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;空气和海洋温度的上升有助于减少海冰的范围，这对于通过冰-水反馈调节地球气候至关重要。由于冰层覆盖面积减少，更多的太阳辐射被海洋沃茨吸收，导致南部冰层形成减少、海平面上升和更不稳定的天气事件。北极海冰的减少也为新航线和海事基础设施的发展开放了沃茨，这进一步影响了海洋生态系统，污染了海底声景。人类活动产生的巨大的脉冲声和环境噪音是许多海洋物种的主要压力源，特别是利用北极理想条件进行长距离通信的鲸鱼。该项目旨在利用被动声学监测技术监测气候变化对北极环境的影响。北极海洋的声景可以连续测量，捕获来自航运，海冰过程和动物发声的声音沿着环境噪声水平的变化。研究这些来源如何随着时间的推移对该地区的声景做出贡献，将对该地区气候变化的速度提供高度信息。虽然许多水下来源的声学特征有很好的记录，但机器学习有新的机会自动识别声学事件。将开发机器学习技术，从录音中分离出单个声源，以便进行更严格的统计分析。声音在不同季节和沿着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，在巴斯开发，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），用于自动化声学签名识别。该项目的早期阶段可能会侧重于了解机器学习，不仅是如何利用它，而且还能够仔细检查算法的性能。数据由Ocean Networks Canada提供（在加拿大北极的一个浅海湾），国家海洋和大气管理局（阿拉斯加以北的深海系泊系统）和欧盟项目HiAOOS（北冰洋高纬度观测系统）（横跨北极）。来自ONC和NOAA的数据已经收集，而来自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数据管理计划，符合大学的指导方针。