基于遥感数据和表层声速的全海深声速剖面反演

海洋声速剖面严重影响着水下声传播特性，近实时地获取声速剖面对水下声通信、水下定位、鱼群探测等都有重要意义。单经验正交函数回归（single Empirical Orthogonal Function regression，sEOF-r）方法通过建立声速剖面的经验正交系数与海面遥感数据之间的线性回归关系来反演声速剖面。但是，海洋是一个复杂的动力系统，声速与海面遥感数据并不是简单的线性关系，因此，本文基于Argo历史网格数据，通过自组织映射（Self-Organizing Map，SOM）生成海平面高度异常（Sea Level Anomaly，SLA）、海表面温度（Sea Surface Temperature，SST）等海表遥感数据以及表层声速仪测量的表层声速与声速剖面异常之间的非线性映射；然后利用近实时的海表遥感数据和表层声速反演三维海洋声速场。声速剖面反演的结果表明，在多源信息融合的优势下，本文方法的反演性能最稳定且精度最高，声速剖面的平均反演精度比经典sEOF-r方法提高约2 m/s，比未考虑表层声速的经典SOM方法提高约1 m/s。

Inversion of the full-depth sound speed profile based on remote sensing data and surface sound speed

The ocean sound speed profile (SSP) determines the underwater acoustic propagation, and it is very important to obtain SSP in near real-time for underwater acoustic communication, positioning, and fish detecting. The single Empirical Orthogonal Function regression (sEOF-r) method inverts the SSP by establishing a linear regression relationship between the empirical orthogonal coefficient of the SSP and the sea surface remote sensing data. However, the ocean is a complex dynamical system, and the SSP and the remote sensing data are not simple linear. Therefore, based on the Argo historical gridded dataset, self-organizing map (SOM) was used to establish the nonlinear mapping between sea surface data, such as sea level anomaly (SLA), sea surface temperature (SST) and surface sound speed measured by surface velocimeter, and SSP anomaly. The three-dimensional sound speed field is then inverted by the near real-time remote sensing data and the surface sound speed. The results of the SSP inversion showed that, under the advantage of multi-source information fusion, the algorithm generated a smaller inversion error than linear inversion and had better robustness. It improved the average accuracy of inversion by about 2 m/s than sEOF-r method, and improved by about 1 m/s than classical SOM method that without considering the surface sound speed.