Characterization and delineation of oil-in-water at the Scott Inlet seeps through robotic autonomous underwater vehicle technology

与加拿大北大西洋和北冰洋的石油和天然气以及航运业务有关的漏油风险增加，要求采用先进的地下数据收集技术。该项目提出了自主水下航行器(AUV)技术，以帮助了解油羽行为，并提高加拿大对漏油的反应效率。事实证明，与其他传统调查方法相比，AUVS提供的高分辨率地下数据更适合于搜索和圈定斑块状油羽。该项目旨在通过考虑油类在水中的实际行为和灾害应对的时间紧迫性，解决目前AUV技术中的空白。该项目将基于自适应采样方法，为AUV开发一种创新的水下数据收集战略。使用这种方法，AUV根据机载传感器检测到的羽流特征实时自主修改其任务，导致路径集中在羽流附近或羽流内，以及传感器识别的信息丰富的区域。到目前为止，针对水下溢油特征的AUV和油类传感技术仍在开发中。只有少数实验是在真实的漏油条件下进行的；大多数实验是在实验室和中尺度实验中进行的，或者使用石油替代品进行的，这大大限制了理解和捕捉现场发现的复杂性的能力。因此，我们项目的另一个关键特征是通过巴芬岛附近的现场试验，在真实的水包油海洋环境中测试我们的石油检测方法，那里的水中自然存在石油。我们的项目与加拿大AUV运营商和制造商合作，将在使用AUV划定水下油羽方面向前迈出实质性的一步，显著改善加拿大的溢油反应和水下人工智能能力，并增强对海洋石油污染物和监测的知识。

Increased risks of oil spills associated with the offshore oil and gas as well as shipping operations in the northern Atlantic and Arctic oceans of Canada call for advanced technologies for subsurface data collection. Autonomous underwater vehicle (AUV) technology is proposed in this project to help understand oil plume behavior and improve the efficiency of Canada's oil spill response. High-resolution subsurface data provided by AUVs has proven to be more suitable for searching and delineating patchy oil plumes compared with other traditional survey methods. This project aims to address gaps in current AUV technology by considering the realistic behaviour of oil in water and the time-critical nature of disaster response. The project will develop an innovative underwater data collection strategy for AUVs, based on an adaptive sampling approach. Using this approach, an AUV autonomously modifies its mission in real-time based on features of the plumes detected by on-board sensors, resulting in a path concentrated nearby or within the plume and in information-rich areas identified by the sensors. To date, AUV and oil sensing technologies specific to the characteristics of underwater oil spills are still under development. Only a few experiments were done in real oil spill conditions; most in laboratory and mesoscale experiments or with oil proxies, which significantly limits the ability to understand and capture the complexity found in the field. Hence, another key feature of our project is to test our oil detection methods in real oil-in-water ocean environments through field trials near Baffin Island where oil is naturally present in the water. Our project, in partnership with Canadian AUV operators and manufacturers, will result in a substantial step forward in delineation of subsurface oil plumes using AUVs, providing a significant improvement in Canada's oil spill response and underwater artificial intelligence capacity, as well as enhanced knowledge of marine oil contaminants and monitoring.