Joint Inversion of Time-Lapse Surface Gravity and Seismic Data for Monitoring of 3D CO2 Plumes via Deep Learning

Geological storage of CO$_2$ is a key climate change mitigation strategy. As important as location selection and injection planning is monitoring that the gas is contained for long periods of time. We introduce a fully 3D, deep learning-based approach for the joint inversion of time-lapse surface gravity and seismic data for reconstructing subsurface density and velocity models. The target application of this proposed inversion approach is the prediction of subsurface CO2 plumes as a complementary tool for monitoring CO2 sequestration deployments. Our joint inversion technique outperforms deep learning-based gravity-only and seismic-only inversion models, achieving improved density and velocity reconstruction, accurate segmentation, and higher R-squared coefficients. Future work will focus on validating our approach with larger datasets, simulations with other geological storage sites, and, ultimately, field data.