S023-04
Combining Ambient Noise and Distributed Acoustic Sensing (DAS) Deployed on Dark Fiber Networks for High-resolution Imaging at the Basin Scale

Wednesday, 9 December 2020: 16:14
Virtual
Verónica Rodríguez Tribaldos1, Nathaniel J Lindsey2, Shan Dou3, Craig Ulrich4, Michelle Robertson5, Bin Dong6, Vincent Dumont6, Kesheng Wu6, Inder Monga6, Chris Tracy6 and Jonathan Blair Ajo-Franklin7, (1)Lawrence Berkeley National Laboratory, Earth and Environmental Sciences Area, Berkeley, CA, United States, (2)Stanford University, Stanford, CA, United States, (3)Feasible Inc., Emeryville, CA, United States, (4)Lawrence Berkeley National Laboratory, Earth and Environmental Sciences, Berkeley, CA, United States, (5)Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA, United States, (6)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (7)Rice University, Earth, Environmental and Planetary Sciences Department, Houston, TX, United States
Recorded Presentation
Abstract:
Distributed Acoustic Sensing (DAS) re-purposes telecommunication optical fibers as dense arrays of seismic sensors. This developing technology enables recording ground motions for long periods of time across long distances (10’s of km) at high spatial (~1 m) and temporal resolution at frequencies ranging from the mHz to the kHz. Recently, the deployment of this novel sensing technique on existing, unused fiber-optic cable networks, known as dark fiber, has offered an attractive alternative to classical seismological studies, as it facilitates acquisition of high-resolution data at regional scale with minimal effort and in environments where deploying classic sensors would be challenging and costly.

Here, we explore the potential of combining DAS with ambient noise analysis techniques for high-resolution subsurface imaging at the basin-scale. We analyze 7 months of DAS ambient noise datasets recorded along a 22 km-long dark fiber profile that runs along the southern end of the Sacramento Basin in the Central Valley of California. Ambient noise interferometry is applied to natural and infrastructure-generated seismic noise, with frequencies between < 1 Hz to 10’s of Hz. The main objective is to retrieve shear-wave velocity structure along the transect at depths ranging from the upper tens of meters of the subsurface to depths of a few hundred meters. Thanks to the combination of extensive lateral coverage and dense sampling, DAS enables capturing spatial variability in subsurface structure at a regional scale. Moreover, the array nature of DAS can be exploited by applying stacking approaches to adjacent channels, with the goal of improving the signal-to-noise ratio of the data.

Despite these exciting opportunities, some challenges exist to fully exploit the capabilities of this sensing technique. One of the pressing issues is the large data volumes generated by these dense arrays, that can amount to several TB/day. New processing frameworks that enable scalable, parallel analysis of DAS ambient noise data on modern supercomputers, as well as novel Machine Learning approaches combining supervised and unsupervised learning for surface wave energy extraction will be explored and utilized. Our investigations show the potential of DAS deployed on dark fiber for cost-effective investigation of the near-surface.

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