Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published April 3, 2023 | Supplemental Material
Journal Article Open

Rapid Seismic Waveform Modeling and Inversion With Neural Operators


Seismic waveform modeling is a powerful tool for determining earth structure models and unraveling earthquake rupture processes, but it is usually computationally expensive. We introduce a scheme to vastly accelerate these calculations with a recently developed machine learning paradigm called the neural operator. Once trained, these models can simulate a full wavefield at negligible cost. We use a U-shaped neural operator to learn a general solution operator to the 2-D elastic wave equation from an ensemble of numerical simulations performed with random velocity models and source locations. We show that full-waveform modeling with neural operators is nearly two orders of magnitude faster than conventional numerical methods, and more importantly, the trained model enables accurate simulation for velocity models, source locations, and mesh discretization distinctly different from the training dataset. The method also enables convenient full-waveform inversion with automatic differentiation.

Additional Information

© 2023 IEEE.

Attached Files

Supplemental Material - supp1-3264210.docx


Files (1.8 MB)
Name Size Download all
1.8 MB Download

Additional details

August 22, 2023
October 23, 2023