SDUST2023BCO: a global seafloor model determined from a multi-layer perceptron neural network using multi-source differential marine geodetic data
<p>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 g...
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Copernicus Publications
2025-01-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/17/165/2025/essd-17-165-2025.pdf |
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| author | S. Zhou S. Zhou J. Guo H. Zhang Y. Jia H. Sun X. Liu D. An |
| author_facet | S. Zhou S. Zhou J. Guo H. Zhang Y. Jia H. Sun X. Liu D. An |
| author_sort | S. Zhou |
| collection | DOAJ |
| description | <p>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 <span class="inline-formula">1<sup>′</sup></span> <span class="inline-formula">×</span> <span class="inline-formula">1<sup>′</sup></span>. 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 <span class="inline-formula">1<sup>′</sup></span> <span class="inline-formula">×</span> <span class="inline-formula">1<sup>′</sup></span> 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 <span class="uri">https://doi.org/10.5281/zenodo.13341896</span> (Zhou et al., 2024).</p> |
| format | Article |
| id | doaj-art-cae78893144f416191c6c125af69d512 |
| institution | Kabale University |
| issn | 1866-3508 1866-3516 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
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| series | Earth System Science Data |
| spelling | doaj-art-cae78893144f416191c6c125af69d5122025-01-20T07:23:10ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162025-01-011716517910.5194/essd-17-165-2025SDUST2023BCO: a global seafloor model determined from a multi-layer perceptron neural network using multi-source differential marine geodetic dataS. Zhou0S. Zhou1J. Guo2H. Zhang3Y. Jia4H. Sun5X. Liu6D. An7College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaState Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaNational Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100812, ChinaState Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaSchool of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China<p>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 <span class="inline-formula">1<sup>′</sup></span> <span class="inline-formula">×</span> <span class="inline-formula">1<sup>′</sup></span>. 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 <span class="inline-formula">1<sup>′</sup></span> <span class="inline-formula">×</span> <span class="inline-formula">1<sup>′</sup></span> 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 <span class="uri">https://doi.org/10.5281/zenodo.13341896</span> (Zhou et al., 2024).</p>https://essd.copernicus.org/articles/17/165/2025/essd-17-165-2025.pdf |
| spellingShingle | S. Zhou S. Zhou J. Guo H. Zhang Y. Jia H. Sun X. Liu D. An SDUST2023BCO: a global seafloor model determined from a multi-layer perceptron neural network using multi-source differential marine geodetic data Earth System Science Data |
| title | SDUST2023BCO: a global seafloor model determined from a multi-layer perceptron neural network using multi-source differential marine geodetic data |
| title_full | SDUST2023BCO: a global seafloor model determined from a multi-layer perceptron neural network using multi-source differential marine geodetic data |
| title_fullStr | SDUST2023BCO: a global seafloor model determined from a multi-layer perceptron neural network using multi-source differential marine geodetic data |
| title_full_unstemmed | SDUST2023BCO: a global seafloor model determined from a multi-layer perceptron neural network using multi-source differential marine geodetic data |
| title_short | SDUST2023BCO: a global seafloor model determined from a multi-layer perceptron neural network using multi-source differential marine geodetic data |
| title_sort | sdust2023bco a global seafloor model determined from a multi layer perceptron neural network using multi source differential marine geodetic data |
| url | https://essd.copernicus.org/articles/17/165/2025/essd-17-165-2025.pdf |
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