Preprints
https://doi.org/10.5194/essd-2024-358
https://doi.org/10.5194/essd-2024-358
28 Aug 2024
 | 28 Aug 2024
Status: a revised version of this preprint was accepted for the journal ESSD.

SDUST2023BCO: a global seafloor model determined from 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

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, the 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 0°–360° E and 80° S–80° N, has been constructed, known as the Shandong University of Science and Technology 2023 Bathymetric Chart of the Oceans (SDUST2023BCO), with a grid size of 1′×1′. The multi-source differential marine geodetic data used include gravity anomaly data released by Shandong University of Science and Technology, vertical gravity gradient and the vertical deflection data released by Scripps Institution of Oceanography, as well as mean dynamic topography data released by the Centre National d’Etudes Spatiales. 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 at interesting points. Finally, a high-precision bathymetric model with a resolution of 1′×1′ has been constructed for the global marine area. The accuracy of the bathymetric model is evaluated by comparing with single-beam shipborne 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 ocean bathymetric information. The SDUST2023BCO model is available at https://doi.org/10.5281/zenodo.13341896 (Zhou et al., 2024).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Shuai Zhou, Jinyun Guo, Huiying Zhang, Yongjun Jia, Heping Sun, Xin Liu, and Dechao An

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-358', Anonymous Referee #1, 17 Sep 2024
    • AC1: 'Reply on RC1', Jinyun Guo, 14 Oct 2024
  • RC2: 'Comment on essd-2024-358', Anonymous Referee #2, 16 Oct 2024
    • AC2: 'Reply on RC2', Jinyun Guo, 27 Oct 2024
Shuai Zhou, Jinyun Guo, Huiying Zhang, Yongjun Jia, Heping Sun, Xin Liu, and Dechao An

Data sets

SDUST2023BCO: a global seafloor model determined from multi-layer perceptron neural network using multi-source differential marine geodetic data Zhou Shuai, Guo Jinyun, Zhang Huiying, Jia Yongjun, Sun Heping, Liu Xin, and An Dechao https://doi.org/10.5281/zenodo.13341896

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

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Latest update: 11 Nov 2024
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Short summary
Our research focuses on using machine learning to enhance the accuracy and efficiency of bathymetric model. In this paper, the 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, has been constructed, known as the Shandong University of Science and Technology 2023 Bathymetric Chart of the Oceans (SDUST2023BCO), with a grid size of 1′.
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