Enhanced Bathymetric Inversion for Tectonic Features via Multi-Gravity-Component DenseNet: A Case Study of Rift Identification in the South China Sea

Submarine rift systems represent critical tectonic features whose accurate bathymetric characterization remains challenging yet essential for understanding plate boundary dynamics. However, traditional bathymetric inversion methods based on altimetric gravity data exhibit poor performance in resolving rift and steep-slope terrains. To address this limitation and enhance accuracy in complex topographic regions, we propose a multi-gravity-component fusion framework based on an improved DenseNet architecture. By integrating shipborne bathymetry, gravity anomaly (GA), vertical gravity gradient (VGG), vertical deflection components (meridian component ξ and prime vertical component η), and GEBCO_2024, we construct a 16 × 16 × 9 input tensor. The model incorporates adaptive transition layers to preserve fine-scale tectonic features and curvature-based stratification to balance learning across diverse terrains. Validation using 43,035 independent points yields an RMSE of 84.75 m, representing a 47.6% reduction relative to GEBCO_2024. Crucially, in the identified rift targets, errors decreased by 69.3–87.1%. Ablation studies reveal that vertical deflection components (ξ, η) dominate the physical constraints, with their removal increasing the RMSE by 91.08 m (a 107.5% increase relative to the baseline error). Architectural innovations and stratification reduce steep-slope RMSE by 6.1%. These results validate the efficacy of directional gravity derivatives for tectonic feature inversion and demonstrate significant potential for application to mid-ocean ridge systems.