Pylops

PyLops - Linear Operators Library

Quick Reference

import numpy as np
import pylops

# Create operator and apply forward/adjoint
A = pylops.FirstDerivative(n=100, dtype='float64')
y = A @ x        # Forward: y = A @ x
x_adj = A.H @ y  # Adjoint: x = A.H @ y
x_est = A / y    # Solve inverse problem

Key Classes

Essential Operations

Basic Operators

# Diagonal operator
D = pylops.Diagonal(np.array([1., 2., 3.]))
y = D @ x; x_adj = D.H @ y

# Derivatives
D1 = pylops.FirstDerivative(n, dtype='float64')
D2 = pylops.SecondDerivative(n, dtype='float64')
G = pylops.Gradient(dims=(64, 64), dtype='float64')

Convolution

wavelet = np.sin(np.linspace(0, 2*np.pi, 21)) * np.hanning(21)
C = pylops.signalprocessing.Convolve1D(n, h=wavelet, offset=10)
y = C @ x      # Convolve
x_adj = C.H @ y  # Correlation (adjoint)

Compose and Stack Operators

# Chain: y = C @ B @ A @ x
composed = pylops.Smoothing1D(5, n) @ pylops.FirstDerivative(n) @ pylops.Identity(n)

# Stack operators
V = pylops.VStack([A, B])     # Vertical: (2n, n)
H = pylops.HStack([A, B])     # Horizontal: (n, 2n)
BD = pylops.BlockDiag([A, B]) # Block diagonal: (2n, 2n)

Solve Inverse Problems

# Simple least squares
x_est = A / y

# Normal equations
x_est = pylops.optimization.leastsquares.NormalEquationsInversion(A, None, y)

# Regularized inversion with smoothness
Reg = pylops.SecondDerivative(n)
x_est = pylops.optimization.leastsquares.RegularizedInversion(
    A, [Reg], y, epsRs=[0.1]
)

Iterative Solvers

x_lsqr = pylops.optimization.solver.lsqr(A, y, iter_lim=100)[0]
x_cgls = pylops.optimization.solver.cgls(A, y, niter=100)[0]

Sparsity-Promoting Inversion

x_l1 = pylops.optimization.sparsity.fista(A, y, niter=100, eps=0.1)[0]

Verify Adjoint (Dot Test)

A = pylops.FirstDerivative(100)
pylops.utils.dottest(A, 100, 100, verb=True)  # Dot test passed!

Common Patterns

Seismic Deconvolution

C = pylops.signalprocessing.Convolve1D(n, h=wavelet, offset=len(wavelet)//2)
seismic = C @ reflectivity

# Deconvolve (regularized)
Reg = pylops.SecondDerivative(n)
reflectivity_est = pylops.optimization.leastsquares.RegularizedInversion(
    C, [Reg], seismic, epsRs=[0.01]
)

Image Denoising with TV

ny, nx = image.shape
G = pylops.Gradient(dims=(ny, nx))
x_tv = pylops.optimization.sparsity.splitbregman(
    pylops.Identity(ny*nx), G, image.ravel(),
    niter_inner=5, niter_outer=10, mu=1.0, epsRL1s=[0.1]
)[0].reshape(ny, nx)

When to Use vs Alternatives

Choose PyLops when: You need matrix-free linear operators that scale to large problems without forming explicit matrices. Its operator algebra (@, VStack, BlockDiag) and built-in solvers (LSQR, FISTA, Split Bregman) make inverse problem workflows concise.

Avoid PyLops when: Your problem is small enough for explicit matrices (use NumPy/SciPy), or you need nonlinear operators (PyLops is strictly linear).

Common Workflows

Regularized seismic deconvolution

• Define wavelet array and create Convolve1D operator
• Generate or load seismic trace data
• Run dot test to verify operator adjoint: pylops.utils.dottest()
• Set up regularization operator (e.g., SecondDerivative for smoothness)
• Run RegularizedInversion(C, [Reg], data, epsRs=[eps])
• Compare estimated reflectivity against true (if available)
• Tune epsRs parameter: higher = smoother, lower = sharper
• For sparse solutions, use pylops.optimization.sparsity.fista() instead

Tips

1. Never form full matrix - Use .matvec() and .rmatvec() for memory efficiency
2. Check shapes - Operators have .shape attribute like matrices
3. Verify adjoint - Always run pylops.utils.dottest() for custom operators
4. Start simple - Test on small problems before scaling up
5. Use GPU - Pass CuPy arrays for automatic GPU acceleration

References

• Operators Reference - Complete list of available operators
• Solvers Reference - Solver methods and configuration options

Scripts

• scripts/deconvolution.py - Seismic deconvolution example