3D cross-gradient joint inversion of gravity, magnetic and MT data using a reweighting strategy: application in the Yanggao geothermal field, China

Summary Geophysical joint inversion offers a robust approach for integrating diverse geophysical datasets, leveraging their complementary information to mitigate the nonuniqueness inherent in individual data inversion. This approach thereby enhances model consistency and improves subsurface characterization. In this study, we investigate the effectiveness of three-dimensional (3D) joint inversion of gravity, magnetic and magnetotelluric (MT) data for deep geothermal exploration. We first implemented a reweighting optimization strategy for gravity and magnetic data to increase the vertical resolution of jointly inverted models of potential data. Subsequently, we integrated the optimized gravity and magnetic inversion scheme with MT data using a cross-gradient constraint for comprehensive joint inversion. Synthetic experiments validated the effectiveness of our developed reweighting optimization strategy and joint inversion scheme, demonstrating improved structural resolution and model reliability compared to separate inversions. Finally, we applied the methodology to the Yanggao geothermal field in northern Datong Basin, China, to assess its applicability for deep geothermal exploration. The joint inversion approach, offering an advantage over separate inversions, provides enhanced subsurface characterization by more accurately delineating hydrothermal pathways and potential geothermal reservoirs of the geothermal system within the investigated area.