10. Marchenko redatuming by inversion

This example shows how to set-up and run the pylops.waveeqprocessing.Marchenko inversion using synthetic data.

# sphinx_gallery_thumbnail_number = 5
# pylint: disable=C0103
import warnings

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

from pylops.waveeqprocessing import Marchenko

warnings.filterwarnings("ignore")
plt.close("all")

Let’s start by defining some input parameters and loading the test data

# Input parameters
inputfile = "../testdata/marchenko/input.npz"

vel = 2400.0  # velocity
toff = 0.045  # direct arrival time shift
nsmooth = 10  # time window smoothing
nfmax = 1000  # max frequency for MDC (#samples)
niter = 10  # iterations

inputdata = np.load(inputfile)

# Receivers
r = inputdata["r"]
nr = r.shape[1]
dr = r[0, 1] - r[0, 0]

# Sources
s = inputdata["s"]
ns = s.shape[1]
ds = s[0, 1] - s[0, 0]

# Virtual points
vs = inputdata["vs"]

# Density model
rho = inputdata["rho"]
z, x = inputdata["z"], inputdata["x"]

# Reflection data (R[s, r, t]) and subsurface fields
R = inputdata["R"][:, :, :-100]
R = np.swapaxes(R, 0, 1)  # just because of how the data was saved

Gsub = inputdata["Gsub"][:-100]
G0sub = inputdata["G0sub"][:-100]
wav = inputdata["wav"]
wav_c = np.argmax(wav)

t = inputdata["t"][:-100]
ot, dt, nt = t[0], t[1] - t[0], len(t)

Gsub = np.apply_along_axis(convolve, 0, Gsub, wav, mode="full")
Gsub = Gsub[wav_c:][:nt]
G0sub = np.apply_along_axis(convolve, 0, G0sub, wav, mode="full")
G0sub = G0sub[wav_c:][:nt]

plt.figure(figsize=(10, 5))
plt.imshow(rho, cmap="gray", extent=(x[0], x[-1], z[-1], z[0]))
plt.scatter(s[0, 5::10], s[1, 5::10], marker="*", s=150, c="r", edgecolors="k")
plt.scatter(r[0, ::10], r[1, ::10], marker="v", s=150, c="b", edgecolors="k")
plt.scatter(vs[0], vs[1], marker=".", s=250, c="m", edgecolors="k")
plt.axis("tight")
plt.xlabel("x [m]")
plt.ylabel("y [m]")
plt.title("Model and Geometry")
plt.xlim(x[0], x[-1])

fig, axs = plt.subplots(1, 3, sharey=True, figsize=(12, 7))
axs[0].imshow(
    R[0].T, cmap="gray", vmin=-1e-2, vmax=1e-2, extent=(r[0, 0], r[0, -1], t[-1], t[0])
)
axs[0].set_title("R shot=0")
axs[0].set_xlabel(r"$x_R$")
axs[0].set_ylabel(r"$t$")
axs[0].axis("tight")
axs[0].set_ylim(1.5, 0)
axs[1].imshow(
    R[ns // 2].T,
    cmap="gray",
    vmin=-1e-2,
    vmax=1e-2,
    extent=(r[0, 0], r[0, -1], t[-1], t[0]),
)
axs[1].set_title("R shot=%d" % (ns // 2))
axs[1].set_xlabel(r"$x_R$")
axs[1].set_ylabel(r"$t$")
axs[1].axis("tight")
axs[1].set_ylim(1.5, 0)
axs[2].imshow(
    R[-1].T, cmap="gray", vmin=-1e-2, vmax=1e-2, extent=(r[0, 0], r[0, -1], t[-1], t[0])
)
axs[2].set_title("R shot=%d" % ns)
axs[2].set_xlabel(r"$x_R$")
axs[2].axis("tight")
axs[2].set_ylim(1.5, 0)
fig.tight_layout()

fig, axs = plt.subplots(1, 2, sharey=True, figsize=(8, 6))
axs[0].imshow(
    Gsub, cmap="gray", vmin=-1e6, vmax=1e6, extent=(r[0, 0], r[0, -1], t[-1], t[0])
)
axs[0].set_title("G")
axs[0].set_xlabel(r"$x_R$")
axs[0].set_ylabel(r"$t$")
axs[0].axis("tight")
axs[0].set_ylim(1.5, 0)
axs[1].imshow(
    G0sub, cmap="gray", vmin=-1e6, vmax=1e6, extent=(r[0, 0], r[0, -1], t[-1], t[0])
)
axs[1].set_title("G0")
axs[1].set_xlabel(r"$x_R$")
axs[1].set_ylabel(r"$t$")
axs[1].axis("tight")
axs[1].set_ylim(1.5, 0)
fig.tight_layout()
  • Model and Geometry
  • R shot=0, R shot=50, R shot=101
  • G, G0

Let’s now create an object of the pylops.waveeqprocessing.Marchenko class and apply redatuming for a single subsurface point vs.

