Does this skill support regularization?

Absolutely. It includes operators for smoothness (derivatives) and sparsity (L1/TV) constraints, along with specialized solvers like FISTA and Split Bregman.

What is the advantage of using PyLops over standard matrices?

PyLops uses matrix-free representations, meaning it calculates results on the fly without storing massive matrices in memory, which is essential for large-scale data.

Can I use PyLops for seismic data processing in Claude?

Yes, PyLops is specifically designed for geoscience workflows, including deconvolution, tomography, and seismic imaging.

Is GPU acceleration available?

Yes, PyLops supports CuPy, allowing operators to run on GPUs automatically when provided with CuPy arrays.

PyLops Inverse Problems & Linear Operators

Name: PyLops Inverse Problems & Linear Operators
Author: SteadfastAsArt

bySteadfastAsArt

•

Ciencia de Datos y ML

Implements matrix-free linear operators and solvers for large-scale inverse problems, signal processing, and seismic imaging workflows.

PyLops is a specialized skill for Claude Code that enables the efficient handling of large-scale inverse problems using an operator algebra approach. It allows developers and geoscientists to perform complex forward and adjoint operations without the memory overhead of explicit matrices. This skill provides a robust library of linear operators—ranging from basic derivatives and FFTs to specialized seismic convolution—and includes advanced iterative solvers like LSQR, FISTA, and Split Bregman. It is ideal for high-performance computing tasks in geoscience, medical imaging, and signal processing where memory efficiency and GPU acceleration are critical.

Características Principales

01Comprehensive solver suite including LSQR, CGLS, FISTA, and Regularized Inversion.

02Advanced operator algebra for vertical/horizontal stacking and block diagonal composition.

03Matrix-free linear operators for memory-efficient large-scale data processing.

0412 GitHub stars

05Built-in signal processing tools for 1D/2D convolution, gradients, and smoothing.

06Automatic GPU acceleration support via seamless CuPy integration.

Casos de Uso

01Performing regularized seismic deconvolution and reflectivity estimation.

02Solving image denoising and reconstruction problems using Total Variation (TV) regularization.

03Building complex multi-step signal processing pipelines for geophysical or medical data.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add steadfastasart/geoscience-skills pylops

For use in Claude.ai and ChatGPT

Download Skill