A Geospatially Encoded Dual-Channel Network with Attention and Physics Constraints for Accurate Seafloor Topography Reconstruction

Mapping seafloor topography is of great significance for deep-sea navigation, marine resource exploration, and aquatic ecosystem conservation. Advances in bathymetric surveying technology have progressively enriched our understanding of the oceans. However, due to the high cost and low coverage of ship-based surveys, extensive regions of the global ocean remain unmeasured. Bathymetric prediction based on gravity anomalies remains the dominant approach for mapping the seafloor. Traditional physical methods are constrained by limitations in model assumptions, making it difficult to further improve the accuracy of depth estimation. Given their strong capability for nonlinear fitting, neural network methods have attracted growing interest in bathymetric estimation. Nevertheless, their inherent tendency to overfit often leads to poor generalization performance—particularly when the water depth distribution in the test set diverges from that of the training set, a common scenario in seafloor mapping due to ship tracks typically avoiding shallow or reef-prone areas. To address this limitation, this paper proposes a novel framework integrating traditional physical models with neural networks: the Geospatial Dual-Channel Attention Physics-constrained Network for bathymetry prediction. The bathymetric model derived from the Parker–Oldenburg method is incorporated into the network. Notably, an attention mechanism is integrated into the architecture to enhance the model’s fitting capacity. Extensive validation demonstrates the effectiveness of the proposed model, with 97.2561% of the residuals falling within \((\pm 100)\) m.