Analyzing seasonally dependent performance of rain detection from underwater acoustic power spectral density

Rainfall and other natural processes can be empirically monitored by analyzing characteristic spectral features in the ocean’s ambient sound. Previous work to detect and estimate rainfall from passive underwater acoustics used linear transformations of these spectral features; Ma and Nystuen (2005) measured acoustic power at a few narrowband frequencies, later extended by Mallary et al. (2023) and Berg (2023) to principal component analysis (PCA), which represents broadband spectra with a small number of linear coefficients. This research proposes a new broadband detection scheme that constructs separate PCA subspaces by rain and season. Separating the linear transformations before training produces subspaces more finely tuned for each class. PSDs are computed using Welch’s method and are separated into dry (<2.4 mm/h) and rainy (>2.4 mm/h) recordings for each season. A linear dimension reduction matrix is defined for the dry PSDs of each season using eigenvectors of their covariance matrix (the principal components), while preserving over 98% of variance. Rainfall can then be detected using a likelihood ratio test of dimension-reduced PSDs for each season. Performance varies substantially by season and wind conditions, with detection ranging from 30% to 80% seasonally at a 1% false alarm rate. Accounting for wind may improve rainfall detection and, more generally, monitoring of natural processes from underwater acoustic recordings. [Work supported by SMART Scholarship and ONR/MUST.]