stepOffBoundsFact=1e-8)
invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
# LIST OF DIRECTIVES
# betaest = Directives.BetaEstimate_ByEig()
IRLS = Directives.Update_IRLS(f_min_change=1e-6,
minGNiter=2, beta_tol=1e-2,
coolingRate=2)
update_SensWeight = Directives.UpdateSensWeighting()
update_Jacobi = Directives.UpdatePreCond()
ProjSpherical = Directives.ProjSpherical()
JointAmpMVI = Directives.JointAmpMVI()
betaest = Directives.BetaEstimate_ByEig(beta0_ratio = 1e+3)
saveModel = Directives.SaveUBCModelEveryIteration(mapping=actvMap,
saveComp=True)
saveModel.fileName = work_dir+out_dir + 'JOINT_MVIC_A'
inv = Inversion.BaseInversion(invProb,
directiveList=[betaest, IRLS, update_SensWeight,
update_Jacobi, saveModel])
# Run JOINT
mrec = inv.run(mstart)
#NOTE - Would like to have dpred working on both surveys
dpred = invProb.getFields(mrec)
print('Amplitude Final phi_d: ' + str(dmis_amp(mrec)))
print('MVI Final phi_d: ' + str(dmis_MVI(mrec)))
betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1e+1)
# Pre-conditioner
update_Jacobi = Directives.UpdatePreconditioner()
IRLS = Directives.Update_IRLS(f_min_change=1e-3,
minGNiter=1,
beta_tol=0.25,
maxIRLSiter=max_IRLS_iter,
chifact_target=target_chi,
betaSearch=False)
# Save model
saveDict = Directives.SaveOutputEveryIteration(save_txt=False)
saveIt = Directives.SaveUBCModelEveryIteration(
mapping=activeCellsMap,
fileName=outDir + input_dict["inversion_type"].lower() + "_C",
vector=input_dict["inversion_type"].lower()[0:3] == 'mvi')
# Put all the parts together
inv = Inversion.BaseInversion(
invProb, directiveList=[saveIt, saveDict, betaest, IRLS, update_Jacobi])
# SimPEG reports half phi_d, so we scale to matrch
print("Start Inversion\nTarget Misfit: %.2e (%.0f data with chifact = %g)" %
(0.5 * target_chi * len(survey.std), len(survey.std), target_chi))
# Run the inversion
mrec = inv.run(mstart)
print("Target Misfit: %.3e (%.0f data with chifact = %g)" %
(0.5 * target_chi * len(survey.std), len(survey.std), target_chi))
maxIRLSiter=max_irls_iterations,
minGNiter=1, beta_tol=0.5, prctile=90, floorEpsEnforced=True,
coolingRate=1, coolEps_q=True, coolEpsFact=1.2,
betaSearch=False
)
)
if initial_beta is None:
directiveList.append(Directives.BetaEstimate_ByEig(beta0_ratio=1e+1))
directiveList.append(Directives.UpdatePreconditioner())
directiveList.append(
Directives.SaveUBCModelEveryIteration(
mapping=activeCellsMap * model_map,
mesh=mesh,
fileName=outDir + input_dict["inversion_type"],
vector=input_dict["inversion_type"][0:3] == 'mvi'
)
)
directiveList.append(
Directives.SaveUBCPredictedEveryIteration(
survey=survey,
fileName=outDir + input_dict["inversion_type"],
format=input_dict["inversion_type"]
)
)
invProb_idx = len(directiveList) - 1
directiveList.append(
Directives.SaveOutputDictEveryIteration()
tolCG=1e-4)
invProb = InvProblem.BaseInvProblem(ComboMisfit, reg, opt)
betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1e0)
# 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,
minGNiter=1,
maxIRLSiter=30,
betaSearch=False)
IRLS.target = driver.survey.nD
update_Jacobi = Directives.UpdatePreconditioner()
saveModel = Directives.SaveUBCModelEveryIteration(mapping=actvMap,
fileName=out_dir + dsep +
"GRAV_Tile")
inv = Inversion.BaseInversion(
invProb, directiveList=[betaest, saveModel, IRLS, update_Jacobi])
# Run the inversion
mrec = inv.run(m0)
# Export tensor model if inputMesh is tensor
if isinstance(meshInput, Mesh.TensorMesh):
tree = cKDTree(mesh.gridCC)
dd, ind = tree.query(meshInput.gridCC)
mOut = (actvMap * invProb.model)[ind]
opt = Optimization.ProjectedGNCG(maxIter=10,
lower=-10.,
upper=10.,
maxIterCG=20,
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=3, beta_tol=1e-2)
update_Jacobi = Directives.UpdatePreCond()
targetMisfit = Directives.TargetMisfit()
saveModel = Directives.SaveUBCModelEveryIteration(mapping=actvMap)
saveModel.fileName = work_dir + out_dir + 'MVI_C'
inv = Inversion.BaseInversion(
invProb, directiveList=[betaest, IRLS, update_Jacobi, saveModel])
mrec_MVI = inv.run(mstart)
beta = invProb.beta
# %% RUN MVI-S
# # STEP 3: Finish inversion with spherical formulation
mstart = PF.Magnetics.xyz2atp(mrec_MVI)
prob.coordinate_system = 'spherical'
prob.model = mstart
# Target misfit to stop the inversion,
# try to fit as much as possible of the signal, we don't want to lose anything
targetMisfit = Directives.TargetMisfit(chifact=targetChi)
