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-反照率反馈来调节地球气候至关重要。由于冰层覆盖率较低，海水吸收了更多的太阳辐射，导致冰形成减少，海平面上升，南部天气事件更加不稳定。北极海冰的减少也为新的航道和海洋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。巨大的脉冲声音和人类活动增加的环境噪音都是许多海洋物种的主要压力来源，特别是利用北极理想条件进行远距离交流的鲸鱼。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。北冰洋的声景可以连续测量，捕捉到航运、海冰过程和动物发声的声音以及环境噪音水平的变化。研究这些声源如何随着时间的推移对该地区的声景做出贡献，将是该地区气候变化速度的高度信息量。虽然许多水下来源的声学特征被很好地记录下来，但机器学习为自动化识别声学事件提供了新的机会。将开发机器学习技术，将单个声源从录音中分离出来，以便进行更严格的统计分析。声音在不同季节和不同年份的演变尤其令人感兴趣，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，在Bath开发并在世界各地使用)。这将被用来评估传统的声学指标，如功率谱密度和第三倍频程频带水平。目前考虑用于该项目的机器学习包是TensorFlow和Ketos(本身基于TensorFlow并为声学数据而设计)，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，还能仔细检查算法的性能。数据由加拿大海洋网络公司(加拿大海洋网络公司)(在加拿大北极的一个浅水湾)、国家海洋和大气管理局(阿拉斯加以北的深海停泊处)和欧盟项目HiAOOS(北极高空观测系统)提供。ONC和NOAA的数据已经收集完毕，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理事会(EPSRC)博士培训伙伴关系(DTP)提供。存储在巴斯的数据将受到符合大学指导方针的数据管理计划的约束。

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.