Seismologists use seismic networks to record ground vibrations (e.g., earthquakes/explosion monitoring) and probe the dark subsurface, just as computerized tomography (CT) scans in hospitals. An emerging technology, Distributed Acoustic Sensing (DAS), can convert pre-existing telecommunication fiber cables into thousands of densely spaced seismic sensors for every few meters. This spacing is significantly denser than the tens of kilometers spacing from traditional seismic networks, providing unprecedented opportunities in science. This award will support data collection of co-located DAS/seismic array to address important research questions, such as 1) what is the rupture process of very small earthquakes and characteristics of induced earthquakes from industry operations; 2) why ground motion can change significantly from block to block, and how shallow soil structure changes; 3) how the ground surface responds to extreme weather events and traffic and other environmental changes. This award will support a female PI and a graduate student from the University of Oklahoma, and collaborations between two early-career investigators. This project will transform the PI's research into a DAS array, which is potentially the next generation of seismic array, and address scientific questions closely related to environmental hazards in Oklahoma. The project will support collaborations with other state agencies and improve overall research infrastructure in Oklahoma.

Seismologists use seismic networks to record ground vibrations (e.g., earthquakes/explosion monitoring) and probe the dark subsurface (e.g., subsurface imaging). In recent years, seismic array have been applied to environmental studies, such as extreme weather events, groundwater changes. Owing to the cost of complex individual seismometers, expansion of the seismic network is challenging. An emerging technology, Distributed Acoustic Sensing (DAS), can convert pre-existing telecommunication fiber cables into densely spaced seismic sensors. It works by sending a laser pulse into the fiber, recording the interrogation with an echo scattered back by intrinsic defects every few meters (acting as trackable waypoints), and inferring strain changes due to ground vibration. Fiber networks have been rapidly expanding for telecommunication purposes, including areas that have sparse seismic coverage but a high seismicity rate in Oklahoma. Leveraging existing infrastructure, DAS array can significantly expand seismic monitoring capabilities and open unprecedented opportunities in science. This RII Track-4 EPSCoR Research Fellows award will support data collection of co-located DAS/seismic array in northern Oklahoma. Datasets from California will be included as a comparison. These datasets will be processed with high-performance computing techniques to exploit the full potential of fiber network, and will address important research questions, including: (1) Earthquake seismology: How can DAS improve resolution small earthquake detection and source characteristics (e.g., stress drops and complexity)? How does the performance compare to seismometers? (2) Structural response: Are site responses and waveform behaviors from DAS and nodal array consistent, and how are they related to properties of subsurface structure? (3) Environmental seismology: What are the manifestations of weather events on DAS array? The data collected and research products will further strengthen the PI’s research in earthquake seismology and transform her research into the next generation of seismic array to environmental problems in Oklahoma. The project will support collaborations with other state agencies and improve the research infrastructures in Oklahoma. The research dataset will be integrated into classroom activities, and students will have hands-on experiences with a next-generation seismic network.

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.

Project Start
Project End
Budget Start
2021-02-01
Budget End
2023-01-31
Support Year
Fiscal Year
2020
Total Cost
$227,876
Indirect Cost
Name
University of Oklahoma
Department
Type
DUNS #
City
Norman
State
OK
Country
United States
Zip Code
73019