SDUST2023BCO: a global seafloor model determined from a multi-layer perceptron neural network using multi-source differential marine geodetic data

Abstract. Seafloor topography, as a fundamental marine spatial geographic information, plays a vital role in marine observation and science research. With the growing demand for high-precision bathymetric models, a multi-layer perceptron (MLP) neural network is used to integrate multi-source marine geodetic data in this paper. A new bathymetric model of the global ocean, spanning 180° E–180° W and 80° S–80° N, known as the Shandong University of Science and Technology 2023 Bathymetric Chart of the Oceans (SDUST2023BCO), has been constructed, with a grid size of 1′ × 1′. The multi-source marine geodetic data used include gravity anomaly data released by the Shandong University of Science and Technology, the vertical gravity gradient and the vertical deflection data released by the Scripps Institution of Oceanography (SIO), and the mean dynamic topography data released by Centre National d'Etudes Spatiales (CNES). First, input and output data are organized from the multi-source marine geodetic data to train the MLP model. Second, the input data at interesting points are fed into the MLP model to obtain prediction bathymetry. Finally, a high-precision bathymetric model with a resolution of 1′ × 1′ has been constructed for the global marine area. The validity and reliability of the SDUST2023BCO model are evaluated by comparing with shipborne single-beam bathymetric data and GEBCO_2023 and topo_25.1 models. The results demonstrate that the SDUST2023BCO model is accurate and reliable, effectively capturing and reflecting global marine bathymetric information. The SDUST2023BCO model is available at https://doi.org/10.5281/zenodo.13341896 (Zhou et al., 2024).