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

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

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.