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https://github.com/wassname/simpeg.git
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D Fournier
103 lines
2.6 KiB
Python
103 lines
2.6 KiB
Python
from SimPEG import *
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def run(N=100, plotIt=True):
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"""
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Inversion: Linear Problem
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=========================
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Here we go over the basics of creating a linear problem and inversion.
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"""
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np.random.seed(1)
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std_noise = 1e-2
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mesh = Mesh.TensorMesh([N])
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m0 = np.ones(mesh.nC) * 1e-4
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mref = np.zeros(mesh.nC)
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nk = 10
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jk = np.linspace(1.,nk,nk)
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p = -2.
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q = 1.
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g = lambda k: np.exp(p*jk[k]*mesh.vectorCCx)*np.cos(np.pi*q*jk[k]*mesh.vectorCCx)
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G = np.empty((nk, mesh.nC))
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for i in range(nk):
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G[i,:] = g(i)
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mtrue = np.zeros(mesh.nC)
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mtrue[mesh.vectorCCx > 0.3] = 1.
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mtrue[mesh.vectorCCx > 0.45] = -0.5
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mtrue[mesh.vectorCCx > 0.6] = 0
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prob = Problem.LinearProblem(mesh, G)
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survey = Survey.LinearSurvey()
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survey.pair(prob)
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survey.dobs = prob.fields(mtrue) + std_noise * np.random.randn(nk)
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wd = np.ones(nk) * std_noise
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# Distance weighting
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wr = np.sum(prob.G**2.,axis=0)**0.5
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wr = ( wr/np.max(wr) )
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dmis = DataMisfit.l2_DataMisfit(survey)
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dmis.Wd = 1./wd
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betaest = Directives.BetaEstimate_ByEig()
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reg = Regularization.Sparse(mesh)
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reg.mref = mref
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reg.cell_weights = wr
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reg.mref = np.zeros(mesh.nC)
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opt = Optimization.ProjectedGNCG(maxIter=100 ,lower=-2.,upper=2., maxIterLS = 20, maxIterCG= 10, tolCG = 1e-3)
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invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
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update_Jacobi = Directives.Update_lin_PreCond()
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# Set the IRLS directive, penalize the lowest 25 percentile of model values
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# Start with an l2-l2, then switch to lp-norms
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norms = [0., 0., 2., 2.]
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IRLS = Directives.Update_IRLS( norms=norms, prctile = 25, maxIRLSiter = 15, minGNiter=3)
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inv = Inversion.BaseInversion(invProb, directiveList=[IRLS,betaest,update_Jacobi])
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# Run inversion
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mrec = inv.run(m0)
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print "Final misfit:" + str(invProb.dmisfit.eval(mrec))
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if plotIt:
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import matplotlib.pyplot as plt
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fig, axes = plt.subplots(1,2,figsize=(12*1.2,4*1.2))
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for i in range(prob.G.shape[0]):
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axes[0].plot(prob.G[i,:])
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axes[0].set_title('Columns of matrix G')
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axes[1].plot(mesh.vectorCCx, mtrue, 'b-')
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axes[1].plot(mesh.vectorCCx, reg.l2model, 'r-')
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#axes[1].legend(('True Model', 'Recovered Model'))
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axes[1].set_ylim(-1.0,1.25)
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axes[1].plot(mesh.vectorCCx, mrec, 'k-',lw = 2)
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axes[1].legend(('True Model', 'Smooth l2-l2',
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'Sparse lp:' + str(reg.norms[0]) + ', lqx:' + str(reg.norms[1]) ), fontsize = 12)
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plt.show()
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return prob, survey, mesh, mrec
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if __name__ == '__main__':
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run()
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