  We introduce three algorithms that invert simulated gravity data to 3D
subsurface rock/flow properties. The first algorithm is a data-driven, deep
learning-based approach, the second mixes a deep learning approach with
physical modeling into a single workflow, and the third considers the time
dependence of surface gravity monitoring. The target application of these
proposed algorithms is the prediction of subsurface CO$_2$ plumes as a
complementary tool for monitoring CO$_2$ sequestration deployments. Each
proposed algorithm outperforms traditional inversion methods and produces
high-resolution, 3D subsurface reconstructions in near real-time. Our proposed
methods achieve Dice scores of up to 0.8 for predicted plume geometry and near
perfect data misfit in terms of $\mu$Gals. These results indicate that
combining 4D surface gravity monitoring with deep learning techniques
represents a low-cost, rapid, and non-intrusive method for monitoring CO$_2$
storage sites.
