海洋测绘2024，Vol.44Issue(4)：12-15,20,5.DOI:10.3969/j.issn.1671-3044.2024.04.003
基于残差神经网络的马里亚纳海沟地形反演
Inversion of mariana trench topography using residual deep neural networks
摘要
Abstract
To improve the accuracy of using gravity data to invert mariana trench topography,this study presents a methodology for bathymetric inversion of the Mariana Trench utilizing Residual Deep Neural Network(RDNN)and gravity anomaly data.The accuracy of the RDNN model is evaluated by the in-situ check point depths,and compared with the Gravity-Geology Method(GGM)model.The results demonstrate that the RDNN provides a more detailed inversion of the Mariana Trench's bathymetry.The root mean square error(RMSE)of the RDNN model is 128.98 m,better than 150.14 m of GGM model,suggesting a better consistency with ship-measured check point depths.The RDNN deep learning model proposed in this study provides a reference for high-precision bathymetric inversion using gravity anomaly data.关键词
重力异常/残差深度神经网络/马里亚纳海沟/短波重力异常/地形特征反演Key words
gravity anomaly/residual deep neural networks/mariana trench/short-wave gravitational anomaly/inversion of topographic features分类
天文与地球科学引用本文复制引用
王永康,张薇,黄令勇,刘鑫仓,杨磊..基于残差神经网络的马里亚纳海沟地形反演[J].海洋测绘,2024,44(4):12-15,20,5.基金项目
国家自然科学基金(41806214). （41806214）
