def test_Marchenko(par):
"""Dot-test and comparison with pylops for Marchenko.apply_onepoint
"""
dMarchenkoWM = dMarchenko(dRtwosided_fft,
nt=nt,
dt=dt,
dr=dr,
wav=wav,
toff=toff,
nsmooth=nsmooth,
saveRt=par['saveRt'])
MarchenkoWM = Marchenko(Rtwosided_fft,
nt=nt,
dt=dt,
dr=dr,
wav=wav,
toff=toff,
nsmooth=nsmooth)
_, _, dp0_minus, dg_inv_minus, dg_inv_plus = \
dMarchenkoWM.apply_onepoint(trav, nfft=2 ** 11, rtm=True, greens=True,
dottest=True, **dict(niter=10,
compute=False))
_, _, p0_minus = \
MarchenkoWM.apply_onepoint(trav, nfft=2 ** 11, rtm=True, greens=False,
dottest=False, **dict(iter_lim=0, show=0))
assert_array_almost_equal(dp0_minus, p0_minus, decimal=5)
dginvsub = (dg_inv_minus + dg_inv_plus)[:, nt - 1:].T
dginvsub_norm = dginvsub / dginvsub.max()
gsub_norm = gsub / gsub.max()
assert np.linalg.norm(gsub_norm - dginvsub_norm) / \
np.linalg.norm(gsub_norm) < 1e-1
def test_Marchenko_time(par):
"""Solve marchenko equations using input Rs in time domain
"""
MarchenkoWM = Marchenko(R, dt=dt, dr=dr, nfmax=nfmax, wav=wav,
toff=toff, nsmooth=nsmooth)
_, _, _, g_inv_minus, g_inv_plus = \
MarchenkoWM.apply_onepoint(trav, G0=g0sub.T, rtm=True, greens=True,
dottest=True, **dict(iter_lim=par['niter'],
show=0))
ginvsub = (g_inv_minus + g_inv_plus)[:, nt - 1:].T
ginvsub_norm = ginvsub / ginvsub.max()
gsub_norm = gsub / gsub.max()
assert np.linalg.norm(gsub_norm - ginvsub_norm) / \
np.linalg.norm(gsub_norm) < 1e-1
def test_Marchenko_freq(par):
"""Solve marchenko equations using input Rs in frequency domain
"""
if par['prescaled']:
Rtwosided_fft_sc = np.sqrt(2*nt - 1) * dt * dr * Rtwosided_fft
R1twosided_fft_sc = np.sqrt(2*nt - 1) * dt * dr * R1twosided_fft
else:
Rtwosided_fft_sc = Rtwosided_fft
R1twosided_fft_sc = R1twosided_fft
MarchenkoWM = Marchenko(Rtwosided_fft_sc, R1=R1twosided_fft_sc,
nt=nt, dt=dt, dr=dr, nfmax=nfmax, wav=wav,
toff=toff, nsmooth=nsmooth,
prescaled=par['prescaled'])
_, _, _, g_inv_minus, g_inv_plus = \
MarchenkoWM.apply_onepoint(trav, G0=g0sub.T, rtm=True, greens=True,
dottest=True, **dict(iter_lim=par['niter'],
show=0))
ginvsub = (g_inv_minus + g_inv_plus)[:, nt-1:].T
ginvsub_norm = ginvsub / ginvsub.max()
gsub_norm = gsub / gsub.max()
assert np.linalg.norm(gsub_norm-ginvsub_norm) / \
np.linalg.norm(gsub_norm) < 1e-1