文献类型：期刊文献

英文题名：Wide-area sound speed profile estimation based on a pre-classification scheme for sound speed perturbation modes

作者：Liu, Chen[1];Qu, Ke[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang, Peoples R China

年份：2023

卷号：10

外文期刊名：FRONTIERS IN MARINE SCIENCE

收录：SCI-EXPANDED(收录号：WOS:000946508600001)、、Scopus(收录号：2-s2.0-85149820724)、WOS

基金：This research was funded by the Natural Science Foundation of Guangdong Province, grant number No.2022A1515011519 and funded by the Innovation Training Funding Project for College Students, grant number No.S202210566001.

语种：英文

外文关键词：sound speed profile; K-means; self-organizing map; South China Sea; empirical orthogonal function

外文摘要：IntroductionThe trend of sound speed profile (SSP) inversion is towards wide-area sound speed estimation. However, the traditional inversion method of dividing the latitude and longitude grids has limitations in terms of significantly lower accuracy when samples are lacking. k-means clustering algorithm (K-means) can divide the training class to achieve high accuracy estimation. MethodThis paper proposes a grid-free pre-classification inversion scheme based on empirical orthogonal function (EOF) vectors. The scheme is based on the K-means to classify the samples according to the perturbation mode of the SSP. After classification, the SSP inversion is carried out using the self-organizing map algorithm (SOM). The experimental sea area is selected from the South China Sea, and the inversion results are evaluated using root mean square error (RMSE) as the criterion. ResultThe inversion results show that the inversion error is 2.1 m/s for the pre-classification solution and 2.7 m/s for the solution without pre-classification, a steady improvement of more than 20% in the inversion error. Accuracy is also improved by 2.14 m/s in the depth range where the sound speed perturbance is greatest. DiscussionThis pre-classification scheme has smaller inversion errors and the classification results are reasonable in terms of distribution in time and space. It provides a feasible solution for SSP inversion in sea areas where samples are lacking.