Ambient-noise-based seismic monitoring of the near surface often has limited spatiotemporal resolutions because dense seismic arrays are rarely sufficiently affordable for such applications. In recent years, however, distributed acoustic sensing (DAS) techniques have emerged to transform telecommunication fiber-optic cables into dense seismic arrays that are cost effective. With DAS enabling both high sensor counts (“large N”) and long-term operations (“large T”), time-lapse imaging of shear-wave velocity (V S) structures is now possible by combining ambient noise interferometry and multichannel analysis of surface waves (MASW). Here we report the first end-to-end study of time-lapse V S imaging that uses traffic noise continuously recorded on linear DAS arrays over a three-week period. Our results illustrate that for the top 20 meters the V S models that is well constrained by the data, we obtain time-lapse repeatability of about 2% in the model domain—a threshold that is low enough for observing subtle near-surface changes such as water content variations and permafrost alteration. This study demonstrates the efficacy of near-surface seismic monitoring using DAS-recorded ambient noise.

Our understanding of subsurface processes suffers from a profound observation bias: seismometers are sparse and clustered on continents. A new seismic recording approach, distributed acoustic sensing (DAS), transforms telecommunication fiber‐optic cables into sensor arrays enabling meter‐scale recording over tens of kilometers of linear fiber length. We analyze cataloged earthquake observations from three DAS arrays with different horizontal geometries to demonstrate some possibilities using this technology. In Fairbanks, Alaska, we find that stacking ground motion records along 20 m of fiber yield a waveform that shows a high degree of correlation in amplitude and phase with a colocated inertial seismometer record at 0.8–1.6 Hz. Using an L‐shaped DAS array in Northern California, we record the nearly vertically incident arrival of an earthquake from The Geysers Geothermal Field and estimate its backazimuth and slowness via beamforming for different phases of the seismic wavefield. Lastly, we install a fiber in existing telecommunications conduits below Stanford University and show that little cable‐to‐soil coupling is required for teleseismic P and S phase arrival detection.

The paper introduces the butterfly factorization as a data-sparse approximation for the matrices that satisfy a complementary low-rank property. The factorization can be constructed efficiently if either fast algorithms for applying the matrix and its adjoint are available or the entries of the matrix can be sampled individually. For an N × N matrix, the resulting factorization is a product of O(log N ) sparse matrices, each with O(N ) non-zero entries. Hence, it can be applied rapidly in O(N log N ) operations. Numerical results are provided to demonstrate the effectiveness of the butterfly factorization and its construction algorithms.

Distributed acoustic sensing (DAS) is an emerging technology that repurposes a fiber-optic cable as a dense array of strain sensors. This technology repeatedly pings a fiber with laser pulses, measuring optical phase changes in Rayleigh backscattered light. DAS is beneficial for studies of fine-scale processes over multi-kilometer distances, long-term time-lapse monitoring, and deployment in logistically challenging areas (e.g., high temperatures, power limitations, land access barriers). These benefits have motivated a decade of applications in subsurface imaging and microseismicity monitoring for energy production and carbon sequestration. DAS arrays have recorded microearthquakes, regional earthquakes, teleseisms, and infrastructure signals. Analysis of these wavefields is enabling earthquake seismology where traditional sensors were sparse, as well as structural and near-surface seismology. These studies improved understanding of DAS instrument response through comparison with traditional seismometers. More recently, DAS has been used to study cryosphere systems, marine geophysics, geodesy, and volcanology. Further advancement of geoscience using DAS requires several community efforts related to instrument access, training, outreach, and cyberinfrastructure. ▪ DAS is a seismic acquisition technology repurposing fiber optics as arrays of dynamic strain sensors at 1- to 10-m spacing over kilometers. ▪ Easy DAS installations have availed time-lapse geophysical sensing in formerly inaccessible sites: urban, icy, and offshore areas. ▪ High-frequency wavefields recorded by DAS are analyzed with array-based methods to characterize seismic sources and image the subsurface. ▪ DAS has shown low-frequency sensitivity in the laboratory and field, for slow hydrodynamic and geodynamic processes. Expected final online publication date for the Annual Review of Earth and Planetary Sciences, Volume 49 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Accurate ground motion prediction requires detailed site effect assessment, but in urban areas where such assessments are most important, geotechnical surveys are difficult to perform, limiting their availability. Distributed acoustic sensing (DAS) offers an appealing alternative by repurposing existing fiber‐optic cables, normally employed for telecommunication, as an array of seismic sensors. We present a proof‐of‐concept demonstration by using DAS to produce high‐resolution maps of the shallow subsurface with the Stanford DAS array, California. We describe new methods and their assumptions to assess H/V spectral ratio—a technique widely used to estimate the natural frequency of the soil—and to extract Rayleigh wave dispersion curves from ambient seismic field. These measurements are jointly inverted to provide models of shallow seismic velocities and sediment thicknesses above bedrock in central campus. The good agreement with an independent survey validates the methodology and demonstrates the power of DAS for microzonation.