Improving the accuracy of bathymetry using the combined neural network and gravity wavelet decomposition method with altimetry derived gravity data

Yongjin Sun; Wei Zheng; Zhaowei LiZhiquan ZhouXiaocong ZhouZhongkai Wen

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Improving the accuracy of bathymetry using the combined neural network and gravity wavelet decomposition method with altimetry derived gravity data

机译：Improving the accuracy of bathymetry using the combined neural network and gravity wavelet decomposition method with altimetry derived gravity data

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摘要

The wide range of bathymetry models can be estimated using the marine gravity information derived from satellite altimetry. However, due to nonlinear factors influences such as isostasy effects, the bathymetry estimated by gravity anomaly and vertical gravity gradient is not satisfactory. Therefore, to improve the accuracy of bathymetry estimation, a combined neural network and gravity information wavelet decomposition (CNNGWD) method is proposed based on wavelet decomposition and correlation analysis. Next, the bathymetry of the Manila Trench area is estimated using the CNNGWD method and multilayer neural network (MNN) method, respectively. Then, the shipbome sounding data and international bathy-metric models such as ET0P01 and GEBCO_2021 are separately used to evaluate the accuracy of the inversion -models. The results show that the root mean square errors (RMSE) of the difference between the bathymetric model one (BM1) estimated by CNNGWD method and the shipborne sounding data is 59.90 m, the accuracy is improved by 12.45, 64.70 and 28.68 compared with the bathymetric model two (BM2) which estimated by MNN, ET0P01 and GEBCO, respectively. Finally, by analyzing the bathymetric accuracy shift with depth, the BM1 has lower RMSE at depths ranging from 1000 m to 3000 m. Furthermore, BM1 shows dominance in flat troughs and rugged ridge regions.

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来源
《Marine geodesy》 |2023年第3期|271-302|共32页
作者
Yongjin Sun; Wei Zheng; Zhaowei LiZhiquan ZhouXiaocong ZhouZhongkai Wen;
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作者单位

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China, China Academy of Aerospace Science and Innovation, China Aerospace Science and Technology Corporation, Beijing, China;

China Academy of Aerospace Science and Innovation, China Aerospace Science and Technology Corporation, Beijing, China;

Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing, ChinaSchool of Information Science and Engineering, Harbin Institute of Technology, Weihai, ChinaChina Academy of Aerospace Science and Innovation, China Aerospace Science and Technology Corporation, Beijing, China, School of Automation, Nanjing University of Science and Technology, Nanjing, ChinaInstitute of Remote Sensing Satellite, China Academy of Space Technology, Beijing, China;

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正文语种英语
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Bathymetric model; CNNGWD method; marine gravity anomaly; satellite altimetry; vertical gravity gradient;

Improving the accuracy of bathymetry using the combined neural network and gravity wavelet decomposition method with altimetry derived gravity data

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