Fuzzy Clustering of Oceanographic Sound Speed Profiles for Acoustic Characterization

et al. 2021

IEEE Access

Ocean fronts are mostly determined according to the horizontal temperature gradient, and various selection criteria may lead to different frontal zones. This study proposes a frontal zone delineation method based on sound speed profile. In this paper, the sound speed profile of the Kuroshio intrusion front in the Luzon Strait is classified via fuzzy cluster analysis based on transitive closure and fuzzy C-means algorithm. At the same time, considering that ocean fronts affect sound propagation, we verify the effectiveness of the two algorithms in terms of sound transmission loss. The results show that in different depth ranges, the effectiveness of the two algorithms differs in identifying the frontal zone. At depth below 300 m, the sound speed profile changes drastically, and fuzzy cluster analysis based on the transitive closure is more effective. At 300-700 m depth where the sound speed profile tends to be stable, the fuzzy Cmeans algorithm is more effective. At 700-1000 m depth near the SOFAR axis (the depth of the minimum sound speed), fuzzy cluster analysis based on the transitive closure is more effective. Finally, based on the sound speed profile, we combine both algorithms for the first time to obtain a three-layer geometric optimized structure of the Kuroshio intrusion front in the Luzon Strait. The classification of sound speed profiles via the two fuzzy cluster algorithms provides a new way to judge the frontal zone in different depth ranges and reconstruct geometric models of ocean fronts. INDEX TERMS Fuzzy cluster analysis, ocean front, sound speed profile, transmission loss.

Section: Introductionmentioning

confidence: 96%

Reconstruction Method of Ocean Front Model Based on Fuzzy Cluster Analysis of Sound Speed Profile

et al. 2021

IEEE Access

“…Abiva et al [12] used principal component analysis (PCA) and self-organizing map (SOM) to automatically cluster the SSP, which was applicable in terms of the sea area of the Strait of Gibraltar. In addition, Dubberley and Zingerelli [13] applied fuzzy clustering to oceanographic parameters related to acoustics and divided them into multiple categories, which proved the practicability of the method. As far as the current research is concerned, many studies have applied cluster analysis and other related methods to the classification of sound speed profiles.…”

Section: Introductionmentioning

confidence: 96%

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

et al. 2021

JMSE

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.

“…Abiva et al [13] used principal component analysis (PCA) and self-organizing map (SOM) to automatically cluster the SSP, which was applicable in terms of the sea area of the Strait of Gibraltar. In addition, Dubberley and Zingerelli [14] applied fuzzy clustering to oceanographic parameters related to acoustics and divided them into multiple categories, which proved the practicability of the method. Although the use of various cluster analysis methods to study the SSP has achieved some gratifying results, the necessity of using the high spatial resolution data of the whole sea depth may lead to some inconveniences in the actual ocean exploration [15].…”

Section: Introductionmentioning

confidence: 96%

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

et al. 2021

Applied Sciences

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.