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

Viewed

Total article views: 3,434 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,587 447 400 3,434 74 123
  • HTML: 2,587
  • PDF: 447
  • XML: 400
  • Total: 3,434
  • BibTeX: 74
  • EndNote: 123
Views and downloads (calculated since 28 Aug 2024)
Cumulative views and downloads (calculated since 28 Aug 2024)

Viewed (geographical distribution)

Total article views: 3,434 (including HTML, PDF, and XML) Thereof 3,390 with geography defined and 44 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 17 Jan 2026
Download
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′.
Read more
Share
Altmetrics
Final-revised paper
Preprint