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Automatic Classification of Sound Speed Profiles using PCA and Self-Organizing Map techniques

An understanding of how sound travels is necessary to gain knowledge of an underwater environment. However, variability in the underwater domain makes it difficult to get a reliable categorization of the field in a timely manner, impacting naval operations such as Mine Countermeasures (MCM) and Anti-Submarine Warfare (ASW). In order to better understand the underwater environment, this paper proposes an automated method of categorizing an underwater environment over time according to its Sound Speed Profile (SSP). Although several feature extraction methods are available, these methods are often computationally expensive. Motivated by unsupervised machine learning techniques, clustering methods will be used to categorize an Area of Interest (AOI). In particular, this paper proposes the Kohonen Self-Organizing Map (SOM) clustering method initialized by Principal Component Analysis (PCA) to quickly categorize a region using SSPs. Further, these methods will be applied to the Strait of Gibraltar region. As the boundary between the Atlantic Ocean and the Mediterranean Sea, this region represents an area where the variability of the SSP depends upon which region the data were extracted. The proposed method will be used to analyze the data from each area over time and space to characterize an underwater environment.