Software for Automated and Continuous Near-surface characterization Using Vehicle-induced DAS signals

This study proposes a novel method for detecting spatial subsurface heterogeneity and rain-induced soil saturation changes in the San Francisco Bay Area. Our approach utilizes vehicles as cost-effective surface-wave sources that excite wavefield recorded by a roadside Distributed Acoustic Sensing (DAS) array. Leveraging a Kalman filter vehicle-tracking algorithm, we can automatically track hundreds of vehicles each day, allowing us to extract space-time windows of high-quality surface waves. By constructing highly accurate virtual shot gathers from these waves, we can perform time-lapse surface-wave analyses with high temporal and spatial resolutions.

Open-source code for the following paper:
Yuan, S., Liu, J., Noh, H. Y., Clapp, R., & Biondi, B.(2023). Using Vehicle-induced DAS Signals for Near-surface Characterization with High Spatiotemporal Resolution. JGR: Solid Earth, in preparation.