Abstract
AbstractThe resolution of altimetry‐derived gravity signals renders traditional bathymetry inversion methods inadequate for detecting short‐wavelength bathymetric features. This study presents a new global seafloor topography model by merging regional models constructed via a deep learning approach recently developed by the authors. Feature extraction techniques were used to refine each regional bathymetric model, resulting in sharpened features that are fuzzily revealed (or not seen) in global bathymetric models such as GEBCO_2023 and SRTM15+V2, especially in areas close to the poles. This is proof that, with regard to current gravity field products, conventional bathymetry inversion methods are weaker at generalizing to uncharted locations of the global ocean floor. At test points, the global seafloor model has mean error and error standard deviation of 1.75 ± 81.15 m. The refined seafloor topography can be used to supplement existing seafloor maps, especially in the polar regions. It is useful for studying seafloor geography, and also for studies in which seafloor ruggedness is required.
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