(self): np.random.seed(0) # Define the inducing field parameter H0 = (50000, 90, 0) # Create a mesh dx = 5. hxind = [(dx, 5, -1.3), (dx, 5), (dx, 5, 1.3)] hyind = [(dx, 5, -1.3), (dx, 5), (dx, 5, 1.3)] hzind = [(dx, 5, -1.3), (dx, 6)] mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCC') # Get index of the center midx = int(mesh.nCx / 2) midy = int(mesh.nCy / 2) # Lets create a simple Gaussian topo and set the active cells [xx, yy] = np.meshgrid(mesh.vectorNx, mesh.vectorNy) zz = -np.exp((xx**2 + yy**2) / 75**2) + mesh.vectorNz[-1] # Go from topo to actv cells topo = np.c_[Utils.mkvc(xx), Utils.mkvc(yy), Utils.mkvc(zz)] actv = Utils.surface2ind_topo(mesh, topo, 'N') actv = np.asarray([inds for inds, elem in enumerate(actv, 1) if elem], dtype=int) - 1 # Create active map to go from reduce space to full actvMap = Maps.InjectActiveCells(mesh, actv, -100) nC = len(actv) # Create and array of observation points xr = np.linspace(-20., 20., 20) yr = np.linspace(-20., 20., 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.vectorNz[-1] + 5. # Create a MAGsurvey rxLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)] rxLoc = PF.BaseMag.RxObs(rxLoc) srcField = PF.BaseMag.SrcField([rxLoc], param=H0) survey = PF.BaseMag.LinearSurvey(srcField) # We can now create a susceptibility model and generate data # Here a simple block in half-space model = np.zeros((mesh.nCx, mesh.nCy, mesh.nCz)) model[(midx - 2):(midx + 2), (midy - 2):(midy + 2), -6:-2] = 0.02 model = Utils.mkvc(model) self.model = model[actv] # Create active map to go from reduce set to full actvMap = Maps.InjectActiveCells(mesh, actv, -100) # Creat reduced identity map idenMap = Maps.IdentityMap(nP=nC) # Create the forward model operator prob = PF.Magnetics.MagneticIntegral(mesh, chiMap=idenMap, actInd=actv) # Pair the survey and problem survey.pair(prob) # Compute linear forward operator and compute some data d = prob.fields(self.model) # Add noise and uncertainties (1nT) data = d + np.random.randn(len(d)) wd = np.ones(len(data)) * 1. survey.dobs = data survey.std = wd # Create sensitivity weights from our linear forward operator wr = np.sum(prob.G**2., axis=0)**0.5 wr = (wr / np.max(wr)) # Create a regularization reg = Regularization.Sparse(mesh, indActive=actv, mapping=idenMap) reg.cell_weights = wr reg.norms = np.c_[0, 0, 0, 0] reg.gradientType = 'component' # reg.eps_p, reg.eps_q = 1e-3, 1e-3 # Data misfit function dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = 1 / wd # Add directives to the inversion opt = Optimization.ProjectedGNCG(maxIter=100, lower=0., upper=1., maxIterLS=20, maxIterCG=10, tolCG=1e-3) invProb = InvProblem.BaseInvProblem(dmis, reg, opt) betaest = Directives.BetaEstimate_ByEig() # Here is where the norms are applied IRLS = Directives.Update_IRLS(f_min_change=1e-4, minGNiter=1) update_Jacobi = Directives.UpdatePreconditioner() self.inv = Inversion.BaseInversion( invProb, directiveList=[IRLS, betaest, update_Jacobi])
actvMap = Maps.InjectActiveCells(mesh, actv, 0) idenMap = Maps.IdentityMap(nP=int(np.sum(actv))) # reg = Regularization.Sparse(mesh, indActive=actv, mapping=idenMap) # reg.norms = np.c_[driver.lpnorms].T # if driver.eps is not None: # reg.eps_p = driver.eps[0] # reg.eps_q = driver.eps[1] # reg.cell_weights = wrGlobal # reg.mref = np.zeros(mesh.nC)[actv] nC = actv.sum() reg1 = Regularization.Sparse(mesh, indActive=actv, mapping=idenMap, gradientType='component') reg1.norms = np.c_[driver.lpnorms].T # reg1.alpha_x = 1#((Dx1.max() - Dx1.min())/2)**-2. # reg1.alpha_y = 3#((Dy1.max() - Dy1.min())/2)**-2. # reg1.alpha_z = 4 # reg1.alpha_y = 4 # reg1.eps_p = 1e-3 # reg1.eps_q = 1e-3 reg1.cell_weights = wrGlobal reg1.mref = np.zeros(mesh.nC)[actv] reg1.objfcts[1].regmesh._cellDiffxStencil = Dx1 reg1.objfcts[1].regmesh._aveCC2Fx = speye(nC) reg1.objfcts[2].regmesh._cellDiffyStencil = Dy1
