## 1 Introduction
However, in the South China Sea, where dynamic characteristics are complex, a single linear relationship cannot effectively explain the connections between dynamic features, sea surface parameters, and the sound speed profile, which in turn leads to a decline in inversion accuracy. The submarine landform of the South China Sea presents a typical three-layer ring structure, which is formed of a continental shelf-continental slope-central sea basin structure that spans from the periphery to the center, manifesting as a diamond-shaped distribution [16]. This unique submarine landform is completely different from the symmetrical spreading mid-ocean ridge of the Atlantic Ocean or the linear trench-arc system of the Pacific Ocean, reflecting the composite tectonic background of continental margin extension-subduction-collision, with significant mesoscale characteristics, in the South China Sea [17]. The South China Sea has a rare deep water mass active invasion mechanism, whereby deep circulation exchanges water with the Pacific Ocean through a \"deep sea waterfall\" (water depth of 2500 m) in the Luzon Strait [18; 19; 20]. The unique geomorphic structure and hydrodynamic characteristics of the South China Sea bring challenges to SSP inversion. At the same time, due to the difficulty of data collection, politics, economics, and other reasons, SSP samples of the South China Sea are scarce, which limits the accuracy of the statistical-based SSP inversion method. Considering the impact of the unique disturbance characteristics of the South China Sea on statistical methods, which causes errors in sound speed profile estimation, this paper uses South China Sea Argo data and the EOF to extract the main disturbance modes, and carries out cluster analysis on the main disturbance modes to discuss the acoustic stability characteristics of the sea. On the basis of clustering, a regression relationship is established based on Argo profiles and SST and SL data to inverse SSPs. A sound field simulation is conducted to verify the effectiveness of the proposed method in terms of transmission loss, and then the Argo float data from 2018 are used to verify the SSP accuracy. The main contributions of this paper are as follows:  
* Proposed a new method for extracting disturbance features and pre-clustering acoustic stability, which effectively solves the problem of uneven distribution in sample division by traditional grids. * Based on the random distribution characteristics of the Empirical Orthogonal Function (EOF) in the South China Sea, revealed significant differences in dynamic features among different clustering categories, provided a new perspective for acoustic stability analysis, and further improved the accuracy of sound speed profile inversion in the South China Sea region. * Through the correlation analysis between statistical clustering and physical mechanisms, clarified the rationality of clustering results as key factors affecting acoustic stability. The method for extracting disturbance features and acoustic stability pre-clustering is presented in Section 2, also describes the data used in the study; Sections 3 and 4 respectively present the research results of acoustic stability pre-clustering and the conclusions. Compared with traditional single machine learning methods, this study introduces a disturbance mechanism to further improve the estimation accuracy of sound speed profiles. From the perspective of dynamic characteristics, the sEOF-r method is constrained to solve the problem of low inversion accuracy in local areas. Through the above improvements, the requirements for the accuracy and coverage of sound speed profiles in the actual marine environment perception process can be met.