文献类型：期刊文献

英文题名：Reconstructing the sound speed profile of South China Sea using remote sensing data and long short-term memory neural networks

作者：Zhao, Yu[1];Xu, Pan[1];Li, Guangming[2];Ou, Zhenyi[3];Qu, Ke[3]

机构：[1]Natl Univ Def Technol, Coll Meteorol & Ocean, Changsha, Peoples R China;[2]Innovat Inst Def Technol, Beijing, Peoples R China;[3]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang, Peoples R China

年份：2024

卷号：11

外文期刊名：FRONTIERS IN MARINE SCIENCE

收录：SCI-EXPANDED(收录号：WOS:001234346300001)、、Scopus(收录号：2-s2.0-85194898468)、WOS

基金：The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by the Natural Science Foundation of Guangdong Province under contract No. 2022A151501151.

语种：英文

外文关键词：sound speed profile; remote sensing observation data; long short-term memory; sound speed disturbance; empirical orthogonal function

外文摘要：Introduction Sound waves are refracted along the direction of their propagation owing to spatial and temporal fluctuations in the speed of sound in seawater. Errors are compounded when sound speed profiles (SSPs) with low precision are used to detect and locate distant underwater targets because an accurate SSP is critical for the identification of underwater objects based on acoustic data. Only sparse historical spatiotemporal data on the SSP of the South China Sea are available owing to political issues, its complex atmospheric system, and the unique topography of its seabed, because of which frequent oceanic movements at the mesoscale affect the accuracy of inversion of its SSP.Method In this study, we propose a method for the inversion of the SSP of the South China Sea based on a long short-term memory model. We use continuous-time data on the SSP of the South China Sea as well as satellite observations of the height and temperature of the sea surface to make use of the long-term and short-term memory-related capacities of the proposed model.Result It can achieve highly accurate results while using a small number of samples by virtue of the unique structure of its memory. Compared with the single empirical orthogonal function regression method, the inversion accuracy of this model is improved by 24.5%, and it performed exceptionally well in regions with frequent mesoscale movements.Discussion This enables it to effectively address the challenges posed by the sparse sample distribution and the frequent mesoscale movements of the South China Sea.