def test_Simulation2DNodal(self):
problemDC = dc.Simulation2DNodal(self.mesh,
survey=self.surveyDC,
sigmaMap=maps.IdentityMap(self.mesh))
problemDC.solver = Solver
data0 = problemDC.dpred(self.sigma0)
datainf = problemDC.dpred(self.sigmaInf)
surveyIP = ip.Survey(self.source_lists_ip)
problemIP = ip.Simulation2DNodal(
self.mesh,
survey=surveyIP,
sigma=self.sigmaInf,
etaMap=maps.IdentityMap(self.mesh),
)
problemIP.solver = Solver
data_full = data0 - datainf
data = problemIP.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 Simulation2DNodal is passed")
print(err)
else:
passed = False
print(">> IP forward test for Simulation2DNodal is failed")
self.assertTrue(passed)
def test_Simulation2DNodal(self):
simDC = dc.Simulation2DNodal(
self.mesh,
sigmaMap=maps.IdentityMap(self.mesh),
solver=Solver,
survey=self.survey_dc,
)
data0 = simDC.dpred(self.sigma0)
datainf = simDC.dpred(self.sigmaInf)
data_full = (data0 - datainf) / datainf
simIP = ip.Simulation2DNodal(
self.mesh,
sigma=self.sigmaInf,
etaMap=maps.IdentityMap(self.mesh),
solver=Solver,
survey=self.survey_ip,
)
data = simIP.dpred(self.eta)
simIP_store = ip.Simulation2DNodal(
self.mesh,
sigma=self.sigmaInf,
etaMap=maps.IdentityMap(self.mesh),
solver=Solver,
survey=self.survey_ip,
storeJ=True,
)
data2 = simIP_store.dpred(self.eta)
np.testing.assert_allclose(data, data2)
np.testing.assert_allclose(simIP._scale, simIP._sign / datainf)
err = np.linalg.norm(
(data - data_full) / data_full)**2 / data_full.size
if err > 0.05:
import matplotlib.pyplot as plt
plt.plot(data_full)
plt.plot(data, "k.")
plt.show()
self.assertLess(err, 0.05)
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 = discretize.TensorMesh([hx, hy], x0="CN")
# x = np.linspace(-200, 200., 20)
x = np.linspace(-200, 200.0, 2)
M = utils.ndgrid(x - 12.5, np.r_[0.0])
N = utils.ndgrid(x + 12.5, np.r_[0.0])
A0loc = np.r_[-150, 0.0]
A1loc = np.r_[-130, 0.0]
B0loc = np.r_[-130, 0.0]
B1loc = np.r_[-110, 0.0]
rx = dc.Rx.Dipole(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) * 1.0
problem = ip.Simulation2DNodal(
mesh,
survey=survey,
rho=1.0 / sigma,
etaMap=maps.IdentityMap(mesh),
verbose=False,
)
mSynth = np.ones(mesh.nC) * 0.1
dobs = problem.make_synthetic_data(mSynth, add_noise=True)
# Now set up the problem to do some minimization
dmis = data_misfit.L2DataMisfit(data=dobs, simulation=problem)
reg = regularization.Tikhonov(mesh)
opt = optimization.InexactGaussNewton(maxIterLS=20,
maxIter=10,
tolF=1e-6,
tolX=1e-6,
tolG=1e-6,
maxIterCG=6)
invProb = inverse_problem.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