Bathymetry Refinement over Seamount Regions from SAR Altimetric Gravity Data through a Kalman Fusion Method

Abstract

Seafloor topography over seamount areas is crucial for studying plate motions, seafloor seismicity, and seamount ecosystems. However, seamount bathymetry modeling is difficult due to the complex hydrodynamic environment, biodiversity, and scarcity of shipborne echo sounding data. The use of satellite altimeter-derived gravity data is a complementary way of bathymetry computation; in particular, the incorporation of synthetic aperture radar (SAR) altimeter data may be useful for seamount bathymetry modeling. Moreover, the widely used filtering method may have difficulty in combing different bathymetry data sets and may affect the quality of the computed bathymetry. To mitigate this issue, we introduce a Kalman fusion method for weighting and combining gravity-derived bathymetry data and the reference bathymetry model. Numerical experiments in the seamount regions over the Molloy Ridge show that the use of SAR-based altimetric gravity data improves the local bathymetry model, by a magnitude of 14.27 m, compared to the result without SAR data. In addition, the developed Kalman fusion method outperforms the traditionally used filtering method, and the bathymetry computed from the Kalman fusion method is improved by a magnitude of 9.34 m. Further comparison shows that our solution has improved quality compared to a recently released global bathymetry model, namely, GEBCO 2022 (GEBCO: General Bathymetric Chart of the Oceans), by a magnitude of 34.34 m. The bathymetry model in this study may be substituted for existing global bathymetry models for geophysical investigations over the target area.