def run(N=100, plotIt=True): np.random.seed(1) std_noise = 1e-2 mesh = Mesh.TensorMesh([N]) m0 = np.ones(mesh.nC) * 1e-4 mref = np.zeros(mesh.nC) nk = 20 jk = np.linspace(1., 60., nk) p = -0.25 q = 0.25 def g(k): return ( np.exp(p*jk[k]*mesh.vectorCCx) * np.cos(np.pi*q*jk[k]*mesh.vectorCCx) ) G = np.empty((nk, mesh.nC)) for i in range(nk): G[i, :] = g(i) mtrue = np.zeros(mesh.nC) mtrue[mesh.vectorCCx > 0.3] = 1. mtrue[mesh.vectorCCx > 0.45] = -0.5 mtrue[mesh.vectorCCx > 0.6] = 0 prob = Problem.LinearProblem(mesh, G=G) survey = Survey.LinearSurvey() survey.pair(prob) survey.dobs = prob.fields(mtrue) + std_noise * np.random.randn(nk) wd = np.ones(nk) * std_noise # Distance weighting wr = np.sum(prob.getJ(m0)**2., axis=0)**0.5 wr = wr/np.max(wr) dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = 1./wd betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1e0) # Creat reduced identity map idenMap = Maps.IdentityMap(nP=mesh.nC) reg = Regularization.Sparse(mesh, mapping=idenMap) reg.mref = mref reg.cell_weights = wr reg.norms = np.c_[0., 0., 2., 2.] reg.mref = np.zeros(mesh.nC) opt = Optimization.ProjectedGNCG( maxIter=100, lower=-2., upper=2., maxIterLS=20, maxIterCG=10, tolCG=1e-3 ) invProb = InvProblem.BaseInvProblem(dmis, reg, opt) update_Jacobi = Directives.UpdatePreconditioner() # Set the IRLS directive, penalize the lowest 25 percentile of model values # Start with an l2-l2, then switch to lp-norms IRLS = Directives.Update_IRLS( maxIRLSiter=40, minGNiter=1, f_min_change=1e-4) saveDict = Directives.SaveOutputEveryIteration(save_txt=False) inv = Inversion.BaseInversion( invProb, directiveList=[IRLS, betaest, update_Jacobi, saveDict] ) # Run inversion mrec = inv.run(m0) print("Final misfit:" + str(invProb.dmisfit(mrec))) if plotIt: fig, axes = plt.subplots(2, 2, figsize=(12*1.2, 8*1.2)) for i in range(prob.G.shape[0]): axes[0, 0].plot(prob.G[i, :]) axes[0, 0].set_title('Columns of matrix G') axes[0, 1].plot(mesh.vectorCCx, mtrue, 'b-') axes[0, 1].plot(mesh.vectorCCx, invProb.l2model, 'r-') # axes[0, 1].legend(('True Model', 'Recovered Model')) axes[0, 1].set_ylim(-1.0, 1.25) axes[0, 1].plot(mesh.vectorCCx, mrec, 'k-', lw=2) axes[0, 1].legend( ( 'True Model', 'Smooth l2-l2', 'Sparse norms: {0}'.format(*reg.norms) ), fontsize=12 ) axes[1, 1].plot(saveDict.phi_d, 'k', lw=2) twin = axes[1, 1].twinx() twin.plot(saveDict.phi_m, 'k--', lw=2) axes[1, 1].plot( np.r_[IRLS.iterStart, IRLS.iterStart], np.r_[0, np.max(saveDict.phi_d)], 'k:' ) axes[1, 1].text( IRLS.iterStart, 0., 'IRLS Start', va='bottom', ha='center', rotation='vertical', size=12, bbox={'facecolor': 'white'} ) axes[1, 1].set_ylabel('$\phi_d$', size=16, rotation=0) axes[1, 1].set_xlabel('Iterations', size=14) axes[1, 0].axis('off') twin.set_ylabel('$\phi_m$', size=16, rotation=0) return prob, survey, mesh, mrec
# Create sensitivity weights from our linear forward operator rxLoc = survey.srcField.rxList[0].locs # This Mapping connects the regularizations for the three-component # vector model wires = Maps.Wires(('p', nC), ('s', nC), ('t', nC)) # Create sensitivity weights from our linear forward operator # so that all cells get equal chance to contribute to the solution wr = np.sum(prob.G**2., axis=0)**0.5 wr = (wr / np.max(wr)) # Create three regularization for the different components # of magnetization reg_p = Regularization.Sparse(mesh, indActive=actv, mapping=wires.p) reg_p.mref = np.zeros(3 * nC) reg_p.cell_weights = (wires.p * wr) reg_s = Regularization.Sparse(mesh, indActive=actv, mapping=wires.s) reg_s.mref = np.zeros(3 * nC) reg_s.cell_weights = (wires.s * wr) reg_t = Regularization.Sparse(mesh, indActive=actv, mapping=wires.t) reg_t.mref = np.zeros(3 * nC) reg_t.cell_weights = (wires.t * wr) reg = reg_p + reg_s + reg_t reg.mref = np.zeros(3 * nC) # Data misfit function
def setUp(self): # We will assume a vertical inducing field H0 = (50000., 90., 0.) # The magnetization is set along a different direction (induced + remanence) M = np.array([90., 0.]) # Block with an effective susceptibility chi_e = 0.05 # 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. + (yy / b)**2.)) topo = np.c_[Utils.mkvc(xx), Utils.mkvc(yy), Utils.mkvc(zz)] # Create and array of observation points xr = np.linspace(-100., 100., 20) yr = np.linspace(-100., 100., 20) X, Y = np.meshgrid(xr, yr) Z = A * np.exp(-0.5 * ((X / b)**2. + (Y / b)**2.)) + 5 # Create a MAGsurvey rxLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)] Rx = PF.BaseMag.RxObs(rxLoc) srcField = PF.BaseMag.SrcField([Rx], param=H0) survey = PF.BaseMag.LinearSurvey(srcField) # Create a mesh h = [5, 5, 5] padDist = np.ones((3, 2)) * 100 nCpad = [4, 4, 2] # Get extent of points limx = np.r_[topo[:, 0].max(), topo[:, 0].min()] limy = np.r_[topo[:, 1].max(), topo[:, 1].min()] limz = np.r_[topo[:, 2].max(), topo[:, 2].min()] # Get center of the mesh midX = np.mean(limx) midY = np.mean(limy) midZ = np.mean(limz) nCx = int(limx[0] - limx[1]) / h[0] nCy = int(limy[0] - limy[1]) / h[1] nCz = int(limz[0] - limz[1] + int(np.min(np.r_[nCx, nCy]) / 3)) / h[2] # Figure out full extent required from input extent = np.max(np.r_[nCx * h[0] + padDist[0, :].sum(), nCy * h[1] + padDist[1, :].sum(), nCz * h[2] + padDist[2, :].sum()]) maxLevel = int(np.log2(extent / h[0])) + 1 # Number of cells at the small octree level # For now equal in 3D nCx, nCy, nCz = 2**(maxLevel), 2**(maxLevel), 2**(maxLevel) # Define the mesh and origin mesh = Mesh.TreeMesh( [np.ones(nCx) * h[0], np.ones(nCx) * h[1], np.ones(nCx) * h[2]]) # Set origin mesh.x0 = np.r_[-nCx * h[0] / 2. + midX, -nCy * h[1] / 2. + midY, -nCz * h[2] / 2. + midZ] # Refine the mesh around topography # Get extent of points F = NearestNDInterpolator(topo[:, :2], topo[:, 2]) zOffset = 0 # Cycle through the first 3 octree levels for ii in range(3): dx = mesh.hx.min() * 2**ii nCx = int((limx[0] - limx[1]) / dx) nCy = int((limy[0] - limy[1]) / dx) # Create a grid at the octree level in xy CCx, CCy = np.meshgrid(np.linspace(limx[1], limx[0], nCx), np.linspace(limy[1], limy[0], nCy)) z = F(mkvc(CCx), mkvc(CCy)) # level means number of layers in current OcTree level for level in range(int(nCpad[ii])): mesh.insert_cells(np.c_[mkvc(CCx), mkvc(CCy), z - zOffset], np.ones_like(z) * maxLevel - ii, finalize=False) zOffset += dx mesh.finalize() # Define an active cells from topo actv = Utils.surface2ind_topo(mesh, topo) nC = int(actv.sum()) # Convert the inclination declination to vector in Cartesian M_xyz = Utils.matutils.dip_azimuth2cartesian( np.ones(nC) * M[0], np.ones(nC) * M[1]) # Get the indicies of the magnetized block ind = Utils.ModelBuilder.getIndicesBlock( np.r_[-20, -20, -10], np.r_[20, 20, 25], mesh.gridCC, )[0] # Assign magnetization value, inducing field strength will # be applied in by the :class:`SimPEG.PF.Magnetics` problem model = np.zeros(mesh.nC) model[ind] = chi_e # Remove air cells self.model = model[actv] # Create active map to go from reduce set to full self.actvPlot = Maps.InjectActiveCells(mesh, actv, np.nan) # Creat reduced identity map idenMap = Maps.IdentityMap(nP=nC) # Create the forward model operator prob = PF.Magnetics.MagneticIntegral(mesh, M=M_xyz, chiMap=idenMap, actInd=actv) # Pair the survey and problem survey.pair(prob) # Compute some data and add some random noise data = prob.fields(self.model) # Split the data in components nD = rxLoc.shape[0] std = 5 # nT data += np.random.randn(nD) * std wd = np.ones(nD) * std # Assigne data and uncertainties to the survey survey.dobs = data survey.std = wd ###################################################################### # Equivalent