Joint inversion of Time-Lapse Surface Gravity and Seismic Data for Monitoring of 3D CO$_2$ Plumes via Deep Learning
This provides a complementary tool for monitoring CO2 sequestration deployments, addressing a domain-specific problem in geophysics with incremental improvements over existing deep learning methods.
The paper tackled the problem of monitoring subsurface CO2 plumes by developing a deep learning-based joint inversion method for time-lapse surface gravity and seismic data, resulting in improved density and velocity reconstruction, accurate segmentation, and higher R-squared coefficients compared to gravity-only or seismic-only models.
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. These results indicate that deep learning-based joint inversion is an effective tool for CO$_2$ storage monitoring. Future work will focus on validating our approach with larger datasets, simulations with other geological storage sites, and ultimately field data.