Characterizing Vehicle-Induced Distributed Acoustic Sensing Signals for Accurate Urban Near-Surface Imaging

Abstract

Continuous seismic monitoring of the near-surface structure is crucial for urban infrastructure safety, aiding in the detection of sinkholes, subsidence, and other seismic hazards. Utilizing existing telecommunication optical fibers as Distributed Acoustic Sensing (DAS) systems offers a cost-effective method for creating dense seismic arrays in urban areas. DAS leverages roadside fiber-optic cables to record vehicle-induced surface waves for near-surface imaging. However, the influence of roadway vehicle characteristics on their induced surface waves and the resulting imaging of near-surface structures is poorly understood. We investigate surface waves generated by vehicles of varying weights and speeds to provide insights into accurate and efficient near-surface characterization. We first classify vehicles into light, mid-weight, and heavy based on the maximum amplitudes of quasi-static DAS records. Vehicles are also classified by their traveling speed using their arrival times at DAS channels. To investigate how vehicle characteristics influence the induced surface waves, we extract phase velocity dispersion and invert the subsurface structure for each vehicle class by retrieving virtual shot gathers (VSGs). Our results reveal that heavy vehicles produce higher signal-to-noise ratio surface waves, and a sevenfold increase in vehicle weight can reduce uncertainties in phase velocity measurements from dispersion spectra by up to 3X. Thus, data from heavy vehicles better constrain structures at greater depths. Additionally, with driving speeds ranging from 5 to 30 meters per second in our study, differences in the dispersion curves due to vehicle speed are less pronounced than those due to vehicle weight. Our results suggest judiciously selecting and processing surface wave signals from certain vehicle types can improve the quality of near-surface imaging in urban environments.