Integrating Remote Sensing and Deep Learning for Predictive Surveillance of Mine Tailings Impoundments

集成遥感和深度学习对尾矿库进行预测监测

• 批准号: 2242668
• 负责人: Thomas Oommen
• 金额: $ 39.95万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242668&HistoricalAwards=false
• 关键词: Integrating; Remote; Sensing; Deep; Learning

The impacts of climate change have led to an increase in extreme weather events, posing significant challenges to infrastructure resilience and community well-being. Research supported by this Disaster Resilience Research Grant (DRRG) project addresses the critical need to monitor and maintain existing infrastructure in the face of these challenges. Specifically, it focuses on mine tailings impoundments, massive geotechnical structures that store mining waste. The failure of these structures during extreme weather events can cause environmental damage and loss of life. By leveraging satellite imagery analysis, weather data, and deep learning techniques, this project aims to establish a standard monitoring approach for mine tailings impoundments and revolutionize infrastructure monitoring and hazard management. The outcomes will enable the identification of movements within these structures and provide a predictive understanding of failure probability, allowing us to act proactively and prevent disasters. This monitoring approach will enhance community resilience, support hazard management, and establish critical risk profiles for surrounding areas.The research aims to develop standards for monitoring mine tailings impoundments following their exposure to extreme weather events. The project's research objectives include: (i) analyzing the utility of satellite-based radar stacking techniques and moisture estimates to characterize the temporal performance of mine tailings impoundments; (ii) utilizing geotechnical engineering concepts and satellite observations to characterize the life-cycle of the mine tailings impoundments; (iii) developing standards for monitoring the failure risk profile of mine tailings impoundments utilizing deep learning models applied to satellite observations, environmental data, and extreme event information. By advancing our knowledge in this area, the project has interdisciplinary implications for remote sensing, geoengineering, computer science, and natural hazards engineering. Fusing these disciplines will result in a cost-effective and nonintrusive monitoring methodology that can reduce the consequences of infrastructure failures and provide timely warnings to mitigate hazards. The project's broader impacts include fostering the development of a diverse STEM workforce, improving community safety, and ensuring accessibility to potential end-users through conferences, journals, and online platforms. The ultimate goal is to prevent future disasters and enhance the well-being of both humans and anthropogenic infrastructure.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

气候变化的影响导致极端天气事件增加，对基础设施的复原力和社区福祉构成重大挑战。这项抗灾能力研究补助金（DRRG）项目支持的研究解决了在面临这些挑战时监测和维护现有基础设施的关键需求。具体而言，它侧重于尾矿蓄水池，储存采矿废物的大型岩土工程结构。这些结构在极端天气事件中的故障可能造成环境破坏和生命损失。通过利用卫星图像分析、天气数据和深度学习技术，该项目旨在为尾矿库建立标准的监测方法，并彻底改变基础设施监测和灾害管理。结果将能够识别这些结构内的运动，并提供对故障概率的预测性了解，使我们能够主动采取行动并预防灾难。这一监测方法将提高社区的复原力，支持灾害管理，并为周边地区建立关键的风险概况。该研究旨在制定监测尾矿库暴露于极端天气事件后的标准。该项目的研究目标包括：㈠分析卫星雷达叠加技术和湿度估计的效用，以确定尾矿库的时间性能; ㈡利用地质工程概念和卫星观测来确定尾矿库的生命周期; ㈢利用应用于卫星观测、环境数据和极端事件信息的深度学习模型，制定监测尾矿库故障风险状况的标准。通过推进我们在这一领域的知识，该项目对遥感，地球工程，计算机科学和自然灾害工程具有跨学科的影响。融合这些学科将产生一种具有成本效益和非侵入性的监测方法，可以减少基础设施故障的后果，并提供及时的警报，以减轻危害。该项目更广泛的影响包括促进多元化STEM劳动力的发展，改善社区安全，并确保通过会议，期刊和在线平台向潜在的最终用户提供服务。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。