def test_PoststackLinearModelling1d(par):
"""Dot-test, comparison of dense vs lop implementation and
inversion for PoststackLinearModelling in 1d with stationary wavelet
"""
# Dense
PPop_dense = PoststackLinearModelling(wav, nt0=nt0, explicit=True)
assert dottest(PPop_dense, nt0, nt0, tol=1e-4)
# Linear operator
PPop = PoststackLinearModelling(wav, nt0=nt0, explicit=False)
assert dottest(PPop, nt0, nt0, tol=1e-4)
# Compare data
d = PPop * m.flatten()
d_dense = PPop_dense * m.T.flatten()
assert_array_almost_equal(d, d_dense, decimal=4)
# Inversion
for explicit in [True, False]:
if par['epsR'] is None:
dict_inv = {}
else:
dict_inv = dict(damp=0 if par['epsI'] is None else par['epsI'],
iter_lim=80)
minv = PoststackInversion(d,
wav,
m0=mback,
explicit=explicit,
epsR=par['epsR'],
epsI=par['epsI'],
simultaneous=par['simultaneous'],
**dict_inv)[0]
assert np.linalg.norm(m - minv) / np.linalg.norm(minv) < 1e-2
def test_PoststackLinearModelling2d(par):
"""Dot-test and inversion for PoststackLinearModelling in 2d
"""
PPop = PoststackLinearModelling(wav,
nt0=nz,
ndims=nx,
explicit=par['explicit'])
assert dottest(PPop, nz * nx, nz * nx, tol=1e-4)
# Data
d = (PPop * m2d.flatten()).reshape(nz, nx)
# Inversion
if par['explicit'] and not par['simultaneous'] and par['epsR'] is None:
dict_inv = {}
elif par['explicit'] and not par['simultaneous'] and par[
'epsR'] is not None:
dict_inv = dict(damp=0 if par['epsI'] is None else par['epsI'],
iter_lim=80)
else:
dict_inv = dict(damp=0 if par['epsI'] is None else par['epsI'],
iter_lim=80)
minv2d = PoststackInversion(d,
wav,
m0=mback2d,
explicit=par['explicit'],
epsR=par['epsR'],
epsI=par['epsI'],
simultaneous=par['simultaneous'],
**dict_inv)[0]
assert np.linalg.norm(m2d - minv2d) / np.linalg.norm(minv2d) < 2e-1
def test_PoststackLinearModelling1d(par):
"""Dot-test and inversion for PoststackLinearModelling in 1d
"""
PPop = PoststackLinearModelling(wav, nt0=nt0, explicit=par['explicit'])
print(PPop)
print(nt0)
assert dottest(PPop, nt0, nt0, tol=1e-4)
# Data
d = PPop * m
print(d.shape)
# Inversion
if par['epsR'] is None:
dict_inv = {}
else:
dict_inv = dict(damp=0 if par['epsI'] is None else par['epsI'],
iter_lim=80)
minv = PoststackInversion(d,
wav,
m0=mback,
explicit=par['explicit'],
epsR=par['epsR'],
epsI=par['epsI'],
simultaneous=par['simultaneous'],
**dict_inv)[0]
assert np.linalg.norm(m - minv) / np.linalg.norm(minv) < 1e-2
def test_PoststackLinearModelling2d(par):
"""Dot-test and inversion for PoststackLinearModelling in 2d"""
# Dense
PPop_dense = PoststackLinearModelling(wav,
nt0=nz,
spatdims=nx,
explicit=True)
assert dottest(PPop_dense, nz * nx, nz * nx, tol=1e-4)
# Linear operator
PPop = PoststackLinearModelling(wav, nt0=nz, spatdims=nx, explicit=False)
assert dottest(PPop, nz * nx, nz * nx, tol=1e-4)
# Compare data
d = (PPop * m2d.ravel()).reshape(nz, nx)
d_dense = (PPop_dense * m2d.ravel()).reshape(nz, nx)
assert_array_almost_equal(d, d_dense, decimal=4)
# Inversion
for explicit in [True, False]:
if explicit and not par["simultaneous"] and par["epsR"] is None:
dict_inv = {}
elif explicit and not par["simultaneous"] and par["epsR"] is not None:
dict_inv = dict(damp=0 if par["epsI"] is None else par["epsI"],
iter_lim=10)
else:
dict_inv = dict(damp=0 if par["epsI"] is None else par["epsI"],
iter_lim=10)
minv2d = PoststackInversion(d,
wav,
m0=mback2d,
explicit=explicit,
epsI=par["epsI"],
epsR=par["epsR"],
epsRL1=par["epsRL1"],
simultaneous=par["simultaneous"],
**dict_inv)[0]
assert np.linalg.norm(m2d - minv2d) / np.linalg.norm(m2d) < 1e-1