DAS applications for near-surface characterization and traffic conditions assessment

Raúl Cova, Heather K. Hardeman-Vooys, Da Li and Matt McDonald


Using distributed acoustic sensing (DAS), previously deployed telecommunication optical fibres can be repurposed as permanent seismic sensors. The ability of this system to acquire data for large distances (>10 km) and with a dense sampling (<1 m) makes this technology very attractive for near-surface monitoring and characterization. We show two applications that illustrate the potential of DAS data for these purposes. First, by using interferometric principles, we compute virtual source gathers from the ambient noise recorded by the fibre. This process allowed us to reconstruct the surface-wave propagation that would have been recorded between two different points along the fibre simulating an active source experiment. Then, dispersion spectra were computed from the data showing the ability of the DAS data to provide the necessary input for near-surface characterization methods like MASW (multichannel analysis of surface waves). A second application of DAS is explored using data acquired along the Ctrain tracks in the City of Calgary. From the raw data, it is possible to identify the signature of different sources propagating with different apparent velocities. Here, we compute the velocities of these signals by using a series of windowed τ-p transformations. Assuming that most of these signals are generated by vehicles driving along the roads next to the Ctrain tracks, this information can be used for monitoring traffic condition in terms of the velocity of the vehicles recorded at any time of the day. We also compute spatial average velocities, vehicle density and estimated travel times that can be used to interpret changes in traffic conditions throughout the day in a given section of the road.

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