Near-surface characterization using a roadside distributed acoustic sensing array

Siyuan Yuan*, Ariel Lellouch, Robert G. Clapp, Biondo Biondi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)646-653
Number of pages8
JournalLeading Edge
Issue number9
StatePublished - 1 Sep 2020
Externally publishedYes


FundersFunder number
Stanford Exploration Project


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