Abstract
Using an optimal duty cycle (DC) can extend the length of recording deployments without greatly reducing the information collected and is critical for measurements in dynamic, remote environments like glacierized fjords. The DC selection is often based on the recording system (battery life/memory) rather than the information in the data. We propose an improved, data-informed method to determine the optimal DC. Fifty-two hours of ambient acoustic data were collected from two underwater vertical line arrays moored near Hubbard and Turner Glaciers in Southeast Alaska in June 2021, prior to a year-long deployment. Two-second-averaged power density spectra were calculated for each of the four channels of both moorings. Spectra were clustered for each recording channel using the k-means clustering algorithm. The process was repeated for a range of DCs from 100% to 1%. Comparisons between clustered prototypes and cluster observations were made between the full and reduced duty cycle (RDC) recordings using 2, 3, 4, 5, and 6 clusters. For DCs greater than 22% (when using four clusters), the information content within the RDC recordings was not significantly enhanced. While this result is specific to this glacierized fjord, we propose this method to optimize DC selection for other passive monitoring applications.
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