文献类型：期刊文献

英文题名：Estimation of sound speed profiles based on remote sensing parameters using a scalable end-to-end tree boosting model

作者：Ou, Zhenyi[1];Qu, Ke[1];Shi, Min[2];Wang, Yafen[2];Zhou, Jianbo[3]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang, Peoples R China;[2]Unit 91977 Peoples Liberat Army, Beijing, Peoples R China;[3]Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian, Peoples R China

年份：2022

卷号：9

外文期刊名：FRONTIERS IN MARINE SCIENCE

收录：SCI-EXPANDED(收录号：WOS:000900159800001)、、Scopus(收录号：2-s2.0-85144581645)、WOS

基金：Funding This research was funded by the Natural Science Foundation of Guangdong Province, grant number No.2022A1515011519.

语种：英文

外文关键词：sound speed profile; remote sensing data; XGBoost; Argo profiles; the South China Sea

外文摘要：IntroductionIn underwater acoustic applications, the three-dimensional sound speed distribution has a significant impact on signal propagation. However, the traditional sound speed profile (SSP) measurement method requires a lot of manpower and time, and it is difficult to popularize. Satellite remote sensing can collect information on a large ocean surface area, from which the underwater information can be derived. MethodIn this paper, we propose a method for reconstructing the SSP based on an extensible end-to-end tree boosting (XGBoost) model. Combined with satellite remote sensing data and Argo profile data, it extracts the characteristic matrix of the SSP and analyzes the contribution rate of each order matrix to reduce the introduction of noise. The model inverts the SSP above 1000 m in the South China Sea by using the root mean square error (RMSE) as the precision evaluation index. ResultThe results showed that the XGBoost model could better reconstruct the SSP above 1000 m, with a RMSE of 1.75 m/s. Compared with the single empirical orthogonal function regression (sEOF-r) model of the linear regression method, the RMSE of the XGBoost model was reduced by 0.59 m/s. DiscussionFor this model, the RMSE of the inversion results was smaller, the robustness was better, and the regression performance was superior to that of the sEOF-r model at different depths. This study provided an efficient tree boosting model for SSP reconstruction, which could reliably and instantaneously monitor the 3D sound speed distribution.