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rowanc1
79 lines
1.9 KiB
Python
79 lines
1.9 KiB
Python
from SimPEG import *
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class LinearSurvey(Survey.BaseSurvey):
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def projectFields(self, u):
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return u
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class LinearProblem(Problem.BaseProblem):
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"""docstring for LinearProblem"""
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surveyPair = LinearSurvey
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def __init__(self, model, G, **kwargs):
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Problem.BaseProblem.__init__(self, model, **kwargs)
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self.G = G
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def fields(self, m, u=None):
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return self.G.dot(m)
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def Jvec(self, m, v, u=None):
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return self.G.dot(v)
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def Jtvec(self, m, v, u=None):
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return self.G.T.dot(v)
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def run(N, plotIt=True):
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mesh = Mesh.TensorMesh([N])
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nk = 20
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jk = np.linspace(1.,20.,nk)
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p = -0.25
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q = 0.25
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g = lambda k: np.exp(p*jk[k]*mesh.vectorCCx)*np.cos(2*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 = LinearProblem(mesh, G)
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survey = LinearSurvey()
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survey.pair(prob)
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survey.makeSyntheticData(mtrue, std=0.01)
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M = prob.mesh
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reg = Regularization.Tikhonov(mesh)
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dmis = DataMisfit.l2_DataMisfit(survey)
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opt = Optimization.InexactGaussNewton(maxIter=20)
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invProb = InvProblem.BaseInvProblem(dmis, reg, opt)
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beta = Directives.BetaSchedule()
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betaest = Directives.BetaEstimate_ByEig()
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inv = Inversion.BaseInversion(invProb, directiveList=[beta, betaest])
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m0 = np.zeros_like(survey.mtrue)
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mrec = inv.run(m0)
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if plotIt:
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import matplotlib.pyplot as plt
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plt.figure(1)
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for i in range(prob.G.shape[0]):
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plt.plot(prob.G[i,:])
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plt.figure(2)
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plt.plot(M.vectorCCx, survey.mtrue, 'b-')
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plt.plot(M.vectorCCx, mrec, 'r-')
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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(100)
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