Use of noise correlation matrices to interpret ocean ambient noise

Oceanic ambient noise is a dynamic mixture of biologic, geophysical, and anthropogenic sound sources. A goal of research is to put some order in this cacophony of information, understand the received spectral content and determine the primary contributors to the ambient noise. This paper compares three methods to assist in that process (with emphasis on noise correlation techniques): noise correlation matrices, manual selection of noise spectra, and principal component analysis. Comparison followed a common process: selection of a replica set (best termed a characteristic subset of noise spectra), which are used to recreate the original noise field for comparison and consequent decision as to whether that replica set represented the noise measurements adequately. Conclusions of this study are (1) noise correlation matrices provide the best definition of the spectra that represent a particular source and offer potential in organizing and identifying specific noise source content. (2) Manual sorting of noise spectra, while able to identify specific events easily, is both labor intensive, given the quantity of data available; and suffers from incorrect interpretation of multiple competing sound sources, when present. (3) Principal component analysis provides the best reconstruction of measured noise, but has difficulty linking components to physical source mechanisms.