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Abstract: Bathymetry Prediction with SWOT Gravity Anomaly using Machine Learning Methods (AGU25)

Bathymetry Prediction with SWOT Gravity Anomaly using Machine Learning Methods

David T Sandwell1, Benjamin James Phrampus2, Bjarke Nilsson3, Yao Yu4, Farshad Salajegheh5, Hugh Harper6, Biao Liu3, Ole Baltazar Andersen3, Walter H F Smith7, Paul Elmore8, James Beale9, Luis Altamirano10, Jonathan Kirby3 and Jamie Roberts6, (1)Scripps Institution of Oceanography, University of California San Diego, Institute of Geophysics and Planetary Physics, La Jolla, United States, (2)US Naval Research Laboratory, Ocean Sciences Division, Stennis, United States, (3)DTU Space, Lyngby, Denmark, (4)Scripps Institution of Oceanography, Institute of Geophysics and Planetary Physics, La Jolla, United States, (5)University of Newcastle, Callaghan, Australia, (6)National Geospatial Intelligence Agency, Springfield, United States, (7)NOAA Lab Satellite Altimetry, College Park, MD, United States, (8)Applied Physics Laboratory Johns Hopkins, Laurel, United States, (9)National Geospatial Intelligence Agency, St. Louis, United States, (10)The University of Southern Mississippi, School of Ocean Science and Engineering, Stennis Space Center, United States
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