This example shows how to use the pylops.signalprocessing.ChirpRadon2D and pylops.signalprocessing.ChirpRadon3D operators to apply the linear Radon Transform to 2-dimensional or 3-dimensional signals, respectively.

When working with the linear Radon transform, this is a faster implementation compared to in pylops.signalprocessing.Radon2D and pylops.signalprocessing.Radon3D and should be preferred. This method provides also an analytical inverse.

Note that the forward and adjoint definitions in these two pairs of operators are swapped.

import matplotlib.pyplot as plt
import numpy as np

import pylops

plt.close("all")


Let’s start by creating a empty 2d matrix of size $$n_x \times n_t$$ with a single linear event.

par = {
"ot": 0,
"dt": 0.004,
"nt": 51,
"ox": -250,
"dx": 10,
"nx": 51,
"oy": -250,
"dy": 10,
"ny": 51,
"f0": 40,
}
theta = [0.0]
t0 = [0.1]
amp = [1.0]

# Create axes
t, t2, x, y = pylops.utils.seismicevents.makeaxis(par)
dt, dx, dy = par["dt"], par["dx"], par["dy"]

# Create wavelet
wav, _, wav_c = pylops.utils.wavelets.ricker(t[:41], f0=par["f0"])

# Generate data
_, d = pylops.utils.seismicevents.linear2d(x, t, 1500.0, t0, theta, amp, wav)


We can now define our operators and apply the forward, adjoint and inverse steps.

npx, pxmax = par["nx"], 5e-4
px = np.linspace(-pxmax, pxmax, npx)

R2Op = pylops.signalprocessing.ChirpRadon2D(t, x, pxmax * dx / dt, dtype="float64")
dL_chirp = R2Op * d
dinv_chirp = R2Op.inverse(dL_chirp).reshape(R2Op.dimsd)

fig, axs = plt.subplots(1, 4, figsize=(12, 4), sharey=True)
axs[0].imshow(d.T, vmin=-1, vmax=1, cmap="bwr_r", extent=(x[0], x[-1], t[-1], t[0]))
axs[0].set(xlabel=r"$x$ [m]", ylabel=r"$t$ [s]", title="Input model")
axs[0].axis("tight")
axs[1].imshow(
dL_chirp.T,
cmap="bwr_r",
vmin=-dL_chirp.max(),
vmax=dL_chirp.max(),
extent=(1e3 * px[0], 1e3 * px[-1], t[-1], t[0]),
)
axs[1].set(xlabel=r"$p$ [s/km]", title="Radon Chirp")
axs[1].axis("tight")
axs[2].imshow(
cmap="bwr_r",
extent=(x[0], x[-1], t[-1], t[0]),
)
axs[2].set(xlabel=r"$x$ [m]", title="Adj Radon Chirp")
axs[2].axis("tight")
axs[3].imshow(
dinv_chirp.T,
cmap="bwr_r",
vmin=-d.max(),
vmax=d.max(),
extent=(x[0], x[-1], t[-1], t[0]),
)
axs[3].set(xlabel=r"$x$ [m]", title="Inv Radon Chirp")
axs[3].axis("tight")
plt.tight_layout()


Finally we repeat the same exercise with 3d data.

par = {
"ot": 0,
"dt": 0.004,
"nt": 51,
"ox": -400,
"dx": 10,
"nx": 81,
"oy": -600,
"dy": 10,
"ny": 61,
"f0": 20,
}
theta = [10]
phi = [0]
t0 = [0.1]
amp = [1.0]

# Create axes
t, t2, x, y = pylops.utils.seismicevents.makeaxis(par)
dt, dx, dy = par["dt"], par["dx"], par["dy"]

# Generate data
_, d = pylops.utils.seismicevents.linear3d(x, y, t, 1500.0, t0, theta, phi, amp, wav)

npy, pymax = par["ny"], 3e-4
npx, pxmax = par["nx"], 5e-4
py = np.linspace(-pymax, pymax, npy)
px = np.linspace(-pxmax, pxmax, npx)

t, y, x, (pymax * dy / dt, pxmax * dx / dt), dtype="float64"
)
dL_chirp = R3Op * d
dinv_chirp = R3Op.inverse(dL_chirp).reshape(R3Op.dimsd)

fig, axs = plt.subplots(1, 4, figsize=(12, 4), sharey=True)
axs[0].imshow(
d[par["ny"] // 2].T,
vmin=-1,
vmax=1,
cmap="bwr_r",
extent=(x[0], x[-1], t[-1], t[0]),
)
axs[0].set(xlabel=r"$x$ [m]", ylabel=r"$t$ [s]", title="Input model")
axs[0].axis("tight")
axs[1].imshow(
dL_chirp[par["ny"] // 2].T,
cmap="bwr_r",
vmin=-dL_chirp.max(),
vmax=dL_chirp.max(),
extent=(1e3 * px[0], 1e3 * px[-1], t[-1], t[0]),
)
axs[1].set(xlabel=r"$p_x$ [s/km]", title="Radon Chirp")
axs[1].axis("tight")
axs[2].imshow(
cmap="bwr_r",
extent=(x[0], x[-1], t[-1], t[0]),
)
axs[2].set(xlabel=r"$x$ [m]", title="Adj Radon Chirp")
axs[2].axis("tight")
axs[3].imshow(
dinv_chirp[par["ny"] // 2].T,
cmap="bwr_r",
vmin=-d.max(),
vmax=d.max(),
extent=(x[0], x[-1], t[-1], t[0]),
)
axs[3].set(xlabel=r"$x$ [m]", title="Inv Radon Chirp")
axs[3].axis("tight")
plt.tight_layout()

fig, axs = plt.subplots(1, 4, figsize=(12, 4), sharey=True)
axs[0].imshow(
d[:, par["nx"] // 2].T,
vmin=-1,
vmax=1,
cmap="bwr_r",
extent=(x[0], x[-1], t[-1], t[0]),
)
axs[0].set(xlabel=r"$y$ [m]", ylabel=r"$t$ [s]", title="Input model")
axs[0].axis("tight")
axs[1].imshow(
dL_chirp[:, 2 * par["nx"] // 3].T,
cmap="bwr_r",
vmin=-dL_chirp.max(),
vmax=dL_chirp.max(),
extent=(1e3 * py[0], 1e3 * py[-1], t[-1], t[0]),
)
axs[1].set(xlabel=r"$p_y$ [s/km]", title="Radon Chirp")
axs[1].axis("tight")
axs[2].imshow(
cmap="bwr_r",
extent=(x[0], x[-1], t[-1], t[0]),
)
axs[2].set(xlabel=r"$y$ [m]", title="Adj Radon Chirp")
axs[2].axis("tight")
axs[3].imshow(
dinv_chirp[:, par["nx"] // 2].T,
cmap="bwr_r",
vmin=-d.max(),
vmax=d.max(),
extent=(x[0], x[-1], t[-1], t[0]),
)
axs[3].set(xlabel=r"$y$ [m]", title="Inv Radon Chirp")
axs[3].axis("tight")
plt.tight_layout()


Total running time of the script: (0 minutes 1.793 seconds)

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