文献类型：期刊文献

英文题名：An Enhanced Approach for Sound Speed Profiles Inversion Using Remote Sensing Data: Sample Clustering and Physical Regression

作者：Zhang, Zixuan[1];Qu, Ke[1];Li, Zhanglong[2]

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

年份：2025

卷号：14

期号：14

外文期刊名：ELECTRONICS

收录：SCI-EXPANDED(收录号：WOS:001539825900001)、、EI(收录号：20253018863001)、Scopus(收录号：2-s2.0-105011605397)、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; ocean normal mode; inversion; South China Sea

外文摘要：Sound speed profile (SSP) inversion based on remote sensing parameters allows for the acquisition of global quasi-real-time SSPs without the need for on-site measurements, thereby fulfilling the requirements of many acoustic applications. This study makes two enhancements to the single empirical orthogonal function regression (SEOF-R) method. First, the k-means clustering algorithm is utilized to cluster SSP samples, ensuring the consistency of perturbation modes in the physical regression. Second, baroclinic modes are employed to derive a novel SSP basis function, named the ocean mode basis, which accurately characterizes the inversion relationship. Validation experiments using data from the South China Sea yield promising results. Compared with the SEOF-R method, the reconstruction error of the improved approach is reduced by 27%, with an average reconstruction error of 1.73 m/s. The average prediction transmission loss error decreases by 70%, reaching 1.29 dB within 50 km. The grid-free processing and low sample dependence of the proposed method further enhance the applicability and accuracy of remote sensing-based SSP inversion.