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ORIGINAL RESEARCH article

Front. Mar. Sci.
Sec. Ocean Observation
Volume 11 - 2024 | doi: 10.3389/fmars.2024.1520401

Enhancing bathymetric prediction by integrating gravity and gravity gradient data with deep learning

Provisionally accepted
Junhui Li Junhui Li 1Nengfang Chao Nengfang Chao 1Houpu Li Houpu Li 2*Gang Chen Gang Chen 1*Shaofeng Bian Shaofeng Bian 2*Zhengtao Wang Zhengtao Wang 3Aoyu Ma Aoyu Ma 1*
  • 1 China University of Geosciences Wuhan, Wuhan, China
  • 2 Naval University of Engineering, Wuhan, China
  • 3 Wuhan University, Wuhan, Hubei Province, China

The final, formatted version of the article will be published soon.

    This study aims to enhance the spatial resolution and accuracy of bathymetric prediction by integrating Gravity Anomaly (GA) and Vertical Gravity Gradient Anomaly (VGG) data with a dualchannel Backpropagation Neural Network (BPNN). The seafloor topography of the Izu-Ogasawara Trench in the Western Pacific will be constructed and evaluated using depth models and single-beam data. The BPNN improved the accuracy of seafloor topography prediction by 0.17% and 0.35% using the 1 arc-minute SIO and GEBCO depth models, respectively, in areas without in-situ data. When single-beam data was utilized, the BPNN improved prediction accuracy by 64.93%, 70.29%, and 68.78% compared to the Gravity Geological Method (GGM), SIO v25.1, and GEBCO 2023, respectively. When single-beam, GA, and VGG data were all combined, the root mean square error (RMSE) was reduced to 19.12 m, representing an improvement of 60.92% and 61.13% compared to using only GA or VGG data, respectively. Comparing bathymetric predictions at different depths, the BPNN achieved a mean relative error (MRE) as low as 0.5%. Across various terrains-such as trench areas, seamounts, and deep-sea plains-the accuracy of seafloor topography predicted by the BPNN improved by 88.36%, 87.42%, and 84.39% compared to GGM, SIO and GEBCO depth models, respectively. These findings demonstrate that BPNN can integrate GA and VGG data to enhance both the accuracy and spatial resolution of seafloor topography in regions with and without in-situ data, and across various depths and terrains. This study provides new data and methodological support for constructing high-precision global seafloor topography.

    Keywords: seafloor topography, BPNN, Gravity anomaly, Vertical gravity gradient, Izu-Ogasawara Trench, deep learning

    Received: 31 Oct 2024; Accepted: 30 Nov 2024.

    Copyright: © 2024 Li, Chao, Li, Chen, Bian, Wang and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Houpu Li, Naval University of Engineering, Wuhan, China
    Gang Chen, China University of Geosciences Wuhan, Wuhan, China
    Shaofeng Bian, Naval University of Engineering, Wuhan, China
    Aoyu Ma, China University of Geosciences Wuhan, Wuhan, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.