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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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Processing Ambient Noise Data Using Phase Cross‐Correlation and Application Toward Understanding Spatiotemporal Environmental Effects
Abstract Typical use of ambient noise interferometry focuses on longer period (>1 s) waves for exploration of subsurface structure and other applications, while very shallow structure and some environmental seismology applications may benefit from use of shorter period (<1 s) waves. We explore the potential for short‐period ambient noise interferometry to determine shallow seismic velocity structures by comparing two methodologies, the conventional amplitude‐based cross‐correlation and linear stacking (TCC‐Lin) and a more recently developed phase cross‐correlation and time‐frequency phase‐weighted‐stacking (PCC‐PWS) method with both synthetic and real data collected in a heterogeneous karst aquifer system. Our results suggest that the PCC‐PWS method is more effective in extracting short‐period wave velocities than the TCC‐Lin method, especially when using data collected in regions containing complex shallow structures such as the karst aquifer system investigated here. In addition to the different methodologies for computing the cross correlation functions, we also examine the relative importance of signal‐to‐noise ratio and number of wavelengths propagating between station pairs to determine data/solution quality. We find that the lower number of wavelengths of 3 has the greatest impact on the network‐averaged group velocity curve. Lastly, we test the sensitivity of the number of stacks used to create the final empirical Green's function, and find that the PCC‐PWS method required about half the number of cross‐correlation functions to develop reliable velocity curves compared to the TCC‐Lin method. This is an important advantage of the PCC‐PWS method when available data collection time is limited.
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- Award ID(s):
- 1850667
- PAR ID:
- 10441582
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Earth Surface
- Volume:
- 128
- Issue:
- 7
- ISSN:
- 2169-9003
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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