Urban Near‐Surface Seismic Monitoring Using Distributed Acoustic Sensing

16 Urban subsurface monitoring requires a system with high temporal-spatial resolution, 17 low maintenance cost, and minimal intrusion to urban life. Distributed acoustic sens18 ing (DAS), in contrast to conventional station-based sensing technology, has the poten19 tial to provide a passive seismic solution to urban monitoring requirements. Based on 20 data recorded by the Stanford Fiber Optic Seismic Observatory, we demonstrate that 21 near-surface velocity change induced by the excavation of basement construction can be 22 monitored using existing fiber optic infrastructure in a noisy urban environment. To achieve 23 the superior results, careful signal processing with noise removal and source signature 24 normalization are applied to raw DAS recordings. The repeated blast signals from quarry 25 sites provide free, unidirectional, and near impulsive sources for periodic urban seismic 26 monitoring, which are essential for increasing the temporal resolution of passive seismic 27 methods. Our study suggests that DAS will likely play an important role in urban sub28 surface monitoring. 29 1 Plain Language Summary 30 Seismic monitoring can provide crucial information about near-surface changes due 31 to natural or manmade activities. However, the high cost and the ”after-effect” nature 32 of conventional station-based monitoring methods limit their application in urban en33 vironments where near real-time and meter-scale resolution are required. Distributed acous34 tic sensing (DAS) has the potential to achieve all requirements utilizing existing com35 munication infrastructure. Using Stanford Fiber Optic Seismic Observatory, we demon36 strate that its recordings of quarry blasts 13.3 km away carry important subsurface ve37 locity information within the footprint of the array. These short bursts of quarry blast 38 signals provide us free, unidirectional, and repetitive sources that sample the urban sub39 surface at an interval frequent enough for monitoring. We observe large velocity decrease 40 from the recordings close to the excavation site. Our study suggests that telecommuni41 cations fiber repurposed for DAS will potentially play an important role in many urban 42 subsurface monitoring applications. 43 2 Introduction 44 Characterizing and monitoring changes in the top tens of meters of the Earth’s sub45 surface will play a significant role to satisfy the increasing need of urban sustainability 46 and resilience (Díaz et al., 2017). Near-surface changes due to natural or man-made events 47 may lead to hazards including ground subsidence (Tran & Sperry, 2018), sinkholes (Dahm 48 et al., 2011; Gutiérrez et al., 2014), and landslides (Renalier et al., 2010; Schenato et al., 49 2017), which may result in direct casualties of urban residents and damages to existing 50 infrastructure (Douglas, 2004). Many such subsurface changes manifest themselves as 51 temporal variations in geophysical properties (such as velocity, attenuation, electric con52 ductivity, gravity, etc.) before catastrophic hazards occur, which can be monitored and 53 predicted by geophysical prospecting using seismic, electric, electromagnetic and grav54 itational methods. 55 Compared to conventional geophysical exploration for resources, near-surface mon56 itoring in urban environments has the unique acquisition requirements of: 1) high spa57 tial resolution towards meter-scale; 2) high temporal resolution towards real-time data 58 collection and daily warning; 3) low maintenance for long term monitoring; and 4) min59 imal intrusion to urban life. These requirements are met by a densely distributed, fre60 quently recording, easy to maintain, passive system that we present in this paper: a pas61 sive seismic monitoring system enabled by distributed acoustic sensing (DAS). A DAS 62 array measures strain along kilometers of optical fiber, producing large datasets with kilo63 hertz time sampling and at sub-meter channel spacing (Parker et al., 2014). Over the 64 past dozen years, DAS has been a rapidly evolving technology for vertical seismic pro65

[1]  D. C. Finfer,et al.  Distributed Acoustic Sensing - A New Tool for Seismic Applications , 2012 .

[2]  Inder Monga,et al.  Distributed Acoustic Sensing Using Dark Fiber for Near-Surface Characterization and Broadband Seismic Event Detection , 2019, Scientific Reports.

[3]  Arthur H. Hartog,et al.  The effect of gauge length on axially incident P‐waves measured using fibre optic distributed vibration sensing , 2017 .

[4]  Zhongwen Zhan,et al.  The Potential of DAS in Teleseismic Studies: Insights From the Goldstone Experiment , 2019, Geophysical Research Letters.

[5]  M. Asten,et al.  A study of near source effects in array-based (SPAC) microtremor surveys , 2008 .

[6]  I. Douglas People Induced Geophysical Risks and Urban Sustainability , 2004 .

[7]  B. Biondi,et al.  Earthquakes analysis using data recorded by the Stanford DAS array , 2017 .

[8]  Y. Li,et al.  Near-surface site investigation by seismic interferometry using urban traffic noise in Singapore , 2019, GEOPHYSICS.

[9]  Tom Richard Parker,et al.  Distributed Acoustic Sensing – a new tool for seismic applications , 2014 .

[10]  Gregory C. Beroza,et al.  Urban Seismic Site Characterization by Fiber‐Optic Seismology , 2020, Journal of Geophysical Research: Solid Earth.

[11]  P. Jousset,et al.  Dynamic strain determination using fibre-optic cables allows imaging of seismological and structural features , 2018, Nature Communications.

[12]  J. Díaz,et al.  Urban Seismology: on the origin of earth vibrations within a city , 2017, Scientific Reports.

[13]  Barry Freifeld,et al.  Distributed Acoustic Sensing for Seismic Monitoring of The Near Surface: A Traffic-Noise Interferometry Case Study , 2017, Scientific Reports.

[14]  M. Parise,et al.  A review on natural and human-induced geohazards and impacts in karst , 2014 .

[15]  Eileen R. Martin,et al.  A Seismic Shift in Scalable Acquisition Demands New Processing: Fiber-Optic Seismic Signal Retrieval in Urban Areas with Unsupervised Learning for Coherent Noise Removal , 2018, IEEE Signal Processing Magazine.

[16]  Michel Campillo,et al.  High-Resolution Surface-Wave Tomography from Ambient Seismic Noise , 2005, Science.

[17]  L. Schenato,et al.  Distributed optical fibre sensing for early detection of shallow landslides triggering , 2017, Scientific Reports.

[18]  Application of 2D full-waveform tomography on land-streamer data for assessment of roadway subsidenceApplication of 2D full waveform tomography , 2018 .

[19]  F. Cotton,et al.  The nature of noise wavefield and its applications for site effects studies A literature review , 2006 .

[20]  W. Bialowons,et al.  A seismological study of shallow weak micro-earthquakes in the urban area of Hamburg city, Germany, and its possible relation to salt dissolution , 2011 .

[21]  P. Bard,et al.  Shear wave velocity imaging of the Avignonet landslide (France) using ambient noise cross correlation , 2010 .

[22]  Mark E. Willis,et al.  Quantitative quality of distributed acoustic sensing vertical seismic profile data , 2016 .

[23]  Eileen R. Martin,et al.  Fiber‐Optic Network Observations of Earthquake Wavefields , 2017 .

[24]  Bruce Edmonds,et al.  The Nature of Noise , 2009, EPOS.