Using Telecommunication Fiber Infrastructure for Earthquake Monitoring and Near‐Surface Characterization

Biondi, Biondo; Yuan, Siyuan; Martin, Eileen; Huot, Fantine; Clapp, Robert G.

doi:10.1002/9781119521808.ch10

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…In this way, the strain field induced by natural processes (e.g., earthquakes) and urban activities (e.g., moving vehicles, construction, pumps) acting on the fiber coupled to the Earth can be sampled at a meter‐scale spatial resolution over tens of linear fiber kilometers. Notably, DAS technology has proven effective on existing and growing “dark fiber” telecommunication infrastructure for earthquake monitoring (Biondi et al., 2021), infrastructure monitoring (Liu, Yuan, Luo, et al., 2023; Yuan et al., 2021), and near‐surface imaging (Fang et al., 2020; E. R. Martin, Castillo, et al., 2017; Mjehovich et al., 2023), significantly reducing installation cost. Furthermore, by incorporating state‐of‐the‐art interrogators, such as OptaSense QuantX (Optasense, a Luna company, 2022), DAS can record full waveform signals over distances up to 50 km, with a granular 1 m channel spacing, which makes DAS promising for continuous city‐wide sensing.…”

Section: Introductionmentioning

confidence: 99%

Using Vehicle‐Induced DAS Signals for Near‐Surface Characterization With High Spatiotemporal Resolution

Yuan,

Liu,

Noh

et al. 2024

JGR Solid Earth

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Vehicle‐induced seismic waves, generated as vehicles traverse the ground surface, carry valuable information for imaging the underlying near‐surface structure. These waves propagate differently in the subsurface depending on soil properties at various spatial locations. By leveraging wave propagation characteristics, such as surface‐wave velocity and attenuation, this study presents a novel method for near‐surface monitoring. Our method employs passing vehicles as active, non‐dedicated seismic sources and leverages pre‐existing telecommunication fibers as large‐scale and cost‐effective roadside sensors empowered by Distributed Acoustic Sensing (DAS) technology. A specialized Kalman filter algorithm is integrated for automated DAS‐based traffic monitoring to accurately determine vehicles' location and speed. Then, our approach uniquely leverages vehicle trajectories to isolate space‐time windows containing high‐quality surface waves. With known vehicle (i.e., seismic source) locations, we can effectively mitigate artifacts associated with suboptimal distribution of sources in conventional ambient noise interferometry. Compared to ambient noise interferometry, our approach enables the synthesis of virtual shot gathers with a high signal‐to‐noise ratio and spatiotemporal resolution at reduced computational costs. We validate the effectiveness of our method using the Stanford DAS‐2 array, with a focus on capturing spatial heterogeneity and monitoring temporal variations in soil seismic properties during rainfall events. Specifically, in non‐built‐up areas, we observed an evident decrease in phase velocity and group velocity and an increase in attenuation due to the rainfall. Our findings illustrate our method's sensitivity and resolution in discerning variations across different spatial locations and demonstrate that our method is a promising advancement for high‐resolution near‐surface imaging in urban settings.

show abstract

Section: Introductionmentioning

confidence: 99%

Using Vehicle‐Induced DAS Signals for Near‐Surface Characterization With High Spatiotemporal Resolution

Yuan,

Liu,

Noh

et al. 2024

JGR Solid Earth

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this way, the strain field induced by natural processes (e.g., earthquakes) and urban activities (e.g., moving vehicles, construction, pumps) acting on the fiber coupled to the Earth can be sampled at a meter-scale spatial resolution over tens of linear fiber kilometers. Notably, DAS technology has proven effective on existing and growing "dark fiber" telecommunication infrastructure for earthquake monitoring (Biondi et al, 2021), infrastructure monitoring (Yuan et al, 2021;Liu, Yuan, Luo, et al, 2023), and near-surface imaging (E. R. Martin et al, 2017;Fang et al, 2020), significantly reducing installation cost. Furthermore, by incorporating state-of-the-art interrogators, such as OptaSense QuantX (Optasense, a Luna company, 2022), DAS can record full waveform signals over distances up to 50 km, with a granular 1 m channel spacing, which makes DAS promising for continuous city-wide sensing.…”

Section: Introductionmentioning

confidence: 99%

Using Vehicle-induced DAS Signals for Near-surface Characterization with High Spatiotemporal Resolution

Yuan,

Liu,

Noh

et al. 2023

Preprint

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Vehicle-induced seismic waves, generated as vehicles traverse the ground surface, carry valuable information for imaging the underlying near-surface structure. These waves propagate differently in the subsurface depending on soil properties at various spatial locations. By leveraging wave propagation characteristics, such as surface-wave velocity and attenuation, this study presents a novel method for near-surface monitoring. Our method employs passing vehicles as active, non-dedicated seismic sources and leverages pre-existing telecommunication fibers as large-scale and cost-effective roadside sensors empowered by Distributed Acoustic Sensing (DAS) technology. A specialized Kalman filter algorithm is integrated for automated DAS-based traffic monitoring to accurately determine vehicles’ location and speed. Then, our approach uniquely leverages vehicle trajectories to isolate space-time windows containing high-quality surface waves. With known vehicle (i.e., seismic source) locations, we can effectively mitigate artifacts associated with suboptimal distribution of sources in conventional ambient noise interferometry. Compared to ambient noise interferometry, our approach enables the synthesis of virtual shot gathers with a high signal-to-noise ratio and spatiotemporal resolution at reduced computational costs. We validate the effectiveness of our method using the Stanford DAS-2 array, with a focus on capturing spatial heterogeneity and monitoring temporal variations in soil seismic properties during rainfall events. Specifically, in non-built-up areas, we observed an evident decrease in phase velocity and group velocity and an increase in attenuation due to the rainfall. Our findings illustrate our method’s sensitivity and resolution in discerning variations across different spatial locations and demonstrate that our method is a promising advancement for high-resolution near-surface imaging in urban settings.

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A Prospective Evaluation of Borehole Distributed Acoustic Sensing Technologies for Detecting and Locating Out‐of‐Network Microseismicity

Lanza,

Tuinstra,

Rinaldi

et al. 2024

Geophysical Monograph Series

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Using Telecommunication Fiber Infrastructure for Earthquake Monitoring and Near‐Surface Characterization

Cited by 10 publications

References 16 publications

Using Vehicle‐Induced DAS Signals for Near‐Surface Characterization With High Spatiotemporal Resolution

Using Vehicle‐Induced DAS Signals for Near‐Surface Characterization With High Spatiotemporal Resolution

Using Vehicle-induced DAS Signals for Near-surface Characterization with High Spatiotemporal Resolution

A Prospective Evaluation of Borehole Distributed Acoustic Sensing Technologies for Detecting and Locating Out‐of‐Network Microseismicity

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