Low-frequency ambient distributed acoustic sensing (DAS): case study from Perth, Australia. Issue 1 (26th March 2021) - Shragge, Jeffrey; Yang, Jihyun; Issa, Nader; Roelens, Michael; Dentith, Michael; Schediwy, Sascha
SUMMARY: Ambient wavefield data acquired on existing (so-called 'dark fibre') optical fibre networks using distributed acoustic sensing (DAS) interrogators allow users to conduct a wide range of subsurface imaging and inversion experiments. In particular, recorded low-frequency (<2 Hz) surface-wave information holds the promise of providing constraints on the shear-wave velocity ( VS ) to depths exceeding 0.5 km. However, surface-wave analysis can be made challenging by a number of acquisition factors that affect the amplitudes of measured DAS waveforms. To illustrate these sensitivity challenges, we present a low-frequency ambient wavefield investigation using a DAS data set acquired on a crooked-line optical fibre array deployed in suburban Perth, Western Australia. We record storm-induced microseism energy generated at the nearby Indian Ocean shelf break and/or coastline in a low-frequency band (0.04−1.80 Hz) and generate high-quality virtual shot gathers (VSGs) through cross-correlation and cross-coherence interferometric analyses. The resulting VSG volumes clearly exhibit surface wave energy, though with significant along-line amplitude variations that are due to the combined effects of ambient source directivity, crooked-line acquisition geometry and the applied gauge length, fibre coupling, among other factors. We transform the observed VSGs into dispersion images using two different methods: phase shift and high-resolution linear Radon transform. These dispersion images are then used to estimate 1-D near-surface VS models using multichannel analysis of surface waves (MASW), which involves picking and inverting the estimated Rayleigh-wave dispersion curves using the particle-swarm optimization global optimization algorithm. The MASW inversion results, combined with nearby deep borehole information and 2-D elastic finite-difference modeling, show that low-frequency ambient DAS data constrain the VS model, including a low-velocity channel, to at least 0.5 km depth. Thus, this case study illustrates the potential of using DAS technology as a tool for undertaking large-scale surface wave analysis in urban geophysical and geotechnical investigations to depths exceeding 0.5 km.