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.
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.
the Creative Commons Attribution 4.0 License.
Data review article
| 
20 Jan 2025
Data review article |
| 20 Jan 2025
| 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
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Huiying Zhang
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Yongjun Jia
National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100812, China
Heping Sun
State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China
Xin Liu
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Dechao An
School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China
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Cited
16 citations as recorded by crossref.
- Enhancing Seafloor Topography Inversion Based on Marine Gravity Data Using Robust Weighted Total Least Squares F. Zhu et al.
- A Hybrid CUBE-IForest Approach for Outlier Detection in Multibeam Bathymetry R. Han et al.
- Deriving full-tensor gravity gradients over the Arabian Sea from SWOT altimetry using a stacked discretization method H. Guo & X. Wan
- Global seafloor topography model based on a frequency-domain filter combination method X. Chen et al.
- Satellite-Derived Bathymetry Using Sentinel-2 and Airborne Hyperspectral Data: A Deep Learning Approach with Adaptive Interpolation S. Lee et al.
- Enhanced Bathymetric Inversion for Tectonic Features via Multi-Gravity-Component DenseNet: A Case Study of Rift Identification in the South China Sea H. Zhang et al.
- Inversion of Seafloor Topography in the Gulf of Mexico Based on Convolutional Neural Network Integrated With Multisource Gravity Data and Bathymetric Data L. Wei et al.
- Recovering bathymetry from altimetry-derived gravity data using a novel inversion framework by considering nonlinear effects of seafloor topography S. Wang et al.
- Trans-UNet Network for Predicting Bathymetry in South China Sea From Gravity and Geological Data S. Zhou et al.
- Investigations on the contribution of airborne gravity measurements in the prediction of bathymetry: a case study in the Puerto Rico region X. Chen et al.
- Enhanced Seafloor Topography Inversion Using an Attention Channel 1D Convolutional Network Based on Multiparameter Gravity Data: Case Study of the Mariana Trench Q. Wang et al.
- Bathymetry of the Philippine sea with convolution neural network from multisource marine geodetic data J. Guo et al.
- Nonlinear black-box approaches and data fusion for ocean bathymetry modeling in south Iran M. Mohammad et al.
- Uncertainty analysis of bathymetry inversion in the South China Sea: a comparison of deep learning and Bayesian approaches S. Zhou et al.
- SYSU_Topo: a 1-arc-minute global bathymetry from SWOT-derived gravity using the gravity-geological method W. Feng et al.
- Quantitative validation of ICESat-2 ATL24 in shallow marine environments using reference-grade bathymetric data G. Dandabathula et al.
16 citations as recorded by crossref.
- Enhancing Seafloor Topography Inversion Based on Marine Gravity Data Using Robust Weighted Total Least Squares F. Zhu et al.
- A Hybrid CUBE-IForest Approach for Outlier Detection in Multibeam Bathymetry R. Han et al.
- Deriving full-tensor gravity gradients over the Arabian Sea from SWOT altimetry using a stacked discretization method H. Guo & X. Wan
- Global seafloor topography model based on a frequency-domain filter combination method X. Chen et al.
- Satellite-Derived Bathymetry Using Sentinel-2 and Airborne Hyperspectral Data: A Deep Learning Approach with Adaptive Interpolation S. Lee et al.
- Enhanced Bathymetric Inversion for Tectonic Features via Multi-Gravity-Component DenseNet: A Case Study of Rift Identification in the South China Sea H. Zhang et al.
- Inversion of Seafloor Topography in the Gulf of Mexico Based on Convolutional Neural Network Integrated With Multisource Gravity Data and Bathymetric Data L. Wei et al.
- Recovering bathymetry from altimetry-derived gravity data using a novel inversion framework by considering nonlinear effects of seafloor topography S. Wang et al.
- Trans-UNet Network for Predicting Bathymetry in South China Sea From Gravity and Geological Data S. Zhou et al.
- Investigations on the contribution of airborne gravity measurements in the prediction of bathymetry: a case study in the Puerto Rico region X. Chen et al.
- Enhanced Seafloor Topography Inversion Using an Attention Channel 1D Convolutional Network Based on Multiparameter Gravity Data: Case Study of the Mariana Trench Q. Wang et al.
- Bathymetry of the Philippine sea with convolution neural network from multisource marine geodetic data J. Guo et al.
- Nonlinear black-box approaches and data fusion for ocean bathymetry modeling in south Iran M. Mohammad et al.
- Uncertainty analysis of bathymetry inversion in the South China Sea: a comparison of deep learning and Bayesian approaches S. Zhou et al.
- SYSU_Topo: a 1-arc-minute global bathymetry from SWOT-derived gravity using the gravity-geological method W. Feng et al.
- Quantitative validation of ICESat-2 ATL24 in shallow marine environments using reference-grade bathymetric data G. Dandabathula et al.
Saved (final revised paper)
Latest update: 10 May 2026
Short summary
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′.
Our research focuses on using machine learning to enhance the accuracy and efficiency of...
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