Reconstruction of Ocean Front Model Based on Sound Speed Clustering and Its Effectiveness in Ocean Acoustic Forecasting

Abstract

As a mesoscale phenomenon of the ocean, the ocean front can directly affect the structural characteristics of sound speed profiles and further affect the acoustic propagation characteristics of the sea area. In this paper, we use the fuzzy C-means (FCM) algorithm to cluster the surface sound speed in the sea area of the Kuroshio Extension (KE) and detect the frontal zone of Kuroshio Extension (KEF). At the same time, the sound speed profile (SSP) is used instead of the temperature profile to establish the model of the sound speed field in the front area of the Kuroshio Extension and to improve the theoretical model of the ocean front. Compared with the actual ocean front calculated by reanalysis data, the root means square error (RSME) of the transmission loss (TL) calculated by the model is controlled below 6 dB, which proves the validity of the model. Finally, we propose the melt function in the model to forecast the depth change of the acoustic convergence area. Compared with the actual calculation result based on reanalysis data, the root means square error (RSME) of the depth forecasting after the frontal zone is 43.3 m. This reconstruction method does not rely on the high spatial resolution data of the whole sea depth and can be of referential significance to acoustic detection in the ocean front environment.

Keywords

• sound speed profile
• fuzzy C-means clustering
• reconstruction method
• ocean front model
• convergence area
• acoustic forecasting