Bathymetry Prediction with SWOT Gravity Anomaly using Machine Learning Methods
Year: 2025
Type: dataset
Abstract: The ocean surface has broad bumps and dips caused by variations in gravity, which reflect the mass of seafloor features such as ridges, seamounts, and trenches. Because of this link, mapping gravity allows us to infer seafloor depth, though physics limits the resolution to about 3–5 km in the deep ocean. Satellite altimeters have steadily improved these maps for decades, and the new SWOT mission now provides the most precise gravity data to date. In this study, five research groups used machine learning to combine SWOT gravity data with ship soundings, producing consistent global seafloor maps that are 23–46\% more accurate than older methods. The largest improvements are in mapping shallow seamounts and deep trenches, advancing our ability to study ocean circulation, tectonics, hazards, and marine ecosystems, while contributing to the international Seabed 2030 effort.
Source: Figshare
Institution: Technical University of Denmark
Language: English
Subfield: Geophysics
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: green
Award: G2056948424
OpenAlex ID: W7117745833