Throughout the recent COVID‐19 pandemic, real‐time measurements about shifting use of roads, hospitals, grocery stores, and other public infrastructure became vital for government decision makers. Mobile phone locations are increasingly assimilated for this purpose, but an alternative, unexplored, natively anonymous, absolute method would be to use geophysical sensing to directly measure public infrastructure usage. In this paper, we demonstrate how fiber‐optic distributed acoustic sensing (DAS) connected to a telecommunication cable beneath Palo Alto, CA, successfully monitored traffic over a 2‐month period, including major reductions associated with COVID‐19 response. Continuous DAS recordings of over 450,000 individual vehicles were analyzed using an automatic template‐matching detection algorithm based on roadbed strain. In one commuter sector, we found a 50% decrease in vehicles immediately following the order, but near Stanford Hospital, the traffic persisted. The DAS measurements correlate with mobile phone locations and urban seismic noise levels, suggesting geophysics would complement future digital city sensing systems.

Structural imaging and event location require an accurate estimation of the seismic velocity.However, active seismic surveys used to estimate it are expensive and time-consuming. During the last decade, fiber-optic-based distributed acoustic sensing has emerged as a reliable, enduring, and high-resolution seismic sensing technology. We show how downhole distributed acoustic sensing passive records from the San Andreas Fault Observatory at Depth can be used for seismic velocity estimation. Using data recorded from earthquakes propagating near-vertically, we compute seismic velocities using first-break picking as well as slant stack decomposition. This methodology allows for the estimation of both P and S wave velocity models. We also use records of the ambient seismic field for interferometry and P wave velocity model extraction. Results are compared to a regional model obtained from surface seismic as well as a conventional downhole geophone survey. We find that using recorded earthquakes, we obtain the highest P wave model resolution. In addition, it is the only method that allows for S wave velocity estimation. Computed P and S models unravel three distinct areas at the depth range of 50-750 m, which were not present in the regional model. In addition, we find high V P /V S values near the surface and a possible V P /V S anomaly about 500 m deep. We confirm its existence by observing a strong S-P mode conversion at that depth.

Due to the broadband nature of distributed acoustic sensing (DAS) measurement, a roadside section of the Stanford DAS-2 array can record seismic signals from various sources. For example, it measures the earth's quasistatic deformation caused by the weight of cars (less than 0.8 Hz) as well as Rayleigh waves induced by earthquakes (less than 3 Hz) and by dynamic car-road interactions (3–20 Hz). We directly utilize the excited surface waves for shallow shear-wave velocity inversion. Rayleigh waves induced by passing cars have a consistent fundamental mode and a noisier first mode. By stacking dispersion images of 33 passing cars, we obtain stable dispersion images. The frequency range of the fundamental mode can be extended by adding the low-frequency earthquake-induced Rayleigh waves. Due to the extended frequency range, we can achieve better depth coverage and resolution for shear-wave velocity inversion. To assure clear separation from Love waves and to align apparent and true phase velocities, we choose an earthquake that is approximately in line with the array. The inverted models match those obtained by a conventional geophone survey, performed using active sources by a geotechnical service company contracted by Stanford University, from the surface to about 50 m. To automate the VS inversion process, we introduce a new objective function that avoids manual dispersion curve picking. We construct a 2D VS profile by performing independent 1D inversions at multiple locations along the fiber. From the low-frequency quasistatic deformation recordings, we also invert for a single Poisson's ratio at each location along the fiber. We observe spatial heterogeneity of both VS and Poisson's ratio profiles. Our approach is less expensive than ambient field interferometry, and reliable estimates can be obtained more frequently because no lengthy crosscorrelations are required.

We compare the performance of a downhole distributed acoustic sensing (DAS) fiber-optic array with that of conventional geophones. The downhole collocated arrays are part of the Frontier Observatory for Research in Geothermal Energy (FORGE) geothermal experiment, in which stimulation of the rock volume in an enhanced geothermal system (EGS) causes microseismic events. The DAS acquisition system yields data sampled at every 1 m at 2000 samples per second for the entire length of the well, spanning to a depth of 985 m from the surface. Whereas single DAS channels are substantially noisier than geophones at the same location, their large number and spatial coherency allow for the application of effective array processing techniques. We follow a complete workflow for the fiber-optic array: velocity model building, event detection, event location, and magnitude estimation. Estimated velocity models agree well with sonic logging in a nearby well and map a granitic contact accurately. Detection performance is somewhat worse than geophones and yields magnitude completeness of −1.4 compared to −1.7 for geophones. Using a single vertical fiber array, we cannot retrieve the azimuth of the events relative to the well. However, we can very accurately estimate their depth and horizontal distance from the array. Magnitude estimation with DAS approaches geophone results to within a standard deviation of M=0.115 and negligible mean difference. The DAS processing results outperform a regional and local surface array, consolidated with a shallow borehole sensor. Although downhole geophones in the FORGE experimental layout performed better, DAS holds several critical practical benefits that were not demonstrated. Thanks to its heat resistance, it can be deployed much closer to the reservoir; fibers can be deployed along cased active wells, eliminating the need for a dedicated monitoring well; the permanently installed fiber can be used for years or decades. Therefore, we argue that DAS holds vast potential for long-term monitoring of EGS projects.

Distributed Acoustic Sensing (DAS) is gaining vast popularity in the industrial and academic sectors for a variety of studies. Its spatial and temporal resolution is ever helpful, but one of the primary benefits of DAS is the ability to install fibers in boreholes and record seismic signals in depth. With minimal operational disruption, a continuous sampling along the trajectory of the borehole is made possible. Such resolution is highly challenging to obtain with conventional downhole tools. This review article summarizes different seismic uses, passive and active, of downhole DAS. We emphasize current DAS limitations and potential ways to overcome them.