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

气候变化的声学——北冰洋的长期观测

• 批准号: 2889921
• 金额: --
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889921
• 关键词: acoustics; climate; change; long; term

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.

北极环境正在经历加速的气候变化。空气和海洋中的温度升高有助于降低海冰的程度，这对于通过冰山反馈来调节地球气候至关重要。随着冰覆盖的较小，海水吸收了更多的太阳辐射，从而导致南部冰层降低，海平面上升和更不稳定的天气事件的因果关系。北极海冰的减少也为海上基础设施的新运输路线和发展开放，这进一步影响了海洋生态系统并污染了海底音景。人为活性的大声冲动声音和增加的环境噪声是许多海洋物种的主要压力源，尤其是使用北极中理想化条件进行长距离通信的鲸鱼。该项目旨在使用被动声学监测（PAM）技术来监测北极环境中气候变化的影响。可以连续测量北极海洋的音景，从运输，海冰工艺和动物发声以及周围噪声水平的变化中捕获声音。随着时间的流逝，研究这些来源如何促进该地区的声学，这将是该地区气候变化率的高度信息。尽管有充分的文献记录了许多来自许多水下来源的声学特征，但机器学习可以自动化声学事件的新机会。将开发机器学习技术，以将单个声源与录音中分离出来，以进行更严格的统计分析。整个季节和多年来的声音的演变特别令人感兴趣，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）。这将用于评估常规的声学指标，例如功率谱密度和第三次曲线级别。该项目当前考虑的机器学习软件包是用于自动化声学签名识别的TensorFlow和Ketos（本身基于张量和声音数据）。该项目的早期阶段可能会专注于了解机器学习的理解，这不仅是如何利用它，而且还可以仔细研究算法的性能。DATA由加拿大海洋网络（在加拿大北极北北方的一个浅海湾），国家海洋和大气管理（Alaska北部的北部地区）和EU Project hige the Ocection（High ancient of Conce）（遍及海洋北部）（遍布海洋北部）（高高的海洋）（遍及海洋）（遍布高空）。已经收集了来自ONC和NOAA的数据，而Hiaoos项目的数据预计将于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。根据大学指南，存储在BATH的数据将遵守数据管理计划。