Shift

This example shows how to use the pylops.signalprocessing.Shift operator to apply fractional delay to an input signal. Whilst this operator acts on 1D signals it can also be applied on any multi-dimensional signal on a specific direction of it.

import matplotlib.pyplot as plt
import numpy as np

import pylops

plt.close("all")

Let’s start with a 1D example. Define the input parameters: number of samples of input signal (nt), sampling step (dt) as well as the input signal which will be equal to a ricker wavelet:

nt = 127
dt = 0.004
t = np.arange(nt) * dt
ntwav = 41

wav = pylops.utils.wavelets.ricker(t[:ntwav], f0=20)[0]
wav = np.pad(wav, [0, nt - len(wav)])
WAV = np.fft.rfft(wav, n=nt)

We can shift this wavelet by \(5.5\mathrm{dt}\):

shift = 5.5 * dt
Op = pylops.signalprocessing.Shift(nt, shift, sampling=dt, real=True, dtype=np.float64)
wavshift = Op * wav
wavshiftback = Op.H * wavshift

plt.figure(figsize=(10, 3))
plt.plot(t, wav, "k", lw=2, label="Original")
plt.plot(t, wavshift, "r", lw=2, label="Shifted")
plt.plot(t, wavshiftback, "--b", lw=2, label="Adjoint")
plt.axvline(t[ntwav - 1], color="k")
plt.axvline(t[ntwav - 1] + shift, color="r")
plt.xlim(0, 0.3)
plt.legend()
plt.title("1D Shift")
plt.tight_layout()
1D Shift

We can repeat the same exercise for a 2D signal and perform the shift along the first and second dimensions.

shift = 10.5 * dt

# 1st axis
wav2d = np.outer(wav, np.ones(10))
Op = pylops.signalprocessing.Shift(
    (nt, 10), shift, axis=0, sampling=dt, real=True, dtype=np.float64
)
wav2dshift = Op * wav2d
wav2dshiftback = Op.H * wav2dshift

fig, axs = plt.subplots(1, 3, figsize=(10, 3))
axs[0].imshow(wav2d, cmap="gray")
axs[0].axis("tight")
axs[0].set_title("Original")
axs[1].imshow(wav2dshift, cmap="gray")
axs[1].set_title("Shifted")
axs[1].axis("tight")
axs[2].imshow(wav2dshiftback, cmap="gray")
axs[2].set_title("Adjoint")
axs[2].axis("tight")
fig.tight_layout()

# 2nd axis
wav2d = np.outer(wav, np.ones(10)).T
Op = pylops.signalprocessing.Shift(
    (10, nt), shift, axis=1, sampling=dt, real=True, dtype=np.float64
)
wav2dshift = Op * wav2d
wav2dshiftback = Op.H * wav2dshift

fig, axs = plt.subplots(1, 3, figsize=(10, 3))
axs[0].imshow(wav2d, cmap="gray")
axs[0].axis("tight")
axs[0].set_title("Original")
axs[1].imshow(wav2dshift, cmap="gray")
axs[1].set_title("Shifted")
axs[1].axis("tight")
axs[2].imshow(wav2dshiftback, cmap="gray")
axs[2].set_title("Adjoint")
axs[2].axis("tight")
fig.tight_layout()
  • Original, Shifted, Adjoint
  • Original, Shifted, Adjoint

Finally we consider a more generic case where we apply a trace varying shift

shift = dt * np.arange(10)

wav2d = np.outer(wav, np.ones(10))
Op = pylops.signalprocessing.Shift(
    (nt, 10), shift, axis=0, sampling=dt, real=True, dtype=np.float64
)
wav2dshift = Op * wav2d
wav2dshiftback = Op.H * wav2dshift

fig, axs = plt.subplots(1, 3, figsize=(10, 3))
axs[0].imshow(wav2d, cmap="gray")
axs[0].axis("tight")
axs[0].set_title("Original")
axs[1].imshow(wav2dshift, cmap="gray")
axs[1].set_title("Shifted")
axs[1].axis("tight")
axs[2].imshow(wav2dshiftback, cmap="gray")
axs[2].set_title("Adjoint")
axs[2].axis("tight")
fig.tight_layout()
Original, Shifted, Adjoint

Total running time of the script: ( 0 minutes 1.111 seconds)

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