def test_Problem2D_N(self):
problemDC = DC.Problem2D_N(self.mesh,
sigmaMap=Maps.IdentityMap(self.mesh))
problemDC.Solver = Solver
problemDC.pair(self.surveyDC)
data0 = self.surveyDC.dpred(self.sigma0)
datainf = self.surveyDC.dpred(self.sigmaInf)
problemIP = IP.Problem2D_N(
self.mesh,
sigma=self.sigmaInf,
etaMap=Maps.IdentityMap(self.mesh),
)
problemIP.Solver = Solver
surveyIP = IP.Survey(self.srcLists_ip)
problemIP.pair(surveyIP)
data_full = data0 - datainf
data = surveyIP.dpred(self.eta)
err = np.linalg.norm(
(data - data_full) / data_full)**2 / data_full.size
if err < 0.05:
passed = True
print(">> IP forward test for Problem2D_N is passed")
print(err)
else:
passed = False
print(">> IP forward test for Problem2D_N is failed")
self.assertTrue(passed)
def setUp(self):
cs = 12.5
hx = [(cs, 7, -1.3), (cs, 61), (cs, 7, 1.3)]
hy = [(cs, 7, -1.3), (cs, 20)]
mesh = Mesh.TensorMesh([hx, hy], x0="CN")
# x = np.linspace(-200, 200., 20)
x = np.linspace(-200, 200., 2)
M = Utils.ndgrid(x - 12.5, np.r_[0.])
N = Utils.ndgrid(x + 12.5, np.r_[0.])
A0loc = np.r_[-150, 0.]
A1loc = np.r_[-130, 0.]
B0loc = np.r_[-130, 0.]
B1loc = np.r_[-110, 0.]
rx = DC.Rx.Dipole_ky(M, N)
src0 = DC.Src.Dipole([rx], A0loc, B0loc)
src1 = DC.Src.Dipole([rx], A1loc, B1loc)
survey = IP.Survey([src0, src1])
sigma = np.ones(mesh.nC) * 1.
problem = IP.Problem2D_N(mesh,
rho=1. / sigma,
etaMap=Maps.IdentityMap(mesh),
verbose=False)
problem.pair(survey)
mSynth = np.ones(mesh.nC) * 0.1
survey.makeSyntheticData(mSynth)
# Now set up the problem to do some minimization
dmis = DataMisfit.l2_DataMisfit(survey)
reg = Regularization.Tikhonov(mesh)
opt = Optimization.InexactGaussNewton(maxIterLS=20,
maxIter=10,
tolF=1e-6,
tolX=1e-6,
tolG=1e-6,
maxIterCG=6)
invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta=1e4)
inv = Inversion.BaseInversion(invProb)
self.inv = inv
self.reg = reg
self.p = problem
self.mesh = mesh
self.m0 = mSynth
self.survey = survey
self.dmis = dmis