Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 17, issue 1
Articles | Volume 17, issue 1
https://doi.org/10.5194/essd-17-165-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-165-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 1
Review article
 | 
20 Jan 2025
Review article |  | 20 Jan 2025

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

Shuai Zhou, Jinyun Guo, Huiying Zhang, Yongjun Jia, Heping Sun, Xin Liu, and Dechao An

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Cited articles

An, D., Guo, J., Li, Z., Ji, B., Liu, X., and Chang, X.: Improved gravity-geologic method reliably removing the long-wavelength gravity effect of regional seafloor topography: A case of bathymetric prediction in the South China Sea, IEEE T. Geosci. Remote, 60, 4211912, https://doi.org/10.1109/TGRS.2022.3223047, 2022. 
An, D., Guo, J., Chang, X., Wang, Z., Jia, Y., Liu, X., Bondur, V., and Sun, H.: High-precision 1′ × 1′ bathymetric model of Philippine Sea inversed from marine gravity anomalies, Geosci. Model Dev., 17, 2039–2052, https://doi.org/10.5194/gmd-17-2039-2024, 2024. 
Annan, R. F. and Wan, X.: Mapping seafloor topography of Gulf of Guinea using an adaptive meshed gravity-geologic method, Arab. J. Geosci., 13, 301, https://doi.org/10.1007/s12517-020-05297-8, 2020. 
Fan, D., Li, S., Meng, S., Lin, Y., Xing, Z., Zhang, C., Yang, J., Wan, X., and Qu, Z.: Applying iterative method to solving high-order terms of seafloor topography, Mar. Geod., 43, 63–85, https://doi.org/10.1080/01490419.2019.1670298, 2020. 
Hirt, C. and Rexer, M.: Earth2014: 1 arc-min shape, topography, bedrock and ice-sheet models – Available as gridded data and degree-10,800 spherical harmonics, Int. J. Appl. Earth Obs., 39, 103–112, https://doi.org/10.1016/j.jag.2015.03.001, 2015. 
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Our research focuses on using machine learning to enhance the accuracy and efficiency of bathymetric models. In this paper, a multi-layer perceptron (MLP) neural network is used to integrate multi-source marine geodetic data. And a new bathymetric model of the global ocean, spanning 0–360° E 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′.
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