mirror of
https://github.com/wassname/simpeg.git
synced 2026-06-27 23:54:55 +08:00
Rowan Cockett
90 lines
1.5 KiB
Python
90 lines
1.5 KiB
Python
import numpy as np
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from SimPEG.mesh import TensorMesh
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from SimPEG.forward import Problem
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from SimPEG.regularization import Regularization
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from SimPEG.inverse import *
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import matplotlib.pyplot as plt
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class LinearProblem(Problem):
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"""docstring for LinearProblem"""
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def dpred(self, m, u=None):
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return self.G.dot(m)
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def J(self, m, v, u=None):
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return G.dot(v)
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def Jt(self, m, v, u=None):
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return G.T.dot(v)
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if __name__ == '__main__':
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N = 100
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h = np.ones(N)/N
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M = TensorMesh([h])
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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]*M.vectorCCx)*np.cos(2*np.pi*q*jk[k]*M.vectorCCx)
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G = np.empty((nk, M.nC))
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for i in range(nk):
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G[i,:] = g(i)
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plt.figure(1)
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for i in range(nk):
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plt.plot(G[i,:])
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m_true = np.zeros(M.nC)
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m_true[M.vectorCCx > 0.3] = 1.
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m_true[M.vectorCCx > 0.45] = -0.5
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m_true[M.vectorCCx > 0.6] = 0
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d_true = G.dot(m_true)
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noise = 0.1 * np.random.rand(d_true.size)
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d_obs = d_true + noise
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# plt.figure(3)
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# plt.plot(d_true,'-o')
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# plt.plot(d_obs,'r-o')
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prob = LinearProblem(M)
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prob.G = G
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prob.dobs = d_obs
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prob.std = np.ones_like(d_obs)*0.1
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reg = Regularization(M)
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opt = InexactGaussNewton(maxIter=20)
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inv = Inversion(prob,reg,opt,beta0=1e-4)
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m0 = np.zeros_like(m_true)
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mrec = inv.run(m0)
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plt.figure(2)
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plt.plot(M.vectorCCx, m_true, 'b-')
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plt.plot(M.vectorCCx, mrec, 'r-')
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plt.show()
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