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 dual-channel 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.

中文翻译：

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通过将重力和重力梯度数据与深度学习集成来增强测深预测


本研究旨在通过将重力异常 （GA） 和垂直重力梯度异常 （VGG） 数据与双通道反向传播神经网络 （BPNN） 集成来提高空间分辨率和测深预测的准确性。西太平洋伊豆小笠原海沟的海底地形将使用深度模型和单光束数据进行构建和评估。在没有原位数据的区域，BPNN 使用 1 弧分 SIO 和 GEBCO 深度模型将海底地形预测的准确性分别提高了 0.17% 和 0.35%。当使用单光束数据时，与重力地质法 （GGM） 、 SIO v25.1 和 GEBCO 2023 相比，BPNN 的预测精度分别提高了 64.93%、70.29% 和 68.78%。当单光束、 GA 和 VGG 数据全部合并时，均方根误差 （RMSE） 降低到 19.12 m，与仅使用 GA 或 VGG 数据相比，分别提高了 60.92% 和 61.13%。比较不同深度的测深预测，BPNN 的平均相对误差 （MRE） 低至 0.5%。在海沟区、海山和深海平原等各种地形中，BPNN 预测的海底地形精度分别比 GGM 、 SIO 和 GEBCO 模型提高了 88.36% 、 87.42% 和 84.39% 。这些发现表明，BPNN 可以整合 GA 和 VGG 数据，以提高有和没有原位数据的区域以及不同深度和地形的海底地形的准确性和空间分辨率。本研究为构建高精度全球海底地形提供了新的数据和方法论支持。