Properties of Noise Cross‐Correlation Functions Obtained from a Distributed Acoustic Sensing Array at Garner Valley, California

Zeng, Xiangfang; Lancelle, C.; Thurber, C. H.; Fratta, Dante; Wang, Herb; Lord, N. E.; Chalari, Athena; Clarke, Andy

doi:10.1785/0120160168

Cited by 98 publications

(59 citation statements)

References 22 publications

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“…Generally, DAS studies have focused on seismic wave phase information, which is sufficient to model seismic wavefield velocities, for example, in vertical seismic profiling (Daley et al, ; Mateeva et al, ), ambient noise velocity inversions (Ajo‐Franklin et al, ; Dou et al, ; Zeng et al, ), and earthquake phase identification (Ajo‐Franklin et al, ; Jousset et al, ; Lindsey et al, ; Yu et al, ). However, true ground motion amplitudes are necessary for many other seismological processing tasks, including full‐waveform inversion, AVO analysis, moment tensor inversion, and attenuation analysis, which the DAS community will likely investigate in the near future (Cole et al, ; Paitz et al, ).…”

Section: Introductionmentioning

confidence: 99%

On the Broadband Instrument Response of Fiber‐Optic DAS Arrays

2020

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Distributed Acoustic Sensing (DAS) is a novel tool in array seismology that measures the phase of backscattered laser pulses traveling in a fiber‐optic cable, and relates this measurement to the axial strain induced on the cable by a propagating seismic wavefield. Combining DAS with telecommunications optical fiber networks has begun to address a range of earth science questions where cost and field logistics have historically hindered observations. Unlike classic inertial seismometers, DAS instrument response is presently unquantified. This topic includes a variable sensing element—the fiber, including packaging and installation—which changes between experiments. Ignoring this element, one DAS record should approximate a fixed‐length strain gauge, which exactly measures Earth's motion down to quasi‐static frequencies relevant to geodesy. In this paper, we test this hypothesis using seismological observations of teleseismic earthquakes and microseism noise spanning the 1 to 120 s period range. We use a commercial DAS interrogator unit connected to an optical fiber previously used for telecommunication and a colocated broadband seismometer to estimate the DAS transfer function. We find a 1:1 correspondence with actual ground motion from 10–120 s. At shorter periods (1–10 s), DAS amplitude response is enhanced by 3–11 dB. Phase response is flat over this range of periods. We interpret the recovered DAS response function in terms of hypothesized fiber coupling and photonic effects. We propose this calibration methodology for future DAS experiments where seismic amplitude information is desired.

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Section: Introductionmentioning

confidence: 99%

On the Broadband Instrument Response of Fiber‐Optic DAS Arrays

2020

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“…They found that DAS waveforms show a high degree of correlation with a colocated seismometer record. Zeng et al (2017) extracted noise cross-correlation functions from a DAS array at Garner Valley, California. Li and Zhan (2018) applied template matching to the PoroTomo DAS data and detected over 100 earthquakes based on five cataloged earthquakes.…”

Section: Introductionmentioning

confidence: 99%

“…Jousset et al (2018) demonstrated the possibility of using DAS data for subsurface fault zone imaging. Zeng et al (2017) extracted noise cross-correlation functions from a DAS array at Garner Valley, California. Dou et al (2017) used traffic noise interferometry for seismic monitoring of the near-surface structure.…”

Section: Introductionmentioning

confidence: 99%

The Potential of DAS in Teleseismic Studies: Insights From the Goldstone Experiment

Zhan

Lindsey

et al. 2019

Geophysical Research Letters

108

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Distributed acoustic sensing (DAS) is a recently developed technique that has demonstrated its utility in the oil and gas industry. Here we demonstrate the potential of DAS in teleseismic studies using the Goldstone OpticaL Fiber Seismic experiment in Goldstone, California. By analyzing teleseismic waveforms from the 10 January 2018 M7.5 Honduras earthquake recorded on ~5,000 DAS channels and the nearby broadband station GSC, we first compute receiver functions for DAS channels using the vertical‐component GSC velocity as an approximation for the incident source wavelet. The Moho P‐to‐s conversions are clearly visible on DAS receiver functions. We then derive meter‐scale arrival time measurements along the entire 20‐km‐long array. We are also able to measure path‐averaged Rayleigh wave group velocity and local Rayleigh wave phase velocity. The latter, however, has large uncertainties. Our study suggests that DAS will likely play an important role in many fields of passive seismology in the near future.

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“…In this study, we use the terms "coherent noise" and "incoherent noise" to demonstrate the "good" and "bad" contribution of the passive surface waves to dispersion measurement, respectively (Cupillard et al 2011). Directional noise effects or azimuthal adjustment would not be involved due to the linear receiver configuration along roads or railways (e.g., Park et al 2004;Hayashi et al 2016;Dou et al 2017;Zeng et al 2017). We do not consider the statistical or stochastic properties (e.g., Groos and Ritter 2009;Nakata and Beroza 2015;Zhong et al 2015) because the conditions based on the Fig.…”

Section: Data Selection In Passive Surface Wave Surveymentioning

confidence: 99%

“…These advantages offer significant complements to active survey methods. In that context, passive surface wave methods are also applied for data acquired with the fiber-optic distributed acoustic sensing (DAS) technique for subsurface seismic monitoring (Daley et al 2013;Dou et al 2017;Zeng et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Automated Data Selection in the Tau–p Domain: Application to Passive Surface Wave Imaging

et al. 2019

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In the recent decades, passive surface wave methods have gained much attention in the near-surface community due to their ability to retrieve low-frequency surface wave information. Temporal averaging over a sufficiently long period of time is a crucial step in the workflow to fulfill the randomization requirement of the stationary source distribution. Because of logistical constraints, passive seismic acquisition in urban areas is mostly limited to short recording periods. Due to insufficient temporal averaging, contributions from non-stationary sources can smear the stacked dispersion measurements, especially for the low-frequency band. We formulate a criterion in the tau-p domain for selective stacking of dispersion measurements from passive surface waves and apply it to high-frequency (> 1 Hz) traffic noise. The criterion is based on the automated detection of input data with a high signal-to-noise ratio in a desired velocity range. Modeling tests demonstrate the ability of the proposed criterion to capture the contributions from the non-stationary sources and classify the passive surface wave data. A real-world application shows that the proposed data selection approach improves the dispersion measurements by extending the frequency band below 5 Hz and attenuating the distortion between 6 and 13 Hz. Our results indicate that significant improvements can be obtained by considering tau-p-based data selection in the workflow of passive surface wave processing and interpretation.

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Properties of Noise Cross‐Correlation Functions Obtained from a Distributed Acoustic Sensing Array at Garner Valley, California

Cited by 98 publications

References 22 publications

On the Broadband Instrument Response of Fiber‐Optic DAS Arrays

On the Broadband Instrument Response of Fiber‐Optic DAS Arrays

The Potential of DAS in Teleseismic Studies: Insights From the Goldstone Experiment

Automated Data Selection in the Tau–p Domain: Application to Passive Surface Wave Imaging

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