def test_inv(self):
reg = regularization.Tikhonov(self.mesh)
opt = optimization.InexactGaussNewton(maxIter=10, use_WolfeCurvature=True)
invProb = inverse_problem.BaseInvProblem(self.dmiscombo, reg, opt)
directives_list = [
directives.ScalingMultipleDataMisfits_ByEig(verbose=True),
directives.AlphasSmoothEstimate_ByEig(verbose=True),
directives.BetaEstimate_ByEig(beta0_ratio=1e-2),
directives.BetaSchedule(),
]
inv = inversion.BaseInversion(invProb, directiveList=directives_list)
m0 = self.model.mean() * np.ones_like(self.model)
mrec = inv.run(m0)
def test_inv_mref_setting(self):
reg1 = regularization.Tikhonov(self.mesh)
reg2 = regularization.Tikhonov(self.mesh)
reg = reg1 + reg2
opt = optimization.ProjectedGNCG(
maxIter=10, lower=-10, upper=10, maxIterLS=20, maxIterCG=50, tolCG=1e-4
)
invProb = inverse_problem.BaseInvProblem(self.dmiscombo, reg, opt)
directives_list = [
directives.ScalingMultipleDataMisfits_ByEig(chi0_ratio=[0.01, 1.0], verbose=True),
directives.AlphasSmoothEstimate_ByEig(verbose=True),
directives.BetaEstimate_ByEig(beta0_ratio=1e-2),
directives.BetaSchedule(),
]
inv = inversion.BaseInversion(invProb, directiveList=directives_list)
m0 = self.model.mean() * np.ones_like(self.model)
mrec = inv.run(m0)
self.assertTrue(np.all(reg1.mref == m0))
self.assertTrue(np.all(reg2.mref == m0))
alpha_z=1.0,
alpha_xx=0.0,
alpha_yy=0.0,
alpha_zz=0.0,
cell_weights_list=[wr_grav,
wr_mag], # weights each phys. prop. by each sensW
)
# Directives
# Add directives to the inversion
# ratio to use for each phys prop. smoothness in each direction:
# roughly the ratio of range of each phys. prop.
alpha0_ratio = np.r_[np.zeros(len(reg.objfcts[0].objfcts)),
1e-2 * np.ones(len(reg.objfcts[1].objfcts)),
1e-2 * 100.0 * np.ones(len(reg.objfcts[2].objfcts)), ]
Alphas = directives.AlphasSmoothEstimate_ByEig(alpha0_ratio=alpha0_ratio,
verbose=True)
# initialize beta and beta/alpha_s schedule
beta = directives.BetaEstimate_ByEig(beta0_ratio=1e-4)
betaIt = directives.PGI_BetaAlphaSchedule(
verbose=True,
coolingFactor=2.0,
tolerance=0.2,
progress=0.2,
)
# geophy. and petro. target misfits
targets = directives.MultiTargetMisfits(
verbose=True,
chiSmall=0.5, # ask for twice as much clustering (target value is /2)
)
# add learned mref in smooth once stable
MrefInSmooth = directives.PGI_AddMrefInSmooth(
upper=10,
)
invProb = inverse_problem.BaseInvProblem(dmis, reg_simple, opt)
# directives
scales = directives.ScalingMultipleDataMisfits_ByEig(chi0_ratio=np.r_[1.0,
1.0],
verbose=True,
n_pw_iter=10)
scaling_schedule = directives.JointScalingSchedule(verbose=True)
alpha0_ratio = np.r_[np.zeros(len(reg_simple.objfcts[0].objfcts)),
100.0 * np.ones(len(reg_simple.objfcts[1].objfcts)),
1.0 * np.ones(len(reg_simple.objfcts[2].objfcts)), ]
alphas = directives.AlphasSmoothEstimate_ByEig(alpha0_ratio=alpha0_ratio,
n_pw_iter=10,
verbose=True)
beta = directives.BetaEstimate_ByEig(beta0_ratio=1e-5, n_pw_iter=10)
betaIt = directives.PGI_BetaAlphaSchedule(
verbose=True,
coolingFactor=2.0,
progress=0.2,
)
targets = directives.MultiTargetMisfits(verbose=True)
petrodir = directives.PGI_UpdateParameters(update_gmm=False)
# Setup Inversion
inv = inversion.BaseInversion(
invProb,
directiveList=[
alphas, scales, beta, petrodir, targets, betaIt, scaling_schedule