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

Virtual shot gathers with one car signal	Virtual shot gathers with 236 car signals

Disperson image with one car signal	Disperson image with 236 car signals

This is the repository for the following paper. If you use this implementation, please cite our papers:

• 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.

[paper]

and

• Jingxiao Liu, Siyuan Yuan, Yiwen Dong, Biondo Biondi, and Hae Young Noh. 2023. TelecomTM: A Fine-Grained and Ubiquitous Traffic Monitoring System Using Pre-Existing Telecommunication Fiber-Optic Cables as Sensors. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 7, 2, Article 64 (June 2023), 24 pages. https://doi.org/10.1145/3596262

[paper] [code]

Description

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.

Code Usage

git clone https://github.com/das_veh

• Run the demo example with

jupyter notebook demo.ipynb

Contact

Feel free to send any questions to:

• Siyuan Yuan

Note: The telecommunication cable DAS data used to support the fndings of this study are available from the corresponding author upon request.