# Wavelet estimation#

This example shows how to use the pylops.avo.prestack.PrestackWaveletModelling to estimate a wavelet from pre-stack seismic data. This problem can be written in mathematical form as:

$\mathbf{d}= \mathbf{G} \mathbf{w}$

where $$\mathbf{G}$$ is an operator that convolves an angle-variant reflectivity series with the wavelet $$\mathbf{w}$$ that we aim to retrieve.

import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import filtfilt

import pylops
from pylops.utils.wavelets import ricker

plt.close("all")
np.random.seed(0)


Let’s start by creating the input elastic property profiles and wavelet

nt0 = 501
dt0 = 0.004
ntheta = 21

t0 = np.arange(nt0) * dt0
thetamin, thetamax = 0, 40
theta = np.linspace(thetamin, thetamax, ntheta)

# Elastic property profiles
vp = (
2000
+ 5 * np.arange(nt0)
+ 2 * filtfilt(np.ones(5) / 5.0, 1, np.random.normal(0, 160, nt0))
)
vs = 600 + vp / 2 + 3 * filtfilt(np.ones(5) / 5.0, 1, np.random.normal(0, 100, nt0))
rho = 1000 + vp + filtfilt(np.ones(5) / 5.0, 1, np.random.normal(0, 120, nt0))
vp[201:] += 1500
vs[201:] += 500
rho[201:] += 100

# Wavelet
ntwav = 41
wavoff = 10
wav, twav, wavc = ricker(t0[: ntwav // 2 + 1], 20)
wav_phase = np.hstack((wav[wavoff:], np.zeros(wavoff)))

# vs/vp profile
vsvp = vs / vp

# Model
m = np.stack((np.log(vp), np.log(vs), np.log(rho)), axis=1)

fig, axs = plt.subplots(1, 3, figsize=(9, 7), sharey=True)
axs[0].plot(vp, t0, "k", lw=3)
axs[0].set(xlabel="[m/s]", ylabel=r"$t$ [s]", ylim=[t0[0], t0[-1]], title="Vp")
axs[0].grid()
axs[1].plot(vp / vs, t0, "k", lw=3)
axs[1].set(title="Vp/Vs")
axs[1].grid()
axs[2].plot(rho, t0, "k", lw=3)
axs[2].set(xlabel="[kg/m³]", title="Rho")
axs[2].invert_yaxis()
axs[2].grid()
plt.tight_layout()


We create now the operators to model a synthetic pre-stack seismic gather with a zero-phase as well as a mixed phase wavelet.

# Create operators
Wavesop = pylops.avo.prestack.PrestackWaveletModelling(
m, theta, nwav=ntwav, wavc=wavc, vsvp=vsvp, linearization="akirich"
)
Wavesop_phase = pylops.avo.prestack.PrestackWaveletModelling(
m, theta, nwav=ntwav, wavc=wavc, vsvp=vsvp, linearization="akirich"
)


Let’s apply those operators to the elastic model and create some synthetic data

d = (Wavesop * wav).reshape(ntheta, nt0).T
d_phase = (Wavesop_phase * wav_phase).reshape(ntheta, nt0).T

dn = d + np.random.normal(0, 3e-2, d.shape)

fig, axs = plt.subplots(1, 3, figsize=(13, 7), sharey=True)
axs[0].imshow(
d, cmap="gray", extent=(theta[0], theta[-1], t0[-1], t0[0]), vmin=-0.1, vmax=0.1
)
axs[0].axis("tight")
axs[0].set(xlabel=r"$\theta$ [°]", ylabel=r"$t$ [s]")
axs[0].set_title("Data with zero-phase wavelet", fontsize=10)
axs[1].imshow(
d_phase,
cmap="gray",
extent=(theta[0], theta[-1], t0[-1], t0[0]),
vmin=-0.1,
vmax=0.1,
)
axs[1].axis("tight")
axs[1].set_title("Data with non-zero-phase wavelet", fontsize=10)
axs[1].set_xlabel(r"$\theta$ [°]")
axs[2].imshow(
dn, cmap="gray", extent=(theta[0], theta[-1], t0[-1], t0[0]), vmin=-0.1, vmax=0.1
)
axs[2].axis("tight")
axs[2].set_title("Noisy Data with zero-phase wavelet", fontsize=10)
axs[2].set_xlabel(r"$\theta$ [°]")
plt.tight_layout()


