Near-surface Characterization Using Ambient Noise Recorded by Fiber-optic Distributed Acoustic Sensing

Summary Fiber-optic Distributed Acoustic Sensing (DAS) technology has recently garnered significant attention across various fields, including engineering, oil exploration, and seismological research. Compared to traditional geophones, DAS offers the advantages of high-resolution data acquisition through dense spatial sampling and ease of deployment in challenging environments. These attributes position DAS as a promising tool for near-surface characterization in engineering geological exploration and monitoring. In this study, a DAS array was employed to capture seismic data generated by ambient noise, enabling an investigation into near-surface structures. Surface waves were extracted from the recorded subtle natural disturbances by using seismic interferometry techniques. Subsequently, the near-surface shear wave velocity profile along the DAS array was derived through inversion of surface wave dispersion curves using a genetic algorithm optimization approach. The findings highlight the efficacy of DAS in leveraging ambient noise for near-surface characterization.