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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Passive Seismic Imaging of Urban Environments Using Distributed Acoustic Sensing: A Case Study from Melbourne, Australia
Abstract Distributed acoustic sensing (DAS) offers a cost effective, nonintrusive method for high-resolution near-surface characterization in urban environments where conventional geophysical surveys are limited or nonexistent. However, passive imaging with DAS in urban settings presents challenges such as strong diurnal traffic noise, nonlinear array geometry, and poor fiber coupling to the ground. We repurposed a dark fiber in Melbourne, Australia, into a 25 km DAS array that traces busy arterial roads, tram routes, and orthogonal sections. By employing noise cross correlation and array beamforming, we calculated dispersion curves and successfully inverted for a near-surface shear-wave velocity model down to 100 meters. Stationary seismic sources are maximized by selecting daytime traffic signals, thereby recovering surface waves and reducing interference from acoustic waves from man-made structures in the subsurface. Poorly coupled channels, which are linked to fiber maintenance pits, are identified through cross-correlation amplitudes. The dispersion curve calculation further considers the channel orientation to avoid mixing Rayleigh and Love waves. Using a trans-dimensional Markov chain Monte Carlo sampling approach, we achieved effective model inversion without a prior reference model. The resulting near-surface profile aligns with mapped lithology and reveals previously undocumented lithological variation.
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- Award ID(s):
- 1848166
- PAR ID:
- 10601314
- Publisher / Repository:
- SSA
- Date Published:
- Journal Name:
- The Seismic Record
- Volume:
- 4
- Issue:
- 4
- ISSN:
- 2694-4006
- Page Range / eLocation ID:
- 308 to 317
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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