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wavefielddecomposition.py
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wavefielddecomposition.py
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r"""
12. Seismic wavefield decomposition
===================================
Multi-component seismic data can be decomposed
in their up- and down-going constituents. This task can be accurately achieved
by linearly combining the input pressure and particle velocity data
in the frequency-wavenumber described in details in
:func:`pylops.waveeqprocessing.UpDownComposition2D` and
:func:`pylops.waveeqprocessing.WavefieldDecomposition`.
In this tutorial we will consider a simple synthetic data composed of six
events (three up-going and three down-going). We will first combine them to
create pressure and particle velocity data and then show how we can retrieve
their directional constituents both by directly combining the input data
as well as by setting an inverse problem. The latter approach results vital in
case of spatial aliasing, as applying simple scaled summation in the
frequency-wavenumber would result in sub-optimal decomposition due to the
superposition of different frequency-wavenumber pairs at some (aliased)
locations.
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import filtfilt
import pylops
from pylops.utils.wavelets import ricker
from pylops.utils.seismicevents import makeaxis, hyperbolic2d
np.random.seed(0)
plt.close('all')
###############################################################################
# Let's first the input up- and down-going wavefields
par = {'ox':-220, 'dx':5, 'nx':89,
'ot':0, 'dt':0.004, 'nt':200,
'f0': 40}
t0_plus = np.array([0.2, 0.5, 0.7])
t0_minus = t0_plus + 0.04
vrms = np.array([1400., 1500., 2000.])
amp = np.array([1., -0.6, 0.5])
vel_sep = 1000.0 # velocity at separation level
rho_sep = 1000.0 # density at separation level
# Create axis
t, t2, x, y = makeaxis(par)
# Create wavelet
wav = ricker(t[:41], f0=par['f0'])[0]
# Create data
_, p_minus = hyperbolic2d(x, t, t0_minus, vrms, amp, wav)
_, p_plus = hyperbolic2d(x, t, t0_plus, vrms, amp, wav)
###############################################################################
# We can now combine them to create pressure and particle velocity data
critical = 1.1
ntaper = 51
nfft=2**10
# 2d fft operator
FFTop = pylops.signalprocessing.FFT2D(dims=[par['nx'], par['nt']],
nffts=[nfft, nfft],
sampling=[par['dx'], par['dt']])
#obliquity factor
[Kx, F] = np.meshgrid(FFTop.f1, FFTop.f2, indexing='ij')
k = F/vel_sep
Kz = np.sqrt((k**2-Kx**2).astype(np.complex))
Kz[np.isnan(Kz)] = 0
OBL=rho_sep*(np.abs(F)/Kz)
OBL[Kz==0]=0
mask = np.abs(Kx)<critical*np.abs(F)/vel_sep
OBL *= mask
OBL = filtfilt(np.ones(ntaper)/float(ntaper), 1, OBL, axis=0)
OBL = filtfilt(np.ones(ntaper)/float(ntaper), 1, OBL, axis=1)
# composition operator
UPop = \
pylops.waveeqprocessing.UpDownComposition2D(par['nt'], par['nx'],
par['dt'], par['dx'],
rho_sep, vel_sep,
nffts=(nfft, nfft),
critical=critical*100.,
ntaper=ntaper,
dtype='complex128')
# wavefield modelling
d = UPop * np.concatenate((p_plus.flatten(), p_minus.flatten())).flatten()
d = np.real(d.reshape(2*par['nx'], par['nt']))
p, vz = d[:par['nx']], d[par['nx']:]
# obliquity scaled vz
VZ = FFTop * vz.flatten()
VZ = VZ.reshape(nfft, nfft)
VZ_obl = OBL * VZ
vz_obl = FFTop.H*VZ_obl.flatten()
vz_obl = np.real(vz_obl.reshape(par['nx'], par['nt']))
fig, axs = plt.subplots(1, 4, figsize=(10, 5))
axs[0].imshow(p.T, aspect='auto',
interpolation='nearest', cmap='gray',
extent=(x.min(),x.max(),t2.max(),t2.min()),
vmin=-1, vmax=1)
axs[0].set_title(r'$p$', fontsize=15)
axs[0].set_xlabel('x')
axs[0].set_ylabel('t')
axs[1].imshow(vz_obl.T, aspect='auto',
interpolation='nearest', cmap='gray',
extent=(x.min(), x.max(), t2.max(), t2.min()),
vmin=-1, vmax=1)
axs[1].set_title(r'$v_z^{obl}$', fontsize=15)
axs[1].set_xlabel('x')
axs[1].set_ylabel('t')
axs[2].imshow(p_plus.T, aspect='auto',
interpolation='nearest', cmap='gray',
