Characterization of shallow karst zones using distributed acoustic sensing and ambient noise tomography: A case study in Mufu Mountain, China

Karst regions pose significant challenges for underground engineering due to their intricate subsurface structures. Accurately imaging these shallow subsurface areas is important for ensuring the safety and stability of construction projects. In the Mufu Mountain region of Nanjing, China, we applied fiber-optic distributed acoustic sensing (DAS) technology to detect karst fracture zones through ambient noise tomography. Our 24-h ambient noise data analysis elucidates urban noise patterns, providing a robust foundation for short-term engineering applications using ambient noise tomography. This method utilized high-density seismic data recorded over a period of 7 h. Phase-velocity dispersion curves of Rayleigh waves, extracted from spectrograms computed using the frequency-Bessel (F-J) method from ambient noise, were subsequently inverted with the competitive particle swarm optimization (CPSO) algorithm to construct a detailed 2D S-wave velocity profile down to 80 m. The analysis revealed complex subsurface features, including stratigraphic conditions and karst formations, which aligned with borehole data. This non-invasive and cost-effective methodology highlights the potential of DAS technology for high-resolution urban subsurface imaging, offering significant benefits for geological assessments, urban planning, and infrastructure monitoring in complex environments.