A Novel Depth-Weighting Approach Based on Regularized Downward Continuation for Enhanced Gravity Inversion

Gravity inversion plays a crucial role in mineral exploration and resource evaluation, yet conventional depth-weighting methods often impose uniform resolution across all depths and fail to effectively delineate anomaly boundaries. This study presents an innovative attentional depth-weighting matrix based on a regularized downward continuation (RDC) mechanism. First, the observed gravity data are projected to greater depths using RDC, which suppresses high-frequency noise amplification. Next, gradient extrema are extracted from each grid cell to identify anomaly boundaries, forming a constant weighting matrix that enhances the focus on target regions. This matrix is then integrated with traditional depth weighting and a minimum-support focusing factor to optimize the inversion process. The proposed method is validated through two synthetic models, demonstrating improved resolution of deeper targets and more accurate amplitude recovery compared to conventional approaches. Further application to the Dahongshan Copper–Iron Ore region in Yunnan, China, reveals a deep intrusive body at approximately 4–5 km depth, extending east–west with a distinct “U”-shaped geometry. These results, consistent with previous geological studies, highlight the method’s ability to enhance deep anomaly characterization while effectively suppressing shallow noise interference. By balancing noise reduction with improved resolution, this approach broadens the applicability of gravity inversion in geological, geothermal, and mineral resource exploration.