Distributed acoustic sensing (DAS) is a technique that measures strain changes along an optical fiber to distances of ∼100 km with a spatial sensitivity of tens of meters. In November 2021, 4 days of DAS data were collected on two cables of the Ocean Observatories Initiative Regional Cabled Array extending offshore central Oregon. Numerous 20 Hz fin whale calls, northeast Pacific blue whale A and B calls, and ship noises were recorded, highlighting the potential of DAS for monitoring the ocean. The data are publicly available to support studies to understand the sensitivity of submarine DAS for low-frequency acoustic monitoring.

Beamforming includes a variety of spatial filtering techniques that may be used for determining sound source locations from near-field sensor array recordings. For this scenario, beamforming resolution depends on the acoustic frequency, array geometry, and target location. Random scattering in the medium between the source and the array may degrade beamforming resolution with higher frequencies being more susceptible to degradation. The performance of frequency-sum (FS) beamforming for reducing such sensitivity to mild scattering while increasing resolution is reported here. FS beamforming was used with a data-dependent [minimum variance (MV)] or data-independent (delay-and-sum, DAS) weight vector to produce higher frequency information from lower frequency signal components via a quadratic product of complex signal amplitudes. The current findings and comparisons are based on simulations and passive cavitation imaging experiments using 3 MHz and 6 MHz emissions recorded by a 128-element linear array. FS beamforming results are compared to conventional DAS and MV beamforming using four metrics: point spread function (PSF) size, axial and lateral contrast, and computation time. FS beamforming produces a smaller PSF than conventional DAS beamforming with less computation time than MV beamforming in free space and mild scattering environments. However, it may fail when multiple unknown sound sources are present.

Extensive monitoring of acoustic activities is important for many fields, including biology, security, oceanography, and Earth science. Distributed acoustic sensing (DAS) is an evolving technique for continuous, wide-coverage measurements of mechanical vibrations, which is suited to ocean applications. DAS illuminates an optical fiber with laser pulses and measures the backscattered wave due to small random variations in the refractive index of the material. External stimuli, such as mechanical strain due to acoustic wavefields impinging on the fiber-optic cable, modulate the backscattered wave. Continuous measurement of the backscattered signal provides a distributed sensing modality of the impinging wavefield. Considering the potential use of existing telecommunication fiber-optic cables deployed across the oceans, DAS has emerged as a promising technology for monitoring the underwater soundscape. This review presents advances in DAS in the last decade and details the underlying physics from electromagnetic to mechanical and eventually acoustic quantities. To guide the use of DAS for ocean applications, the effect of DAS acquisition parameters in signal processing is explained. Finally, DAS is demonstrated on data from the OOI Regional Cabled Array for the detection of sound sources, such as whales, ships, and earthquakes.

Detecting and locating marine mammals is essential for understanding their behavior and supporting conservation efforts. Acoustic methods complement visual surveys and tagging, which are often limited in spatial and temporal coverage. Fin whales are particularly suited for acoustic monitoring due to their stereotypical 20 Hz vocalizations. Distributed Acoustic Sensing (DAS) offers a promising addition to hydrophone data, using fiber-optic cables as sensors for continuous, high-resolution monitoring over distances up to about 100 km. In November 2021, a DAS dataset was collected using the Ocean Observatories Initiative Regional Cabled Array, capturing valuable data on fin whale vocalizations. This dataset includes measurements from two cables with 2 m channel spacing, spanning 65–95 km. This study evaluates various approaches—including signal-to-noise ratio estimation, matched filtering, Gabor filtering, and noise envelope subtraction—for enhancing and denoising fin whale calls in DAS data. A method that combines matched filtering and envelope subtraction is most effective at detecting even low SNR fin whale calls and obtaining arrival times. Overall, this study highlights the potential of DAS array processing to significantly improve signal-to-noise ratios and enhance detection capabilities for monitoring fin whales.

Long-term noise interferometry analysis is conducted over six years of data using two hydrophones on the Ocean Observatories Initiative Cabled Array. The two hydrophones are separated by 3.2 km and are bottom-mounted at 1500 m. We demonstrate the ability of ambient noise interferometry to reliably detect multi-path arrivals in the deep ocean from bottom-mounted hydrophones. An analysis of the multi-path arrival peak emergence is presented, as well as long-term trends of the signal-to-noise ratio of the arrival peaks. Last, we show that long-term ambient noise interferometry provides the opportunity for monitoring directional, coherent ambient sound such as the fin whale chorus.

Marine seismic reflection surveys use airguns to generate repetitive high energy sound signals to image the structure of the seafloor. To better mitigate against the impact of airgun pulses on marine mammals, safety criteria are defined to ensure marine mammals are not exposed to high levels of acoustic energy. Accurate prediction of the sound received levels away from the airguns is required for conducting effective marine mammal monitoring. In this study, measurements by a horizontal hydrophone array towed by the R/V Marcus G. Langseth behind a seismic source array have been used to characterize short-range propagation of airgun pulses and predict the acoustic energy radiated from a seismic source. Data from the Cascadia Open-Access Seismic Transects seismic reflection survey are used to train a linear regression (LR) and a random forest (RF) model to estimate sound exposure levels (SELs) in short ranges from the airguns. Results show that the LR model does not account for all the variance in data. However, the RF model is able to estimate the SELs with a high coefficient of determination and a low mean squared error. Results from the LR model show that the rate at which SELs decrease in deep water does not match either of the cylindrical or spherical spreading models. Simulations are undertaken to understand this inconsistency as well as the effect of hydrophone group-averaging on data recorded by a seismic hydrophone array.