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

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Verónica Rodríguez Tribaldos, Jonathan Ajo-Franklin, Shan Dou, Nathaniel Lindsey, Craig Ulrich, Michelle Robertson, Barry Freifeld, Tom Daley, Inder Monga, Chris Tracy


Several recent studies have demonstrated that Distributed Acoustic Sensing (DAS) can utilize existing subsurface telecom fiber (i.e. dark fiber) for high quality seismic measurements. Researchers to date have shown that this sensing combination, coupled to ambient noise interferometry techniques, can effectively image the shallow subsurface (< 30 m) using vehicle and infrastructure noise (f = 8 - 30 Hz). We present the first long-offset surface wave inversion study targeting deeper (⁓ 500 m) structure using DAS and dark fiber. This study utilizes a previously acquired dataset collected on a 23 km fiber section between West Sacramento and Woodland, CA, part of the DOE Energy Science Network (ESnet). By targeting noise generated by a co-linear rail line, broadband and rich in low frequencies (down to f=1 Hz), and long array offsets, we generate high-quality interferometric gathers suitable for inversion. Subsequent surface wave inversions using a multimode Monte Carlo (MC) sampling algorithm are consistent with geology and available confirmatory datasets derived from co-located sonic logs. The relatively sparse confirmatory data demonstrates, by comparison, the utility of the high spatial sampling provided by DAS. These results open the door to larger regional DAS studies targeting deeper targets but with resolutions higher than those afforded by the use of persistent low frequency (f<1 Hz) ocean microseism-related noise.



Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics


Distributed acoustic sensing, Ambient noise analysis, Dark Fiber


Published: 2019-02-08 01:25

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GNU Lesser General Public License (LGPL) 2.1

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