Low-frequency Ambient Distributed Acoustic Sensing (DAS): Useful for subsurface investigation?

Distributed Acoustic Sensing (DAS) is a rapidly growing novel sensing method for seismic data acquisition. DAS arrays are particularly well-suited for dense recording of low-frequency ambient surface waves on long (> 5km) linear sections of deployed optical fiber cable. Applying multi-channel analysis of surface waves (MASW) to ambient wavefield DAS recordings characterized by a large number of sensing points and long recording times may enable 1D characterization of the S-wave velocity profile to depths of 750 m or greater. We present a low-frequency ambient wavefield investigation using a DAS dataset acquired on an array deployed in suburban Perth, Australia. We extract storm-induced swell noise from the nearby Indian Ocean in a low-frequency band (0.1 - 1.8 Hz) and generate virtual shot gathers by applying cross-correlation and deconvolution seismic interferometric analyses. The resulting gathers are transformed into dispersion images through two different methods: phase shift and high-resolution linear Radon transform. To recover the near-surface S-wave velocity model, we first pick and then invert the recovered 1D Rayleigh-wave dispersion curves using a particle-swarm optimization algorithm. Inversion results show that low-frequency ambient-wavefield DAS data can constrain the V_s model to 750m depth, which helps validate the potential of DAS technology as a tool for large-scale surface-wave investigation.