Imaging shallow structure with active-source surface wave signal recorded by distributed acoustic sensing arrays

Abstract Seismological methods have been widely used to construct subsurface images in urban areas, for both seismological and engineering purposes. However, it remains a challenge to continuously operate a dense array in cities for high-resolution 4D imaging. In this study, we utilized distributed acoustic sensing (DAS) and a 5.2 km long, L-shaped, telecom, fiber-optic cable to record the wavefield from a highly repeatable airgun source located 7–10 km away. No P-wave signal was observed, but the S-wave signal emerged clearly on the shot-stacked traces, and the arrivals were consistent with collocated geophone traces. Because the signal quality is significantly affected by cable coupling and local noise, three methods can be employed to improve signal-to-noise ratio: (1) stacking contiguous, colinear channels to increase effective gauge length, (2) connecting multiple fibers within a single conduit and stacking collocated channels, and (3) using engineered fiber. In conclusion, the combination of DAS, using internet fiber and an airgun source with proven efficient signal enhancement methods, can provide frequent snapshots of the near surface across an urban area.

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Surface-wave dispersion spectrum inversion method applied to Love and Rayleigh waves recorded by distributed acoustic sensing

Geophysics ◽

10.1190/geo2019-0691.1 ◽

2021 ◽

Vol 86 (1) ◽

pp. EN1-EN12 ◽

Cited By ~ 1

Author(s):

Zhenghong Song ◽

Xiangfang Zeng ◽

Clifford H. Thurber

Keyword(s):

Surface Wave ◽

Wave Velocity ◽

Love Wave ◽

Wave Dispersion ◽

Dynamic Strain ◽

Inversion Method ◽

Data Sets ◽

Surface Wave Dispersion ◽

Acoustic Sensing ◽

Distributed Acoustic Sensing

Recently, distributed acoustic sensing (DAS) has been applied to shallow seismic structure imaging providing dense spatial sampling at a relatively low cost. DAS on a standard straight fiber-optic cable mostly records axial dynamic strain, which makes it difficult to separate the Rayleigh and Love wavefields. As a result, the mixed Rayleigh and Love wave signals cannot be used in the conventional surface-wave dispersion inversion method. Therefore, it is often ensured that the source and the cable are in the same line and only Rayleigh wave dispersion is used, which limits the constraints on structure and model resolution. We have inverted surface-wave dispersion spectra instead of dispersion curves. This inversion method can use mixed Rayleigh and Love waves recorded when the source and receiver array are not aligned. The multiple-channel records are transformed to the frequency domain, and a slant stack method is used to construct the dispersion spectra. The genetic algorithm method is used to obtain an optimal S-wave velocity model that minimizes the difference between theoretical and observed dispersion spectra. A series of synthetic tests are conducted to validate our method. The results suggest that our method not only improves the flexibility of the acquisition system design, but the Love wave data also provide additional constraints on the structure. Our method is applied to the active source and ambient noise data sets acquired at a geothermal site and provides consistent results for different data sets and acquisition geometries. The sensitivity of the dispersion spectra to layer thickness, density, and P-wave velocity is also discussed. With our method, the amount of usable data can be increased, helping deliver better subsurface images.

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High‐Resolution Shallow Structure at Brady Hot Springs Using Ambient Noise Tomography (ANT) on a Trenched Distributed Acoustic Sensing (DAS) Array

Wetland Carbon and Environmental Management - Geophysical Monograph Series ◽

10.1002/9781119521808.ch8 ◽

2021 ◽

pp. 101-110

Author(s):

Xiangfang Zeng ◽

Clifford H. Thurber ◽

Herbert F. Wang ◽

Dante Fratta ◽

Kurt L. Feigl

Keyword(s):

High Resolution ◽

Ambient Noise ◽

Hot Springs ◽

Ambient Noise Tomography ◽

Acoustic Sensing ◽

Distributed Acoustic Sensing ◽

Shallow Structure

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Surface Wave Imaging using Distributed Acoustic Sensing Deployed on Dark Fiber: Moving Beyond High Frequency Noise

10.31223/osf.io/jb2na ◽

2019 ◽

Author(s):

Verónica Rodríguez Tribaldos ◽

Jonathan Ajo-Franklin ◽

Shan Dou ◽

Nathaniel Lindsey ◽

Craig Ulrich ◽

...

Keyword(s):

Surface Wave ◽

High Frequency ◽

Frequency Noise ◽

Acoustic Sensing ◽

High Frequency Noise ◽

Wave Imaging ◽

Distributed Acoustic Sensing

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Low-frequency Ambient Distributed Acoustic Sensing (DAS): Case Study from Perth, Australia

Geophysical Journal International ◽

10.1093/gji/ggab111 ◽

2021 ◽

Author(s):

Jeffrey Shragge ◽

Jihyun Yang ◽

Nader Issa ◽

Michael Roelens ◽

Michael Dentith ◽

...

Keyword(s):

Surface Wave ◽

Optical Fiber ◽

Low Frequency ◽

Wave Analysis ◽

Near Surface ◽

Acoustic Sensing ◽

Geotechnical Investigations ◽

Wide Range ◽

Distributed Acoustic Sensing

Summary Ambient wavefield data acquired on existing (so-called “dark fiber”) optical fiber networks using distributed acoustic sensing (DAS) interrogators allow users to conduct a wide range of subsurface imaging and inversion experiments. In particular, recorded low-frequency (<2 Hz) surface-wave information holds the promise of providing constraints on the shear-wave velocity (VS) to depths exceeding 0.5 km. However, surface-wave analysis can be made challenging by a number of acquisition factors that affect the amplitudes of measured DAS waveforms. To illustrate these sensitivity challenges, we present a low-frequency ambient wavefield investigation using a DAS dataset acquired on a crooked-line optical fiber array deployed in suburban Perth, Western Australia. We record storm-induced microseism energy generated at the nearby Indian Ocean shelf break and/or coastline in a low-frequency band (0.04 − 1.80 Hz) and generate high-quality virtual shot gathers (VSGs) through cross-correlation and cross-coherence interferometric analyses. The resulting VSG volumes clearly exhibit surface-wave energy, though with significant along-line amplitude variations that are due to the combined effects of ambient source directivity, crooked-line acquisition geometry, and the applied gauge length, fiber coupling, among other factors. We transform the observed VSGs into dispersion images using two different methods: phase shift and high-resolution linear Radon transform. These dispersion images are then used to estimate 1-D near-surface VS models using multi-channel analysis of surface-waves (MASW), which involves picking and inverting the estimated Rayleigh-wave dispersion curves using the particle-swarm optimization global optimization algorithm. The MASW inversion results, combined with nearby deep borehole information and 2-D elastic finite-difference modeling, show that low-frequency ambient DAS data constrain the VS model, including a low-velocity channel, to at least 0.5 km depth. Thus, this case study illustrates the potential of using DAS technology as a tool for undertaking large-scale surface-wave analysis in urban geophysical and geotechnical investigations to depths exceeding 0.5 km.

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