# direct arrival window
trav = np.sqrt((vs[0] - r[0]) ** 2 + (vs[1] - r[1]) ** 2) / vel

MarchenkoWM = Marchenko(
    R, dt=dt, dr=dr, nfmax=nfmax, wav=wav, toff=toff, nsmooth=nsmooth
)

(
    f1_inv_minus,
    f1_inv_plus,
    p0_minus,
    g_inv_minus,
    g_inv_plus,
) = MarchenkoWM.apply_onepoint(
    trav,
    G0=G0sub.T,
    rtm=True,
    greens=True,
    dottest=True,
    **dict(iter_lim=niter, show=True)
)
g_inv_tot = g_inv_minus + g_inv_plus
Dot test passed, v^H(Opu)=405.16509639614344 - u^H(Op^Hv)=405.1650963961446
Dot test passed, v^H(Opu)=172.06507561051328 - u^H(Op^Hv)=172.06507561051316

LSQR            Least-squares solution of  Ax = b
The matrix A has 282598 rows and 282598 columns
damp = 0.00000000000000e+00   calc_var =        0
atol = 1.00e-06                 conlim = 1.00e+08
btol = 1.00e-06               iter_lim =       10

   Itn      x[0]       r1norm     r2norm   Compatible    LS      Norm A   Cond A
     0  0.00000e+00   3.134e+07  3.134e+07    1.0e+00  3.3e-08
     1  0.00000e+00   1.374e+07  1.374e+07    4.4e-01  9.3e-01   1.1e+00  1.0e+00
     2  0.00000e+00   7.770e+06  7.770e+06    2.5e-01  3.9e-01   1.8e+00  2.2e+00
     3  0.00000e+00   5.750e+06  5.750e+06    1.8e-01  3.3e-01   2.1e+00  3.4e+00
     4  0.00000e+00   3.930e+06  3.930e+06    1.3e-01  3.4e-01   2.5e+00  5.1e+00
     5  0.00000e+00   3.042e+06  3.042e+06    9.7e-02  2.6e-01   2.9e+00  6.8e+00
     6  0.00000e+00   2.423e+06  2.423e+06    7.7e-02  2.2e-01   3.3e+00  8.6e+00
     7  0.00000e+00   1.675e+06  1.675e+06    5.3e-02  2.5e-01   3.6e+00  1.1e+01
     8  0.00000e+00   1.248e+06  1.248e+06    4.0e-02  2.0e-01   3.9e+00  1.3e+01
     9  0.00000e+00   1.004e+06  1.004e+06    3.2e-02  1.5e-01   4.2e+00  1.4e+01
    10  0.00000e+00   7.762e+05  7.762e+05    2.5e-02  1.8e-01   4.4e+00  1.6e+01

LSQR finished
The iteration limit has been reached

istop =       7   r1norm = 7.8e+05   anorm = 4.4e+00   arnorm = 6.1e+05
itn   =      10   r2norm = 7.8e+05   acond = 1.6e+01   xnorm  = 3.6e+07

We can now compare the result of Marchenko redatuming via LSQR with standard redatuming

fig, axs = plt.subplots(1, 3, sharey=True, figsize=(12, 7))
axs[0].imshow(
    p0_minus.T,
    cmap="gray",
    vmin=-5e5,
    vmax=5e5,
    extent=(r[0, 0], r[0, -1], t[-1], -t[-1]),
)
axs[0].set_title(r"$p_0^-$")
axs[0].set_xlabel(r"$x_R$")
axs[0].set_ylabel(r"$t$")
axs[0].axis("tight")
axs[0].set_ylim(1.2, 0)
axs[1].imshow(
    g_inv_minus.T,
    cmap="gray",
    vmin=-5e5,
    vmax=5e5,
    extent=(r[0, 0], r[0, -1], t[-1], -t[-1]),
)
axs[1].set_title(r"$g^-$")
axs[1].set_xlabel(r"$x_R$")
axs[1].set_ylabel(r"$t$")
axs[1].axis("tight")
axs[1].set_ylim(1.2, 0)
axs[2].imshow(
    g_inv_plus.T,
    cmap="gray",
    vmin=-5e5,
    vmax=5e5,
    extent=(r[0, 0], r[0, -1], t[-1], -t[-1]),
)
axs[2].set_title(r"$g^+$")
axs[2].set_xlabel(r"$x_R$")
axs[2].set_ylabel(r"$t$")
axs[2].axis("tight")
axs[2].set_ylim(1.2, 0)
fig.tight_layout()

fig = plt.figure(figsize=(12, 7))
ax1 = plt.subplot2grid((1, 5), (0, 0), colspan=2)
ax2 = plt.subplot2grid((1, 5), (0, 2), colspan=2)
ax3 = plt.subplot2grid((1, 5), (0, 4))
ax1.imshow(
    Gsub, cmap="gray", vmin=-5e5, vmax=5e5, extent=(r[0, 0], r[0, -1], t[-1], t[0])
)
ax1.set_title(r"$G_{true}$")
axs[0].set_xlabel(r"$x_R$")
axs[0].set_ylabel(r"$t$")
ax1.axis("tight")
ax1.set_ylim(1.2, 0)
ax2.imshow(
    g_inv_tot.T,
    cmap="gray",
    vmin=-5e5,
    vmax=5e5,
    extent=(r[0, 0], r[0, -1], t[-1], -t[-1]),
)
ax2.set_title(r"$G_{est}$")
axs[1].set_xlabel(r"$x_R$")
axs[1].set_ylabel(r"$t$")
ax2.axis("tight")
ax2.set_ylim(1.2, 0)
ax3.plot(Gsub[:, nr // 2] / Gsub.max(), t, "r", lw=5)
ax3.plot(g_inv_tot[nr // 2, nt - 1 :] / g_inv_tot.max(), t, "k", lw=3)
ax3.set_ylim(1.2, 0)
fig.tight_layout()
  • $p_0^-$, $g^-$, $g^+$
  • $G_{true}$, $G_{est}$

Note that Marchenko redatuming can also be applied simultaneously to multiple subsurface points. Use pylops.waveeqprocessing.Marchenko.apply_multiplepoints instead of pylops.waveeqprocessing.Marchenko.apply_onepoint.

Total running time of the script: ( 0 minutes 7.610 seconds)

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