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
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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Total article views: 3,971 (including HTML, PDF, and XML)
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Total article views: 2,729 (including HTML, PDF, and XML)
Thereof 2,729 with geography defined
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Total article views: 1,242 (including HTML, PDF, and XML)
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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′.
Our research focuses on using machine learning to enhance the accuracy and efficiency of...
