Investigations on the contribution of airborne gravity measurements in the prediction of bathymetry: a case study in the Puerto Rico region

Global bathymetric mapping remains incomplete, with more than 70% of the global seafloor topography requiring supplemental bathymetric data derived from gravity data. Airborne gravity offers clear advantages over satellite-derived marine gravity in nearshore and coastal regions, as it is unaffected by errors such as contamination of altimeter echo waveforms or insufficient tidal corrections. This study investigates the potential of airborne gravity to improve bathymetric predictions compared to satellite-derived gravity data. Airborne gravity data from the GRAV-D project in the Puerto Rico region are processed via downward continuation. Using the gravity-geologic method (GGM), two bathymetric models are constructed: one integrating airborne gravity data (BAT_DG) and the other using the satellite-derived SS v32.1 model from the Scripps Institution of Oceanography (SIO) (BAT_SS), both constrained by single-beam depth soundings. The accuracy of these models was evaluated using independent multibeam sounding data. Results indicate that the BAT_DG model demonstrates approximately 18% higher accuracy than the BAT_SS model in shallow waters (0–1000 m depth range). Moreover, when single-beam sounding data constraints were removed for depths of 0–2000 m, the accuracy of the BAT_DG model in nearshore regions improved significantly, achieving a two- to three-fold enhancement over the BAT_SS model. Power spectral density analysis further highlights the capability of the BAT_DG model to resolve bathymetric features with wavelengths shorter than 10 km. These findings underscore the advantages of airborne gravity surveys in improving bathymetric accuracy, particularly in offshore and coastal areas with sparse depth measurements. The enhanced short-wavelength signals and precision of airborne gravity data significantly improve bathymetric resolution and accuracy, offering a robust alternative for seafloor mapping in challenging environments.