Ambient noise interferometry using chirped-pulse DAS data for subsurface exploration

This study demonstrates the potential of Chirped-Pulse Distributed Acoustic Sensing (DAS) technology for subsurface imaging through the application of Ambient Noise Interferometry (ANI). By analyzing strain data, we successfully extracted geological insights without relying on active seismic sources. The low-intensity signals were isolated and enhanced using ANI techniques such as FK filtering, temporal normalization and spectral whitening. Using a custom ANI algorithm, velocity dispersion maps were generated from 24 hours of continuous DAS recordings. These maps revealed dispersion relationships corresponding to different terrain layers. The methodology employed, including Phase-Weighted Stacking and slant-stacking, proved effective for reducing noise and isolating surface wave propagation signals within the frequency range of 0.5–15 Hz. This versatile and cost-effective approach is well-suited for high-resolution, non-invasive subsurface imaging in diverse environments, from submarine cables to urban settings.