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ABSTRACT Distributed acoustic sensing (DAS) technology is an emerging field of seismic sensing that enables recording ambient noise seismic data along the entire length of a fiber-optic cable at meter-scale resolution. Such a dense spatial resolution of recordings over long distances has not been possible using traditional methods because of limited hardware resources and logistical concerns in an urban environment. The low spatial resolution of traditional passive seismic acquisition techniques has limited the accuracy of the previously generated velocity profiles in many important urban regions, including the Reno-area basin, to the top 100 m of the underlying subsurface. Applying the method of seismic interferometry to ambient noise strain rate data obtained from a dark-fiber cable allows for generating noise cross correlations, which can be used to infer shallow and deep subsurface properties and basin geometry. We gathered DAS ambient noise seismic data for this study using a 12 km portion of a dark-fiber line in Reno, Nevada. We used gathered data to generate and invert dispersion curves to estimate the near-surface shear-wave velocity structure. Comparing the generated velocity profiles with previous regional studies shows good agreement in determining the average depth to bedrock and velocity variations in the analyzed domain. A synthetic experiment is also performed to verify the proposed framework further and better understand the effect of the infrastructural cover along the cable. The results obtained from this research provide insight into the application of DAS using dark-fiber lines in subsurface characterization in urban environments. It also discusses the potential effects of the conduit that covers such permanent fiber installations on the produced inversion results.
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Characterizing Fractured Zones in Urban Karst Geology Using Leaky Surface Waves From Distributed Acoustic Sensing
Abstract Urban karst geology poses significant geohazard risks, most notably sinkholes and surface depression stemming from soluble and fractured bedrock that is prone to dissolution and collapse. However, mapping and characterizing these hazards using traditional geophysical surveys in cities is challenging due to dense infrastructure and high levels of human activity. In this work, we demonstrate how distributed acoustic sensing (DAS), deployed via preexisting telecommunication fiber‐optic cables, can be leveraged to detect fractured weak zones in a populated setting. By recording traffic noises, we are able to conduct large‐scale, cost‐effective, and minimally intrusive subsurface investigations. Our workflow integrates ambient noise interferometry with advanced signal enhancement techniques, specifically frequency‐wavenumber (F‐K) filtering and bin‐offset stacking. F‐K filtering isolates wavefields traveling in opposite directions to suppress localized noise, while bin‐offset stacking further enhances signal coherency by superposing channels with common offsets. The resulting Noise Cross‐correlation Functions exhibit unique inverse‐dispersion patterns that signify the presence of leaky surface waves generated by a low‐velocity half‐space. We invert the corresponding dispersion curves to derive a 2D S‐wave velocity model, highlighting a prominent low‐velocity anomaly indicative of a fractured zone. To confirm the karstic nature of this anomaly, rock physics modeling is employed to estimate spatial variations in fracture density, revealing marked heterogeneity in the fractured zone. Our findings underscore the power of DAS‐based ambient noise interferometry for delineating karst features and diagnosing potential sinkhole risks in urban environments. By exploiting widely available fiber‐optic networks, this approach significantly broadens the practicality of near‐surface geohazard mapping at the city scale.
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- Award ID(s):
- 2322198
- PAR ID:
- 10614094
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Solid Earth
- Volume:
- 130
- Issue:
- 7
- ISSN:
- 2169-9313
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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