Seismic monitoring leveraging existing telecom infrastructure at the SDASA: Active, passive, and ambient-noise analysis

Abstract

We analyze active and passive seismic data recorded by the Stanford distributed acoustic sensing array (SDASA) located in conduits under the Stanford University campus. For the active data we used low-energy sources (betsy gun and sledge hammer) and recorded data using both the DAS array and 98 three-component nodes deployed along a 2D line. The joint analysis of shot profiles extracted from the two data sets shows that some surface waves and refracted events are consistently recorded by the DAS array. In areas where geophone coupling was suboptimal because of surface obstructions, DAS recordings are more coherent. In contrast, surface waves are more reliably recorded by the geophones than the DAS array. Because of the noisy environment and weak sources, neither data set shows clear reflections. We demonstrate the repeatability of DAS recordings of local earthquakes by comparing two weak events (magnitude 0.95 and 1.34) with epicenters 100 m apart that occurred only one minute from each other. Analyzing another local, and slightly stronger, earthquake (magnitude 2.0) we show how the kinematics of both the P-arrival and S-arrival can be measured from the DAS data. Interferometric analysis of passive data shows that reliable virtual-source responses can be extracted from the DAS data. We observe Rayleigh waves when correlating aligned receivers, and Love waves when correlating receivers belonging to segments of the array parallel to each other. Dispersion analysis of the virtual sources shows the expected decrease in surface-wave velocity with increasing frequency.

Urban Seismic Site Characterization by Fiber‐Optic Seismology

“…The Stanford DAS array recorded and stored continuous data from 620 independent seismic channels at a frequency of 50 samples per second with 7.14 m gauge length and 8.16 m channel spacing. Through this experiment Martin et al () showed that DAS technology can be used to record seismic data directly from a free‐floating cable in a horizontal PVC conduit. Furthermore, by analyzing adjacent earthquakes on nearby faults, Biondi et al () demonstrated that signals recorded using this cable provide repeatable and reliable ground motion measurements.…”

“…We apply passive Rayleigh wave interferometry to the DAS channels along Via Ortega using 1 year of continuous data starting from early September 2016 (Martin et al, ). Only a collinear sub‐array is used for interferometry because that virtual‐source configuration is expected to yield Rayleigh waves (Martin et al, ).…”

“…We window of continuous signal into 5 min intervals with 50% overlap, band‐passed filter from 0.5 to 24 Hz, perform a 1‐bit normalization, and then stack hourly cross correlations. After saving each hour's average cross correlations throughout the week, we normalized them by their L1 norms and stack them for each month, yielding a series of virtual‐source response estimates (Martin et al, ).…”

“…Our approach can be used to extract almost continuous profiles along a fiber cable network and could eventually be repeated through time at little additional cost. We illustrate our method using the Stanford DAS array (Figure ), which consists of a fiber cable laying in an air‐filled PVC conduit (no clamping or cementing) (Martin et al, ). Our results suggest that if a standard velocimeter (i.e, seismometer) is close to a DAS array, similar analysis could be performed on many existing fiber‐optic networks around the world.…”

A Scalable Algorithm for Cross-correlations of Compressed Ambient Seismic Noise

“…Since September 2016, the Stanford Fiber Optic Seismic Observatory has been continuously collecting passive seismic data at a rate of 50 samples per second at 4 meter channel spacing on 2.5 km of fiber optic cables in existing telecommunications conduits underneath the Stanford University campus (Martin et al, 2018). It is the longest running, ultra-dense urban seismic experiment, and has been used to ambient noise interferometry, earthquake detection (Lindsey et al, 2017), and active seismic acquisition (Martin et al, 2017). An array with similar settings that covered 1000 km (a reasonable length for an array covering a major metropolitan area in an earthquake-prone region) would collect 0.5 petabytes of data annually, so some form of lossy compression will need to be part of any scalable plan for future DAS data archiving.…”