A New Region Ocean Sound Velocity Field Model Considering Variation Mechanisms of Temperature and Salt

The limited availability of in situ ocean observations poses significant challenges to real-time oceanographic applications, particularly in hydroacoustic measurements where accuracy critically depends on the spatiotemporal variability of sound speed. To address the sparsity of sound-speed profile (SSPs), this study proposes an advanced modeling framework for constructing regional sound speed fields by integrating temporal and spatial dynamics. Specifically, the variation mechanisms of temperature and salinity are analyzed, and empirical orthogonal function decomposition is used to extract compact SSP representations. A novel multimodel temporal prediction architecture, combining seasonal-trend decomposition using LOESS, long short-term memory, multivariate unsupervised domain adaptation, and inverted Transformer, captures complex seasonal and adaptive patterns. Meanwhile, spatial modeling adopts a particle swarm optimization least squares support vector machine approach to enhance interpolation across diverse marine environments. Experiments show that the model outperforms existing methods, achieving a root-mean-square error of 0.812 m/s and a mean absolute percentage error of 0.037%. Its robust prediction capability supports accurate multibeam bathymetric processing even without direct SSP observations, confirming its practical value for real-time ocean mapping.