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Merge pull request #332 from simpeg/ref/regularization

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Automate the epsilon picking based on percentile of model values for …
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dfournier
2016-06-29 08:39:29 -07:00
committed by GitHub
parent 3deca9ed77 ef12a3674a
commit 394dc9106a
3 changed files with 36 additions and 43 deletions
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SimPEG/Examples/Inversion_IRLS.py
+7 -29
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@@ -42,55 +42,33 @@ def run(N=100, plotIt=True):
survey = Survey.LinearSurvey()
survey.pair(prob)
survey.dobs = prob.fields(mtrue) + std_noise * np.random.randn(nk)
#survey.makeSyntheticData(mtrue, std=std_noise)
wd = np.ones(nk) * std_noise
#print survey.std[0]
#M = prob.mesh
# Distance weighting
wr = np.sum(prob.G**2.,axis=0)**0.5
wr = ( wr/np.max(wr) )
# reg = Regularization.Simple(mesh)
# reg.mref = mref
# reg.cell_weights = wr
#
dmis = DataMisfit.l2_DataMisfit(survey)
dmis.Wd = 1./wd
#
# opt = Optimization.ProjectedGNCG(maxIter=20,lower=-2.,upper=2., maxIterCG= 10, tolCG = 1e-4)
# invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
# invProb.curModel = m0
#
# beta = Directives.BetaSchedule(coolingFactor=2, coolingRate=1)
# target = Directives.TargetMisfit()
#
betaest = Directives.BetaEstimate_ByEig()
# inv = Inversion.BaseInversion(invProb, directiveList=[beta, betaest, target])
#
#
# mrec = inv.run(m0)
# ml2 = mrec
# print "Final misfit:" + str(invProb.dmisfit.eval(mrec))
#
# # Switch regularization to sparse
# phim = invProb.phi_m_last
# phid = invProb.phi_d
reg = Regularization.Sparse(mesh)
reg.mref = mref
reg.cell_weights = wr
reg.mref = np.zeros(mesh.nC)
eps_p = 5e-2
eps_q = 5e-2
norms = [0., 0., 2., 2.]
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()
IRLS = Directives.Update_IRLS( norms=norms, eps_p=eps_p, eps_q=eps_q)
# 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])