extent=(x.min(), x.max(), t2.max(), t2.min()),
vmin=-1, vmax=1)
axs[2].set_title(r'$p^+$', fontsize=15)
axs[2].set_xlabel('x')
axs[2].set_ylabel('t')
axs[3].imshow(p_minus.T, aspect='auto',
interpolation='nearest', cmap='gray',
extent=(x.min(), x.max(), t2.max(), t2.min()),
vmin=-1, vmax=1)
axs[3].set_title(r'$p^-$', fontsize=15)
axs[3].set_xlabel('x')
axs[3].set_ylabel('t')
plt.tight_layout()
###############################################################################
# Wavefield separation is first performed using the analytical expression
# for combining pressure and particle velocity data in the wavenumber-frequency
# domain
pup_sep, pdown_sep = \
pylops.waveeqprocessing.WavefieldDecomposition(p, vz, par['nt'], par['nx'],
par['dt'], par['dx'],
rho_sep, vel_sep,
nffts=(nfft, nfft),
kind='analytical',
critical=critical*100,
ntaper=ntaper,
dtype='complex128')
fig = plt.figure(figsize=(12, 5))
axs0 = plt.subplot2grid((2, 5), (0, 0), rowspan=2)
axs1 = plt.subplot2grid((2, 5), (0, 1), rowspan=2)
axs2 = plt.subplot2grid((2, 5), (0, 2), colspan=3)
axs3 = plt.subplot2grid((2, 5), (1, 2), colspan=3)
axs0.imshow(pup_sep.T, cmap='gray',
extent=(x.min(),x.max(),t2.max(),t2.min()),
vmin=-1, vmax=1)
axs0.set_title(r'$p^-$ analytical')
axs0.axis('tight')
axs1.imshow(pdown_sep.T, cmap='gray',
extent=(x.min(),x.max(),t2.max(),t2.min()),
vmin=-1, vmax=1)
axs1.set_title(r'$p^+$ analytical')
axs1.axis('tight')
axs2.plot(t, p[par['nx']//2], 'r', lw=2, label=r'$p$')
axs2.plot(t, vz_obl[par['nx']//2], '--b', lw=2, label=r'$v_z^{obl}$')
axs2.set_ylim(-1, 1)
axs2.set_title('Data at x=%.2f' % x[par['nx']//2])
axs2.set_xlabel('t [s]')
axs2.legend()
axs3.plot(t, pup_sep[par['nx']//2], 'r', lw=2, label=r'$p^-$ ana')
axs3.plot(t, pdown_sep[par['nx']//2], '--b', lw=2, label=r'$p^+$ ana')
axs3.set_title('Separated wavefields at x=%.2f' % x[par['nx']//2])
axs3.set_xlabel('t [s]')
axs3.set_ylim(-1, 1)
axs3.legend()
plt.tight_layout()
###############################################################################
# We repeat the same exercise but this time we invert the composition operator
# :func:`pylops.waveeqprocessing.UpDownComposition2D`
pup_inv, pdown_inv = \
pylops.waveeqprocessing.WavefieldDecomposition(p, vz, par['nt'], par['nx'],
par['dt'], par['dx'],
rho_sep, vel_sep,
nffts=(nfft, nfft),
kind='inverse',
critical=critical*100,
ntaper=ntaper,
dtype='complex128',
**dict(damp=1e-10,
iter_lim=20))
fig = plt.figure(figsize=(12, 5))
axs0 = plt.subplot2grid((2, 5), (0, 0), rowspan=2)
axs1 = plt.subplot2grid((2, 5), (0, 1), rowspan=2)
axs2 = plt.subplot2grid((2, 5), (0, 2), colspan=3)
axs3 = plt.subplot2grid((2, 5), (1, 2), colspan=3)
axs0.imshow(pup_inv.T, cmap='gray',
extent=(x.min(),x.max(),t2.max(),t2.min()),
vmin=-1, vmax=1)
axs0.set_title(r'$p^-$ inverse')
axs0.axis('tight')
axs1.imshow(pdown_inv.T, cmap='gray',
extent=(x.min(),x.max(),t2.max(),t2.min()),
vmin=-1, vmax=1)
axs1.set_title(r'$p^+$ inverse')
axs1.axis('tight')
axs2.plot(t, p[par['nx']//2], 'r', lw=2, label=r'$p$')
axs2.plot(t, vz_obl[par['nx']//2], '--b', lw=2, label=r'$v_z^{obl}$')
axs2.set_ylim(-1, 1)
axs2.set_title('Data at x=%.2f' % x[par['nx']//2])
axs2.set_xlabel('t [s]')
axs2.legend()
axs3.plot(t, pup_inv[par['nx']//2], 'r', lw=2, label=r'$p^-$ inv')
axs3.plot(t, pdown_inv[par['nx']//2], '--b', lw=2, label=r'$p^+$ inv')
axs3.set_title('Separated wavefields at x=%.2f' % x[par['nx']//2])
axs3.set_xlabel('t [s]')
axs3.set_ylim(-1, 1)
axs3.legend()
plt.tight_layout()
###############################################################################
# The up- and down-going constituents have been succesfully separated in both
# cases. To see more examples, including applying wavefield separation and
# regularization simultaneously, head over to these companion notebooks:
# `notebook1 <https://github.com/mrava87/pylops_notebooks/blob/master/developement/WavefieldSeparation.ipynb>`_
# and `notebook2 <https://github.com/mrava87/pylops_notebooks/blob/master/developement/WavefieldSeparation-Synthetic.ipynb>`_