Data for Near-Surface Characterization Using Classified Vehicle-Induced Surface Waves from DAS

In this study, we characterize surface waves generated by vehicles of varying sizes and speeds to provide insights into accurate and efficient near-surface imaging using vehicle-induced DAS data. We employ a specialized Kalman filter algorithm to track vehicle locations and classify them into lightweight, midweight, and heavyweight based on the maximum amplitudes of quasi-static DAS records. Vehicles are also classified by their traveling speed (slow, medium, and fast) using their arrival times at DAS channels. Virtual shot gathers for the same class of vehicles are constructed from their surface wave windows selected using the tracked vehicle trajectories. We analyze the dispersion of the phase velocity to investigate the influence of vehicle characteristics on the induced surface waves. Put these pickle files into '/das_diff_veh/data/sw_data/' and run the scripts at https://github.com/jingxiaoliu/das_diff_veh If you found this useful, please cite our papers: [1] Liu, J., Li, H., Yuan, S., Noh, H. Y., & Biondi, B. (2024). Characterizing Vehicle-Induced Distributed Acoustic Sensing Signals for Accurate Urban Near-Surface Imaging. arXiv preprint arXiv:2408.14320. [2] Yuan, S., Liu, J., Noh, H. Y., Clapp, R., & Biondi, B. (2024). Using vehicle‐induced DAS signals for near‐surface characterization with high spatiotemporal resolution. Journal of Geophysical Research: Solid Earth, 129(4), e2023JB028033. [3] Liu, J., Yuan, S., Dong, Y., Biondi, B., & Noh, H. Y. (2023). TelecomTM: A fine-grained and ubiquitous traffic monitoring system using pre-existing telecommunication fiber-optic cables as sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 7(2), 1-24.