  Seismic monitoring of carbon storage sequestration is a challenging problem
involving both fluid-flow physics and wave physics. Additionally, monitoring
usually requires the solvers for these physics to be coupled and differentiable
to effectively invert for the subsurface properties of interest. To drastically
reduce the computational cost, we introduce a learned coupled inversion
framework based on the wave modeling operator, rock property conversion and a
proxy fluid-flow simulator. We show that we can accurately use a Fourier neural
operator as a proxy for the fluid-flow simulator for a fraction of the
computational cost. We demonstrate the efficacy of our proposed method by means
of a synthetic experiment. Finally, our framework is extended to carbon
sequestration forecasting, where we effectively use the surrogate Fourier
neural operator to forecast the CO2 plume in the future at near-zero additional
cost.

Geophysics;Machine Learning