Distributed Acoustic Sensing (DAS) is an optical sensing technology increasingly used in ocean acoustics to measure underwater sound by transforming fiber optic cables into dense arrays of sensors, enabling high-resolution, kilometer-scale acoustic recordings. In this work, theoretical frameworks are used to analyze how temporal and spatial sampling parameters, such as gauge length, channel spacing, and sampling rate, affect signal-to-noise ratio (SNR) and signal detectability in DAS systems. These concepts are examined using real submarine DAS data collected in coastal marine environments, and key trade-offs between spatial resolution, sensitivity, and computational efficiency are explored through both modeling and data analysis. Signal processing techniques are applied to enhance detectability in noisy conditions, including beamforming and coherent averaging across spatial apertures. Beamforming methods are employed to estimate the direction and location of underwater acoustic sources, and their utility is demonstrated in the detection and localization of both anthropogenic (ship noise) and biological (marine mammal vocalizations) signals. This work illustrates the growing potential of DAS for broad-scale, high-resolution ocean acoustic sensing.
Shima Abadi (2025) studied this question.