文献类型：期刊文献

英文题名：Enhanced Inversion of Sound Speed Profile Based on a Physics-Inspired Self-Organizing Map

作者：Xu, Guojun[1];Qu, Ke[2];Li, Zhanglong[1];Zhang, Zixuan[2];Xu, Pan[1];Gao, Dongbao[1];Dai, Xudong[3]

机构：[1]Natl Univ Def Technol, Coll Meteorol & Oceanog, Changsha 410073, Peoples R China;[2]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[3]92677 Troops PLA, Qingdao 266000, Peoples R China

年份：2025

卷号：17

期号：1

外文期刊名：REMOTE SENSING

收录：SCI-EXPANDED(收录号：WOS:001393622200001)、、EI(收录号：20250217670694)、Scopus(收录号：2-s2.0-85214477426)、WOS

基金：This research was funded by the Open fund of the National Key Laboratory of Science and Technology on Underwater Acoustic Antagonizing, grant number JCKY2024207CH07.

语种：英文

外文关键词：sound speed profile; self-organizing map; physics-inspired clustering

外文摘要：The remote sensing-based inversion of sound speed profile (SSP) enables the acquisition of high-spatial-resolution SSP without in situ measurements. The spatial division of the inversion grid is crucial for the accuracy of results, determining both the number of samples and the consistency of inversion relationships. The result of our research is the introduction of a physics-inspired self-organizing map (PISOM) that facilitates SSP inversion by clustering samples according to the physical perturbation law. The linear physical relationship between sea surface parameters and the SSP drives dimensionality reduction for the SOM, resulting in the clustering of samples exhibiting similar disturbance laws. Subsequently, samples within each cluster are generalized to construct the topology of the solution space for SSP reconstruction. The PISOM method significantly improves accuracy compared with the SOM method without clustering. The PISOM has an SSP reconstruction error of less than 2 m/s in 25% of cases, while the SOM method has none. The transmission loss calculation also shows promising results, with an error of only 0.5 dB at 30 km, 5.5 dB smaller than that of the SOM method. A physical interpretation of the neural network processing confirms that physics-inspired clustering can bring better precision gains than the previous spatial grid.