Using vehicle-induced DAS signals on dark fiber for MASW and monitoring of spatio-temporal variations of near-surface ground properties

Fiber-optic telecommunication cables are commonly installed along major transport corridors such as highways and railways. Traffic along these infrastructures continuously excites the shallow subsurface, generating surface waves whose dispersive behavior can be analyzed to investigate near-surface structures. Applying Distributed Acoustic Sensing (DAS) to dark fiber deployed along such corridors makes it possible to record these signals with dense spatial sampling over large distances, thereby offering new opportunities for passive imaging and/or monitoring of the shallow subsurface.

In this study, we investigate the potential of DAS recordings acquired on dark fiber to perform Multi-Channel Analysis of Surface Waves (MASW) using traffic-induced seismic sources. Our analysis is based on two independent datasets: one collected along a main road crossing the Karlsruhe Institute of Technology (KIT) campus, and another acquired along a railway line. We introduce a comprehensive processing framework including (i) automated vehicle detection and tracking along the fiber, independent of prior knowledge of vehicle trajectories or source locations; (ii) surface-wave analysis based on cross-correlation to retrieve virtual shot gathers (VSGs); and (iii) a stacking strategy designed to enhance coherent surface-wave energy while suppressing noise, improving the resolution of dispersion spectra and enabling robust dispersion-curve estimation even in challenging, high-noise environments. The stacking strategy enables the analysis of temporal variations in the retrieved dispersion characteristics, beside resolving spatial variations along the cable.