Goestchel Quentin, Wilcock William S D, Abadi Shima
School of Oceanography, University of Washington, Seattle, Washington 98195, USA.
J Acoust Soc Am. 2025 May 1;157(5):3655-3666. doi: 10.1121/10.0036696.
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.
检测和定位海洋哺乳动物对于了解它们的行为以及支持保护工作至关重要。声学方法是对视觉调查和标记的补充,而视觉调查和标记在空间和时间覆盖范围上往往存在限制。由于长须鲸具有典型的20赫兹发声,因此它们特别适合进行声学监测。分布式声学传感(DAS)为水听器数据提供了一个很有前景的补充,它使用光纤电缆作为传感器,可对长达约100公里的距离进行连续、高分辨率的监测。2021年11月,利用海洋观测倡议区域电缆阵列收集了一个DAS数据集,获取了有关长须鲸发声的宝贵数据。该数据集包括来自两条电缆的测量数据,通道间距为2米,跨度为65 - 95公里。本研究评估了各种方法,包括信噪比估计、匹配滤波、加博尔滤波和噪声包络减法,用于增强和去除DAS数据中的长须鲸叫声噪声。一种将匹配滤波和包络减法相结合的方法在检测甚至低信噪比的长须鲸叫声并获取到达时间方面最为有效。总体而言,本研究突出了DAS阵列处理在显著提高信噪比和增强监测长须鲸的检测能力方面的潜力。