Ocean Front Reconstruction Method Based on K-Means Algorithm Iterative Hierarchical Clustering Sound Speed Profile

As one of the most common mesoscale phenomena in the ocean, the ocean front is defined as a narrow transition zone between two water masses with obviously different properties. In this study, we proposed an ocean front reconstruction method based on the K-means algorithm iterative hierarchical clustering sound speed profile (SSP). This method constructed the frontal zone from the perspective of SSP. Meanwhile, considering that acoustic ray tracing is a very sensitive tool for detecting the location of ocean fronts because of the strong dependence of the transmission loss (TL) on SSP structure, this paper verified the feasibility of the method from the perspective of the TL calculation. Compared with other existing methods, this method has the key step of iterative hierarchical clustering according to the accuracy of clustering results. The results of iterative hierarchical clustering of the SSP can reconstruct the ocean front. Using this method, we reconstructed the ocean front in the Gulf Stream-related sea area and obtained the three-dimensional structure of the Gulf Stream front (GSF). The three-dimensional structure was divided into seven layers in the depth range of 0-1000 m. Iterative hierarchical clustering SSP by K-means algorithm provides a new method for judging the frontal zone and reconstructing the geometric model of the ocean front in different depth ranges.

Automated summary

This study proposed a method for ocean front reconstruction based on sound speed profiles, which was applied to reconstruct the Gulf Stream front in a related sea area. The method utilizes iterative hierarchical clustering of sound speed profiles for judging frontal zones in different depth ranges.