RII Track 4: Illuminating the Dark Subsurface using Fiber Optic Distributed Acoustic Sensing (DAS) Array

RII 轨道 4：使用光纤分布式声学传感 (DAS) 阵列照亮黑暗的地下

• 批准号: 2033376
• 负责人: Xiaowei Chen
• 金额: $ 22.79万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033376&HistoricalAwards=false
• 关键词: RII; Track; Illuminating; Dark; Subsurface

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.

地震学家使用地震网络记录地面振动（例如，地震/爆炸监测）并探测黑暗的地下，就像医院里的计算机断层扫描（CT）一样。分布式声学传感（DAS）是一项新兴技术，它可以将现有的电信光缆转换成每隔几米就有数千个密集分布的地震传感器。与传统地震台网的几十公里间距相比，这一间距的密度要大得多，为科学研究提供了前所未有的机遇。该合同将支持协同定位的DAS/地震阵列的数据收集，以解决重要的研究问题，例如1)什么是非常小的地震的破裂过程和工业操作诱发地震的特征；2)为什么不同地块的地震动会发生显著变化，以及浅层土壤结构是如何变化的；3)地表对极端天气事件、交通和其他环境变化的响应。该奖项将支持一名女私家侦探和俄克拉荷马大学的一名研究生，以及两名早期职业调查员之间的合作。该项目将PI的研究转变为DAS阵列，这可能是下一代地震阵列，并解决与俄克拉荷马州环境危害密切相关的科学问题。该项目将支持与其他州机构的合作，并改善俄克拉荷马州的整体研究基础设施。地震学家使用地震网络来记录地面振动（例如，地震/爆炸监测）和探测黑暗的地下（例如，地下成像）。近年来，地震阵列已被应用于环境研究，如极端天气事件、地下水变化等。由于复杂的单个地震仪的成本，地震台网的扩展是具有挑战性的。分布式声学传感（DAS）是一项新兴技术，它可以将现有的电信光缆转换成密集间隔的地震传感器。它的工作原理是向光纤中发送激光脉冲，每隔几米就用固有缺陷散射回来的回声记录询问（作为可跟踪的航路点），并推断由于地面振动引起的应变变化。光纤网络正在迅速扩展，用于通信目的，包括地震覆盖较少但地震活动率高的俄克拉何马州。利用现有的基础设施，DAS阵列可以显着扩展地震监测能力，并为科学提供前所未有的机会。该RII Track-4 EPSCoR研究人员奖将支持俄克拉荷马州北部协同定位的DAS/地震阵列的数据收集。来自加州的数据集将被纳入比较。这些数据集将使用高性能计算技术进行处理，以充分利用光纤网络的潜力，并将解决重要的研究问题，包括：(1)地震地震学：DAS如何提高小地震探测的分辨率和震源特征（例如，应力下降和复杂性）？性能与地震仪相比如何？(2)结构响应：DAS和节点阵的场地响应和波形行为是否一致？它们与地下结构的性质有何关系？(3)环境地震学：天气事件在DAS阵列上有哪些表现？收集的数据和研究产品将进一步加强PI在地震地震学方面的研究，并将她的研究转化为俄克拉荷马州下一代地震阵列的环境问题。该项目将支持与其他州机构的合作，并改善俄克拉荷马州的研究基础设施。研究数据集将被整合到课堂活动中，学生们将有机会亲身体验下一代地震网络。该奖项反映了美国国家科学基金会的法定使命，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

Source parameter analysis using distributed acoustic sensing – an example with the PoroTomo array

使用分布式声学传感进行源参数分析 — 以 PoroTomo 阵列为例

• DOI: 10.1093/gji/ggad061
• 发表时间: 2023
• 期刊: Geophysical Journal International
• 影响因子: 2.8
• 作者: Chen, Xiaowei