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.
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