upper=10,
maxIterLS=20,
maxIterCG=30,
tolCG=1e-4)
#opt = Optimization.InexactGaussNewton(maxIter = 20)
invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt, beta=beta)
# Create an inversion object
beta = Directives.BetaSchedule(coolingFactor=2, coolingRate=2)
betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1e0)
# save = Directives.SaveOutputEveryIteration()
target = Directives.TargetMisfit()
update_IRLS = Directives.Update_IRLS(f_min_change=1e-3,
minGNiter=2,
maxIRLSiter=10)
updateWr = Directives.UpdateSensWeighting()
inv = Inversion.BaseInversion(invProb, directiveList=[update_IRLS, updateWr])
problem.counter = opt.counter = Utils.Counter()
opt.LSshorten = 0.5
opt.remember('xc')
mopt = inv.run(m0)
#
#%% Plot the model
if changeMref:
figName = 'DC_PDP_' + wrType + '_' + str(mref_val) + 'RefUpdate.png'
else:
figName = 'DC_PDP_' + wrType + '_' + str(mref_val) + 'Ref.png'
mout = np.log10(mapping * mopt)
lower=Lbound,
upper=Ubound,
maxIterLS=10,
maxIterCG=20, tolCG=1e-3,
LSalwaysPass=True,
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)
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=3,
coolingRate=1,
chifact_target=0.25,
maxIRLSiter=3)
# Special directive specific to the mag amplitude problem. The sensitivity
# weights are update between each iteration.
update_SensWeight = Directives.UpdateSensWeighting(everyIter=True)
update_Jacobi = Directives.UpdatePreCond(epsilon=1e-3)
saveModel = Directives.SaveUBCModelEveryIteration(mapping=actvMap)
saveModel.fileName = work_dir + out_dir + 'AmpInv'
# Put all together
inv = Inversion.BaseInversion(
invProb,
directiveList=[betaest, IRLS, update_SensWeight, update_Jacobi, saveModel])
# Invert
mrec = inv.run(mstart)
# Outputs
Mesh.TensorMesh.writeModelUBC(mesh, work_dir + out_dir + "AmpInv_l2l2.sus",
