TY  - Data
T1  - SDUST2023BCO: a global seafloor model determined from multi-layer perceptron neural network using multi-source differential marine geodetic data
A1  - GOU Jinyun 
DO  - 10.5281/zenodo.13341896
PY  - 2026
DA  - 2026-01-30
PB  - National Cryosphere Desert Data Center
AB  - This study uses a multi-layer perceptron (MLP) neural network to fuse multi-source ocean geodetic data. A new global ocean depth model covering 180 ° E-180 ° W, 80 ° S-80 ° N, with a grid resolution of 1 ′ × 1 ′ has been constructed - Shandong University of Science and Technology 2023 Global Chart (SDUST2023BCO). The multi-source marine geodetic data used includes gravity anomaly data released by Shandong University of Science and Technology, vertical gravity gradient and vertical deflection data released by Scripps Institution of Oceanography, and average dynamic terrain data released by the French National Centre for Space Research. Firstly, the input and output training data of the MLP model are organized based on multi-source marine geodetic data; Secondly, input the input data of the target point into the trained MLP model to obtain the predicted water depth; Finally, a high-precision seabed terrain model covering the global sea area with a resolution of 1 ′ × 1 ′ was constructed. The effectiveness and reliability of the SDUST2023BCO model were evaluated by comparing it with shipborne single beam depth measurement data, as well as the GEBCO2023 and topo-25.1 models. The results indicate that the SDUST2023BCO model is accurate and reliable, and can effectively capture and reflect global ocean terrain information.
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/89d706f9-b18f-40d6-8890-1ca4297ecdee
ER  -