Learned multiphysics inversion with differentiable programming and machine learning
Abstract
We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, our software is designed to be both readable and scalable. This allows researchers to easily formulate their problems in an abstract fashion while exploiting the latest developments in high-performance computing. We illustrate and demonstrate our design principles and their benefits by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which aside from coupling of wave physics and multiphase flow, involves machine learning.
Cite
@article{arxiv.2304.05592,
title = {Learned multiphysics inversion with differentiable programming and machine learning},
author = {Mathias Louboutin and Ziyi Yin and Rafael Orozco and Thomas J. Grady and Ali Siahkoohi and Gabrio Rizzuti and Philipp A. Witte and Olav Møyner and Gerard J. Gorman and Felix J. Herrmann},
journal= {arXiv preprint arXiv:2304.05592},
year = {2023}
}