# Pre-conditioner
update_Jacobi = Directives.UpdatePreconditioner()
IRLS = Directives.Update_IRLS(f_min_change=1e-3,
minGNiter=1,
beta_tol=0.25,
maxIRLSiter=1,
chifact_target=targetChi)
# Save model
saveIt = Directives.SaveUBCModelEveryIteration(mapping=activeCellsMap,
fileName=workDir + dsep +
outDir + driver["dataFile"][0])
# Put all the parts together
inv = Inversion.BaseInversion(
invProb, directiveList=[saveIt, betaest, IRLS, update_Jacobi])
# Run the equivalent source inversion
mstart = np.zeros(nC)
mrec = inv.run(mstart)
# Ouput result
Mesh.TreeMesh.writeUBC(
mesh,
workDir + dsep + outDir + 'OctreeMeshGlobal.msh',
models={
workDir + dsep + outDir + driver["dataFile"][0] + '.mod':
# Target misfit to stop the inversion,
# try to fit as much as possible of the signal, we don't want to lose anything
targetMisfit = Directives.TargetMisfit(chifact=targetChi)
# Pre-conditioner
update_Jacobi = Directives.UpdatePreconditioner()
IRLS = Directives.Update_IRLS(f_min_change=1e-3,
minGNiter=1,
beta_tol=0.25,
maxIRLSiter=1,
chifact_target=targetChi)
# Save model
saveIt = Directives.SaveUBCModelEveryIteration(mapping=activeCellsMap,
fileName=workDir + dsep +
outDir + 'EquivalentSource')
# Put all the parts together
inv = Inversion.BaseInversion(
invProb, directiveList=[saveIt, betaest, IRLS, update_Jacobi])
# Run the equivalent source inversion
mstart = np.zeros(nC)
mrec = inv.run(mstart)
# Ouput result
Mesh.TreeMesh.writeUBC(mesh,
workDir + dsep + outDir + 'OctreeMeshGlobal.msh',
models={
workDir + dsep + outDir + 'EquivalentSource.mod':
activeCellsMap * invProb.model
invProb = InvProblem.BaseInvProblem(ComboMisfit, reg, opt)
betaest = Directives.BetaEstimate_ByEig()
# Add directives to the inversion
# Here is where the norms are applied
IRLS = Directives.Update_IRLS(f_min_change=1e-3,
maxIRLSiter=0,
minGNiter=1,
betaSearch=False)
# Pre-conditioner
update_Jacobi = Directives.UpdatePreconditioner()
# Output models between each iteration
saveModel = Directives.SaveUBCModelEveryIteration(mapping=actvMap, vector=True)
saveModel.fileName = out_dir + 'MVI_C'
# Put it all together
inv = Inversion.BaseInversion(
invProb, directiveList=[betaest, IRLS, update_Jacobi, saveModel])
# Invert
mrec_MVI = inv.run(mstart)
###############################################################################
# MVI-Spherical with sparsity
# ---------------------------
#
# Finish inversion with spherical formulation
#
maxIterCG=20,
tolCG=1e-4)
invProb = InvProblem.BaseInvProblem(ComboMisfit, 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-3,
minGNiter=1,
maxIRLSiter=10)
IRLS.target = driver.survey.nD
update_Jacobi = Directives.UpdatePreconditioner()
saveModel = Directives.SaveUBCModelEveryIteration(mapping=actvMap,
fileName=work_dir +
out_dir + "MAG_Tile")
inv = Inversion.BaseInversion(
invProb, directiveList=[betaest, saveModel, IRLS, update_Jacobi])
# Run the inversion
mrec = inv.run(m0)
#for ind, dmis in enumerate(ComboMisfit.objfcts):
#
# Mesh.TreeMesh.writeUBC(dmis.prob.mesh, work_dir + out_dir + "Tile" + str(ind) + ".msh",
# models={work_dir + out_dir + "Tile" + str(ind) + ".sus": dmis.prob.chiMap * mrec})
# Mesh.TensorMesh.writeUBC(prob.mesh,
# work_dir + out_dir + "Tile" + str(ind) + ".msh")
# Mesh.TensorMesh.writeModelUBC(prob.mesh,
# work_dir + out_dir + "Tile" + str(ind) + ".sus", prob.mapping() * mrec)
tolCG=1e-3)
# 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)
# Beta schedule for inversion
betaSchedule = Directives.BetaSchedule(coolingFactor=2., coolingRate=1)
# Target misfit to stop the inversion,
# try to fit as much as possible of the signal, we don't want to lose anything
targetMisfit = Directives.TargetMisfit(chifact=0.1)
saveModel = Directives.SaveUBCModelEveryIteration(mapping=surfMap)
saveModel.fileName = work_dir + out_dir + 'EquivalentSource'
# Put all the parts together
inv = Inversion.BaseInversion(
invProb, directiveList=[betaest, betaSchedule, saveModel, targetMisfit])
# Run the equivalent source inversion
mstart = np.zeros(nC)
mrec = inv.run(mstart)
# %% STEP 2: COMPUTE AMPLITUDE DATA
# Now that we have an equialent source layer, we can forward model alh three
# components of the field and add them up: |B| = ( Bx**2 + Bx**2 + Bx**2 )**0.5
# Won't store the sensitivity and output 'xyz' data.
