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Application of Dual-Channel BP Neural Network in Bathymetric Prediction in the Gulf of Guinea
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Hao LIU1, 2, Jiajia YUAN1, 2, *, Chen YANG1, Di DONG2, 3, Daocheng YU4, Zhendong WU5
Journal of Geodesy and Geodynamics | 2025, 45(10) : 1065 - 1070
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Journal of Geodesy and Geodynamics | 2025, 45(10): 1065-1070
Application of Dual-Channel BP Neural Network in Bathymetric Prediction in the Gulf of Guinea
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Hao LIU1, 2, Jiajia YUAN1, 2, *, Chen YANG1, Di DONG2, 3, Daocheng YU4, Zhendong WU5
Affiliations
  • 1 School ofSpatial Information and Geomatics Engineering, Anhui University of Science and Technology, 168 Taifeng Road, Huainan 232001, China
  • 2 Key Laboratory of Marine Environmental Survey Technology and Application, MNR, 155 West-Xingang Road, Guangzhou 510300, China
  • 3 South China Sea Institute of Planning and Environmental Research, SOA, 353 Mid-Xingang Road, Guangzhou 510300, China
  • 4 School of Geomatics, Liaoning Technical University, 88 Yulong Road, Fuxin 123000, China
  • 5 School of Geography and Planning, Chizhou University, 199 Muzhi Road, Chizhou 247000, China

    • LIU Hao, majors in seafloor topography inversion, E-mail:

About Author:

LIU Hao, majors in seafloor topography inversion, E-mail:

Published: 2025-10-15 doi: 10.14075/j.jgg.2024.09.421
Outline
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We propose a bathymetric prediction method based on a dual-channel BP neural network, integrate gravity anomaly data(including long-wave, short-wave, and residual gravity anomalies) and shipborne bathymetry data to predict seafloor topography in the gulf of Guinea. The prediction results are compared with those obtained from gravity geology methods and the GEBCO_2023 and Topo_25.1 models. The results show that dual-channel BP neural network-based inversion achieves superior accuracy compared to other models. This study can provide new insights into applying deep learning in marine geodesy and enhances the accuracy of seafloor topography inversion.

BP neural network  /  seafloor topography  /  gravity anomalies  /  GEBCO_2023  /  Topo_25.1
刘浩, 袁佳佳, 杨晨, 董迪, 于道成, 仵振东. 双通道BP神经网络在几内亚湾海底地形反演中的应用. 大地测量与地球动力学, 2025 , 45 (10) : 1065 -1070 . DOI: 10.14075/j.jgg.2024.09.421
Hao LIU, Jiajia YUAN, Chen YANG, Di DONG, Daocheng YU, Zhendong WU. Application of Dual-Channel BP Neural Network in Bathymetric Prediction in the Gulf of Guinea[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (10) : 1065 -1070 . DOI: 10.14075/j.jgg.2024.09.421
Funding
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  • Open Fund of Key Laboratory of Marine Environmental Survey Technology and Application, MNR(MESTA-2022-B002)
  • Science Foundation for High-Level Talents of Anhui University of Science and Technology(2022yjrc66)
Appendix
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Year 2025 volume 45 Issue 10
38
18
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Article Info
doi: 10.14075/j.jgg.2024.09.421
  • Receive Date：2024-09-05
  • Online Date：2026-10-29
  • Published：2025-10-15
Article Data
Affiliations
History
  • Received：2024-09-05
Funding
Open Fund of Key Laboratory of Marine Environmental Survey Technology and Application, MNR(MESTA-2022-B002)
Science Foundation for High-Level Talents of Anhui University of Science and Technology(2022yjrc66)
Affiliations
    1 School ofSpatial Information and Geomatics Engineering, Anhui University of Science and Technology, 168 Taifeng Road, Huainan 232001, China
    2 Key Laboratory of Marine Environmental Survey Technology and Application, MNR, 155 West-Xingang Road, Guangzhou 510300, China
    3 South China Sea Institute of Planning and Environmental Research, SOA, 353 Mid-Xingang Road, Guangzhou 510300, China
    4 School of Geomatics, Liaoning Technical University, 88 Yulong Road, Fuxin 123000, China
    5 School of Geography and Planning, Chizhou University, 199 Muzhi Road, Chizhou 247000, China
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表12种不同金属材料的力学参数
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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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