Digital Bathymetric Model Fusion Using Vertical Gravity Gradient: A Bayesian Approach

As a systematic outcome of ocean depth exploration, global digital bathymetric models provide convenient data support and tools for the in-depth study and application of seafloor topography. Five major digital bathymetric models are publicly available. However, significant differences can be found between these digit models at some places through a careful comparison, as they are constructed on methods and data originating from different sources. Confusion may exist when choosing which one of them to use in practical applications. A natural question that arises is how to obtain a more reliable fused model based on the existing models. To this end, we develop a novel model fusion method within the Bayesian framework. Its most outstanding feature is the ability to incorporate the marine gravity field as auxiliary data to guide the fusion of digital bathymetric models. Based on publicly available shipborne bathymetry within the North Pacific, the fused model reduces RMSE by 7.13–14.08 m on average across the stratified in-sample assessments relative to DTU18, ETOPO2022, GEBCO2024, SRTM15 + v2.6, and Topo27.1. In the independent local validation area covered by WESPMI Cruises 581/582, the fused model further reduces the overall RMSE by 26.88–92.04 m compared with the same set of digital bathymetric models.