The sound speed profile (SSP) reflects the variation in sound speed from the sea surface to the seabed, and its structural changes will have a major impact on sound propagation. There is a mapping link between the ocean’s surface and underwater parameters because of its nonlinear dynamic properties. Accurately inverse SSPs by sea surface remote sensing data use have become a research hotspot in recent years. In this letter, we propose for the first time to use a combination of single empirical orthogonal function (sEOF) and generalized regression neural network (GRNN) to inverse SSPs in the Luzon Strait using satellite remote sensing data, including sea surface temperature anomaly (SSTA), sea level anomaly (SLA), and eddy kinetic energy (EKE). To satisfy training requirements, necessary quality control of the Argo data is performed in the study area. Despite the limited amount of data, sEOF-GRNN can still achieve better inversion results. When sEOF-GRNN is used instead of linear regression (sEOF-R), the performance is improved by more than 50% and the root mean square error (RMSE) is less than 1.5 m/s. Based on Argo-SSPs and inversed SSPs, the differences in sound propagation are analyzed. With the aid of remote sensing data, this study offers theoretical and technical support for the precise inversion of SSPs.

中文翻译：

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单经验正交函数与广义回归神经网络相结合反演吕宋海峡声速剖面

声速剖面（SSP）反映了声速从海面到海底的变化情况，其结构变化将对声音传播产生重大影响。由于其非线性动力学特性，海洋表面和水下参数之间存在映射联系。利用海面遥感数据精确反演SSP已成为近年来的研究热点。在这封信中，我们首次提出使用单一经验正交函数（sEOF）和广义回归神经网络（GRNN）的组合，利用卫星遥感数据，包括海面温度异常（SSTA）反演吕宋海峡的SSP )、海平面异常 (SLA) 和涡动能 (EKE)。为满足训练要求，研究区对Argo资料进行必要的质量控制。尽管数据量有限，sEOF-GRNN仍然可以获得更好的反演结果。当使用sEOF-GRNN代替线性回归（sEOF-R）时，性能提高了50％以上，并且均方根误差（RMSE）小于1.5 m/s。基于Argo-SSP和反演SSP，分析了声音传播的差异。本研究借助遥感数据，为SSP的精确反演提供理论和技术支持。