Source # Get the active cells for equivalent source is the top only surf = Utils.modelutils.surface_layer_index(mesh, topo) # Get the layer of cells directyl below topo nC = np.count_nonzero(surf) # Number of active cells # Create active map to go from reduce set to full surfMap = Maps.InjectActiveCells(mesh, surf, np.nan) # Create identity map idenMap = Maps.IdentityMap(nP=nC) # Create static map prob = PF.Magnetics.MagneticIntegral(mesh, chiMap=idenMap, actInd=surf, parallelized=False, equiSourceLayer=True) prob.solverOpts['accuracyTol'] = 1e-4 # Pair the survey and problem if survey.ispaired: survey.unpair() survey.pair(prob) # Create a regularization function, in this case l2l2 reg = Regularization.Sparse(mesh, indActive=surf, mapping=Maps.IdentityMap(nP=nC), scaledIRLS=False) reg.mref = np.zeros(nC) # Specify how the optimization will proceed, # set susceptibility bounds to inf opt = Optimization.ProjectedGNCG(maxIter=20, lower=-np.inf, upper=np.inf, maxIterLS=20, maxIterCG=20, tolCG=1e-3) # Define misfit function (obs-calc) dmis = DataMisfit.l2_DataMisfit(survey) dmis.W = 1. / survey.std # Create the default L2 inverse problem from the above objects invProb = InvProblem.BaseInvProblem(dmis, reg, opt) # Specify how the initial beta is found betaest = Directives.BetaEstimate_ByEig() # Target misfit to stop the inversion, # try to fit as much as possible of the signal, # we don't want to lose anything IRLS = Directives.Update_IRLS(f_min_change=1e-3, minGNiter=1, beta_tol=1e-1) update_Jacobi = Directives.UpdatePreconditioner() # Put all the parts together inv = Inversion.BaseInversion( invProb, directiveList=[betaest, IRLS, update_Jacobi]) # Run the equivalent source inversion mstart = np.ones(nC) * 1e-4 mrec = inv.run(mstart) # Won't store the sensitivity and output 'xyz' data. prob.forwardOnly = True prob.rx_type = 'xyz' prob._G = None prob.modelType = 'amplitude' prob.model = mrec pred = prob.fields(mrec) bx = pred[:nD] by = pred[nD:2 * nD] bz = pred[2 * nD:] bAmp = (bx**2. + by**2. + bz**2.)**0.5 # AMPLITUDE INVERSION # Create active map to go from reduce space to full actvMap = Maps.InjectActiveCells(mesh, actv, -100) nC = int(actv.sum()) # Create identity map idenMap = Maps.IdentityMap(nP=nC) self.mstart = np.ones(nC) * 1e-4 # Create the forward model operator prob = PF.Magnetics.MagneticIntegral(mesh, chiMap=idenMap, actInd=actv, modelType='amplitude', rx_type='xyz') prob.model = self.mstart # Change the survey to xyz components surveyAmp = PF.BaseMag.LinearSurvey(survey.srcField) # Pair the survey and problem surveyAmp.pair(prob) # Create a regularization function, in this case l2l2 wr = np.sum(prob.G**2., axis=0)**0.5 wr = (wr / np.max(wr)) # Re-set the observations to |B| surveyAmp.dobs = bAmp surveyAmp.std = wd # Create a sparse regularization reg = Regularization.Sparse(mesh, indActive=actv, mapping=idenMap) reg.norms = np.c_[0, 0, 0, 0] reg.mref = np.zeros(nC) reg.cell_weights = wr # Data misfit function dmis = DataMisfit.l2_DataMisfit(surveyAmp) dmis.W = 1. / surveyAmp.std # Add directives to the inversion opt = Optimization.ProjectedGNCG(maxIter=30, lower=0., upper=1., maxIterLS=20, maxIterCG=20, tolCG=1e-3) invProb = InvProblem.BaseInvProblem(dmis, reg, opt) # Here is the list of directives betaest = Directives.BetaEstimate_ByEig() # Specify the sparse norms IRLS = Directives.Update_IRLS(f_min_change=1e-3, minGNiter=1, coolingRate=1, betaSearch=False) # The sensitivity weights are update between each iteration. update_SensWeight = Directives.UpdateSensitivityWeights() update_Jacobi = Directives.UpdatePreconditioner(threshold=1 - 3) # Put all together self.inv = Inversion.BaseInversion( invProb, directiveList=[betaest, IRLS, update_SensWeight, update_Jacobi]) self.mesh = mesh