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We introduce a fully 3D, deep learning-based approach for the joint inversionof time-lapse surface gravity and seismic data for reconstructing subsurfacedensity and velocity models. The target application of this proposed inversionapproach is the prediction of subsurface CO2 plumes as a complementary tool formonitoring CO2 sequestration deployments. Our joint inversion techniqueoutperforms deep learning-based gravity-only and seismic-only inversion models,achieving improved density and velocity reconstruction, accurate segmentation,and higher R-squared coefficients. These results indicate that deeplearning-based joint inversion is an effective tool for CO$_2$ storagemonitoring. Future work will focus on validating our approach with largerdatasets, simulations with other geological storage sites, and ultimately fielddata.

Joint inversion of Time-Lapse Surface Gravity and Seismic Data for Monitoring of 3D CO$_2$ Plumes via Deep Learning