← Back to Gallery

Session: Filling the Data Gap: Ocean-bottom Sensing with Fiber-optic Cables [Poster]

Type: Poster

Date: 10/9/2024

Time: 07:00 AM

Room: Stanley Park Ballroom

Application of Distributed Acoustic Sensing for Fin Whale Calls Detection and Localization

Detecting and locating marine mammals such as fin whales is critical to understanding their behavior and protecting their habitats. Traditional methods of monitoring using visual observations and tagging are limited in coverage and can be usefully complemented by acoustic methods. Fin whales are well suited for acoustic monitoring because males produce a stereotypical, 1-second, 20-Hz, down-swept chirp that is incorporated into songs associated with breeding and observed in non-song vocalizations in migratory groups. However, the deployment of dedicated hydrophone arrays is expensive. Distributed Acoustic Sensing (DAS) is an emerging technology that shows promise for passive acoustic monitoring of marine mammals in real time. DAS uses fiber optic cables as sensors by exploiting Rayleigh scattering, allowing continuous monitoring over distances of up to ~100 km with an unprecedented resolution of tens of meters. For 4 days in November 2021, a public domain DAS dataset was collected on the two submarine cables of the Ocean Observatories Initiative Regional Cabled Array that extend offshore Pacific City, Oregon. This experiment took place during the fin whale breeding season, and tens of thousands of 20 Hz calls are present in the data. The dataset includes DAS measurements on three fibers extending 65-95 km with 2 m channel spacing. In this study, we demonstrate a scalable example of detection and localization. Different detection techniques have been explored, from classical methods such as matched filtering and spectrogram cross correlation, to image processing, exploiting the new possibilities of DAS time-space representations. Due to the performance of spectrogram correlation, the final detection combines Gabor filters on the recorded wave field envelope and single channel matched filtering. This provides a set of detection times for automated localization using the time difference of arrival method. Scaling these techniques to the full dataset for tracking the whales and inferring their behavior will also be discussed. [Work supported by ONR].

Presenting Author: Quentin

Additional Authors