mirror of
https://github.com/wassname/simpeg.git
synced 2026-06-28 02:02:39 +08:00
rowanc1
75 lines
1.6 KiB
Python
75 lines
1.6 KiB
Python
from SimPEG import *
|
|
import matplotlib.pyplot as plt
|
|
|
|
|
|
class LinearProblem(Problem.BaseProblem):
|
|
"""docstring for LinearProblem"""
|
|
|
|
def __init__(self, mesh, model, G, **kwargs):
|
|
Problem.BaseProblem.__init__(self, mesh, model, **kwargs)
|
|
self.G = G
|
|
|
|
def fields(self, m, u=None):
|
|
return self.G.dot(m)
|
|
|
|
def Jvec(self, m, v, u=None):
|
|
return self.G.dot(v)
|
|
|
|
def Jtvec(self, m, v, u=None):
|
|
return self.G.T.dot(v)
|
|
|
|
|
|
def example(N):
|
|
h = np.ones(N)/N
|
|
M = Mesh.TensorMesh([h])
|
|
|
|
nk = 20
|
|
jk = np.linspace(1.,20.,nk)
|
|
p = -0.25
|
|
q = 0.25
|
|
|
|
g = lambda k: np.exp(p*jk[k]*M.vectorCCx)*np.cos(2*np.pi*q*jk[k]*M.vectorCCx)
|
|
|
|
G = np.empty((nk, M.nC))
|
|
|
|
for i in range(nk):
|
|
G[i,:] = g(i)
|
|
|
|
mtrue = np.zeros(M.nC)
|
|
mtrue[M.vectorCCx > 0.3] = 1.
|
|
mtrue[M.vectorCCx > 0.45] = -0.5
|
|
mtrue[M.vectorCCx > 0.6] = 0
|
|
|
|
|
|
|
|
model = Model.BaseModel(M)
|
|
prob = LinearProblem(M, model, G)
|
|
data = prob.createSyntheticData(mtrue, std=0.01)
|
|
|
|
return prob, data, model
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
prob, data, model = example(100)
|
|
M = prob.mesh
|
|
|
|
reg = Regularization.Tikhonov(model)
|
|
objFunc = ObjFunction.BaseObjFunction(data, reg)
|
|
opt = Optimization.InexactGaussNewton(maxIter=20)
|
|
inv = Inversion.BaseInversion(objFunc, opt)
|
|
m0 = np.zeros_like(data.mtrue)
|
|
|
|
mrec = inv.run(m0)
|
|
|
|
plt.figure(1)
|
|
for i in range(prob.G.shape[0]):
|
|
plt.plot(prob.G[i,:])
|
|
|
|
plt.figure(2)
|
|
|
|
plt.plot(M.vectorCCx, data.mtrue, 'b-')
|
|
plt.plot(M.vectorCCx, mrec, 'r-')
|
|
|
|
plt.show()
|