We can invert the data. First we will consider noise-free data, subsequently we will add some noise and add a regularization terms in the inversion process to obtain a well-behaved wavelet also under noise conditions.

wav_est = Wavesop / d.T.ravel()
wav_phase_est = Wavesop_phase / d_phase.T.ravel()
wavn_est = Wavesop / dn.T.ravel()

# Create regularization operator
D2op = pylops.SecondDerivative(ntwav, dtype="float64")

# Invert for wavelet
(
wavn_reg_est,
istop,
itn,
r1norm,
r2norm,
) = pylops.optimization.leastsquares.regularized_inversion(
Wavesop,
dn.T.ravel(),
[D2op],
epsRs=[np.sqrt(0.1)],
**dict(damp=np.sqrt(1e-4), iter_lim=200, show=0)
)


As expected, the regularization helps to retrieve a smooth wavelet even under noisy conditions.

# sphinx_gallery_thumbnail_number = 3
fig, axs = plt.subplots(2, 1, sharex=True, figsize=(8, 6))
axs[0].plot(wav, "k", lw=6, label="True")
axs[0].plot(wav_est, "--r", lw=4, label="Estimated (noise-free)")
axs[0].plot(wavn_est, "--g", lw=4, label="Estimated (noisy)")
axs[0].plot(wavn_reg_est, "--m", lw=4, label="Estimated (noisy regularized)")
axs[0].set_title("Zero-phase wavelet")
axs[0].grid()
axs[0].legend(loc="upper right")
axs[0].axis("tight")
axs[1].plot(wav_phase, "k", lw=6, label="True")
axs[1].plot(wav_phase_est, "--r", lw=4, label="Estimated")
axs[1].set_title("Wavelet with phase")
axs[1].grid()
axs[1].legend(loc="upper right")
axs[1].axis("tight")
plt.tight_layout()


Finally we repeat the same exercise, but this time we use a preconditioner. Initially, our preconditioner is a pylops.Symmetrize operator to ensure that our estimated wavelet is zero-phase. After we chain the pylops.Symmetrize and the pylops.Smoothing1D operators to also guarantee a smooth wavelet.

# Create symmetrize operator
Sop = pylops.Symmetrize((ntwav + 1) // 2)

# Create smoothing operator
Smop = pylops.Smoothing1D(5, dims=((ntwav + 1) // 2,), dtype="float64")

# Invert for wavelet
wavn_prec_est = pylops.optimization.leastsquares.preconditioned_inversion(
Wavesop, dn.T.ravel(), Sop, **dict(damp=np.sqrt(1e-4), iter_lim=200, show=0)
)[0]

wavn_smooth_est = pylops.optimization.leastsquares.preconditioned_inversion(
Wavesop, dn.T.ravel(), Sop * Smop, **dict(damp=np.sqrt(1e-4), iter_lim=200, show=0)
)[0]

fig, ax = plt.subplots(1, 1, sharex=True, figsize=(8, 3))
ax.plot(wav, "k", lw=6, label="True")
ax.plot(wav_est, "--r", lw=4, label="Estimated (noise-free)")
ax.plot(wavn_prec_est, "--g", lw=4, label="Estimated (noisy symmetric)")
ax.plot(wavn_smooth_est, "--m", lw=4, label="Estimated (noisy smoothed)")
ax.set_title("Zero-phase wavelet")
ax.grid()
ax.legend(loc="upper right")
plt.tight_layout()


Total running time of the script: ( 0 minutes 2.282 seconds)

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