RCN: Distributed Acoustic Sensing (DAS) in Geosciences and Engineering

RCN：地球科学和工程中的分布式声学传感 (DAS)

• 批准号: 1948737
• 负责人: Herbert Wang
• 金额: $ 47.98万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1948737&HistoricalAwards=false
• 关键词: RCN; Distributed; Acoustic; Sensing; DAS

The purpose of the Research Coordination Network (RCN) is to advance and extend the applications of Distributed Acoustic Sensing (DAS) in geosciences and engineering. DAS senses ground vibration at one-meter spacing along optical fiber that is buried in the ground or in a borehole. DAS has been successfully implemented over distances of tens of kilometers for detecting earthquakes, monitoring geothermal reservoirs and carbon sequestration sites, and monitoring traffic and civil infrastructure. The RCN will reach out to researchers in optoelectronics and machine learning to develop synergies between these communities and geosciences and engineering. The RCN activities will be focused around annual, multi-day workshops and short courses, and topical webinars. One course will be field intensive and one will be devoted to data analysis. Guidance and direction for the community activities will be by the principal investigators and a multidisciplinary steering committee. The RCN steering committee is comprised of members with broad expertise in DAS applications, technology, data analysis, data interpretation, and data management. The steering committee includes several early-career members. Early-career scientists and graduate students are the target audience for many of the RCN activities. The RCN will also strive to bring together DAS researchers and users from a diverse international base of research organizations, industry, technology companies, and government agencies to exploit synergies and enhance efficient utilization of resources in DAS development and applications. The RCN will speed the development of technology transfer of DAS applications and technology to societally important monitoring, such as natural hazards and engineering infrastructure.DAS senses deformation in continuous fiber-optic arrays, which can span tens of kilometers with a spatial resolution of meters and with a broadband response from millihertz to kilohertz. DAS has been applied to 1) imaging the near-surface using ambient noise, 2) Vertical Seismic Profiling (VSP), 3) earthquake seismology, 4) geophysical monitoring of hydrocarbon, carbon sequestration and geothermal reservoirs, and 5) infrastructure monitoring of traffic and industrial facilities. Machine learning and utilization of dark fiber in telecommunication networks (excess fiber not used for communication) are exciting new developments. Other applications on the horizon include monitoring deformation in glaciers and snow avalanches, slope stability, and long-distance, civil infrastructure such as roads, pipelines, and rail. Meanwhile DAS technology itself is the subject of research and is rapidly improving. The RCN will provide guidance for managing and archiving terabytes per day of data to meet the challenges DAS data present to research teams and federal agencies charged with providing open data access.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.

研究协调网络（RCN）的目的是促进和扩展分布式声学传感（DAS）在地球科学和工程中的应用。DAS沿着埋在地下或钻孔中的光纤以一米的间距沿着感测地面振动。DAS已成功应用于数十公里范围内的地震探测、地热储层和碳封存点监测以及交通和民用基础设施监测。RCN将接触光电子学和机器学习领域的研究人员，以发展这些社区与地球科学和工程之间的协同作用。RCN的活动将集中在年度、为期多天的研讨会和短期课程以及专题网络研讨会上。一门课程将是实地密集型的，另一门课程将专门讨论数据分析。社群活动的指导和方向将由主要调查员和多学科指导委员会负责。RCN指导委员会由在DAS应用、技术、数据分析、数据解释和数据管理方面具有广泛专业知识的成员组成。指导委员会包括几名早期职业成员。 早期职业科学家和研究生是RCN许多活动的目标受众。RCN还将努力汇集DAS研究人员和来自研究机构，工业，技术公司和政府机构的各种国际基础的用户，以发挥协同作用，提高DAS开发和应用中资源的有效利用。RCN将加快DAS应用和技术向社会重要监测（如自然灾害和工程基础设施）的技术转让。DAS通过连续光纤阵列感知变形，该阵列可跨越数十公里，空间分辨率为米，宽带响应范围从毫赫兹到千赫兹。DAS已被应用于1）使用环境噪声对近地表进行成像，2）垂直地震剖面（VSP），3）地震学，4）碳氢化合物、碳固存和地热储层的地球物理监测，以及5）交通和工业设施的基础设施监测。机器学习和在电信网络中使用暗光纤（多余的光纤不用于通信）是令人兴奋的新发展。其他即将出现的应用包括监测冰川和雪崩的变形、斜坡稳定性以及公路、管道和铁路等长距离民用基础设施。与此同时，DAS技术本身也是研究的主题，并且正在迅速改进。RCN将为管理和归档每天TB级的数据提供指导，以应对DAS数据给负责提供开放数据访问的研究团队和联邦机构带来的挑战。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。

项目成果

Distributed Sensing and Machine Learning Hone Seismic Listening

分布式传感和机器学习磨练地震监听

• DOI: 10.1029/2022eo220121
• 发表时间: 2022
• 期刊: Eos
• 作者: Trainor-Guitton, Whitney;Martin, Eileen;Rodr�guez Tribaldos, Ver�nica;Taverna, Nicole;Dumont, Vincent
• 通讯作者: Dumont, Vincent

The Global DAS Month of February 2023

2023 年 2 月全球 DAS 月

• DOI: 10.1785/0220230180
• 发表时间: 2023
• 期刊: Seismological Research Letters
• 影响因子: 3.3
• 作者: Wuestefeld, Andreas;Spica, Zack J.;Aderhold, Kasey;Huang, Hsin-Hua;Ma, Kuo-Fong;Lai, Voon Hui;Miller, Meghan;Urmantseva, Lena;Zapf, Daniel;Bowden, Daniel C.
• 通讯作者: Bowden, Daniel C.