Reconstruction of the Sound Speed Profile in Typical Sea Areas Based on the Single Empirical Orthogonal Function Regression Method

The sound speed profile (SSP) is a necessary prerequisite for acoustic field computation and underwater target localization and monitoring. Due to the dynamic nature of the ocean, the reconstruction of SSPs with surface characteristics is a big challenge. In this study, the Single Empirical Orthogonal Function Regression (sEOF-R) method is employed to establish the regression relationship between the surface parameters and the sound speed anomaly profile (SSAP) in three typical sea areas, namely the equator, Kuroshio Extension (KE), and Northeast Pacific. Based on the established regression relationship and the surface parameters, the underwater SSP is reconstructed. Results show that the reconstruction effects in the three areas show the best performance in the Northeast Pacific, followed by the equator and finally the KE. The quantitative analysis suggests that the local sea level anomaly (SLA) plays the dominant role in influencing the reconstruction effect, followed by the sea surface temperature anomaly (SSTA). Further analysis demonstrates that the sEOF-R method is limited in time-varying and space-varying areas. The SSP reconstructed from the sea surface information in this study is useful for the inversion of the underwater structures.

Automated summary

The study uses the Single Empirical Orthogonal Function Regression (sEOF-R) method to establish the regression relationship between surface parameters and sound speed anomaly profiles (SSAP) in three typical sea areas. Based on this relationship and the surface parameters, the underwater sound speed profile (SSP) is reconstructed. The results show that the reconstruction effects are best in the Northeast Pacific, followed by the equator and then the Kuroshio Extension (KE). The study also analyzes the factors influencing the reconstruction effect and concludes that local sea level anomaly (SLA) and sea surface temperature anomaly (SSTA) play important roles.