The acoustics of climate change - long-term observations in the arctic oceans

北极环境正在经历加速的气候变化；空气和海洋温度的升高有助于减少海冰的范围，这对于通过冰反照率反馈调节地球气候至关重要。随着冰层覆盖的减少，更多的太阳辐射被海水吸收，导致了冰形成减少、海平面上升和南方更不稳定的天气事件的因果序列。北极海冰的减少也为新的航运路线和海上基础设施的发展开辟了水域，这进一步影响了海洋生态系统，污染了海底声景。对于许多海洋物种来说，巨大的脉冲声和人为活动带来的环境噪音都是主要的压力源，尤其是那些利用北极理想条件进行长途通信的鲸鱼。该项目旨在利用被动声监测（PAM）技术监测气候变化对北极环境的影响。可以连续测量北极海洋的声景，捕捉来自船舶、海冰过程和动物发声的声音，以及环境噪声水平的变化。研究这些源如何随着时间的推移对该地区的声景做出贡献，将对该地区的气候变化速度提供大量信息。虽然许多水下来源的声学特征都有很好的记录，但机器学习有新的机会来自动识别声学事件。将开发机器学习技术，从录音中分离单个声源，进行更严格的统计分析。声音在季节和年份之间的演变是特别有趣的，因为它反映了气候变化的影响。我们将使用PAMGuide作为PAM数据处理的基础（PAMGuide是开源的，由Bath开发并在世界各地使用）。这将用于评估传统的声学指标，如功率谱密度和三倍频带水平。该项目目前考虑的机器学习包是TensorFlow和Ketos（本身基于TensorFlow，专为声学数据设计），用于自动识别声学特征。该项目的早期阶段可能会专注于了解机器学习，不仅是如何利用它，而且能够仔细检查算法的性能。数据由加拿大海洋网络（位于加拿大北极的一个浅水湾）、美国国家海洋和大气管理局（位于阿拉斯加北部的深海系泊处）和欧盟项目HiAOOS（高北冰洋观测系统）（横跨北极）提供。ONC和NOAA已经收集了数据，而HiAOOS项目的数据预计将在2025年收到。该项目的资金由工程和物理科学研究委员会（EPSRC）博士培训伙伴关系（DTP）提供。存储在巴斯大学的数据将根据巴斯大学的指导方针，遵守数据管理计划。

The Arctic environment is experiencing accelerated climate change; increasing temperatures in the air and in the ocean contribute to reducing the extent of sea ice, which is essential for regulating Earth's climate through ice-albedo feedback. With less ice coverage, more solar radiation is absorbed by the ocean waters leading to a causal sequence of decreased ice formation, rising sea levels and more destabilised weather events in the south. The reduction of sea ice in the Arctic is also opening the waters for new shipping routes and developments in maritime infrastructure, which further impacts the marine ecosystem and pollutes the subsea soundscape. Both loud impulsive sounds and increased ambient noise from anthropogenic activity are major stressors for many marine species, in particular whales who make use of the idealised conditions in the Arctic for long-distance communication. This project aims to monitor the effects of climate change in the Arctic environment using passive acoustic monitoring (PAM) techniques. The soundscape of the Arctic oceans can be measured continuously, capturing sounds from shipping, sea ice processes and animal vocalisations along with changes to ambient noise levels. Studying how these sources contribute to the soundscape of the region over time will be highly informative of the rate of climate change of the region.While acoustic signatures from many underwater sources are well documented, there are new opportunities with machine learning to automate identification of acoustic events. Machine learning techniques will be developed to isolate individual sound sources from recordings for more rigorous statistical analyses. The evolution of sounds across the seasons and along the years is of particular interest, as it reflects the effects of climate change. We will use PAMGuide as a basis for PAM data processing (PAMGuide is open source, developed at Bath and used around the world). This will be used to assess conventional acoustic metrics, such as power spectral density and third-octave band level. Machine learning packages currently considered for the project are TensorFlow and Ketos (itself based on TensorFlow and designed for acoustic data) for automating acoustic signature identification. Early stages of the project will likely focus on obtaining an understanding of machine learning, not only in how to utilise it but to be able to scrutinise performance of algorithms as well.Data is provided by Ocean Networks Canada (in a shallow bay in Arctic Canada), National Oceanic and Atmospheric Administration (deep-sea moorings north of Alaska) and the EU project HiAOOS (High Arctic Ocean Observation System) (across the Arctic). Data from ONC and NOAA has already been collected, whereas data from the HiAOOS project is expected to be received in 2025. Funding for the project is provided by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnerships (DTP). The data stored at Bath will be subject to a Data Management Plan, in line with University guidelines.