Multi-Scale Surface-Wave Imaging Using Distributed Acoustic Sensing and Voronoi Tessellation

Extreme climate events and geological hazards have underscored the urgency of advancing multi-scale seismic imaging to accurately characterize the Earth system. Despite the widespread application of seismic surface wave methods based on dispersion analysis, which have been used for S-wave velocity imaging across scales from the critical zone (meter-scale) to the crust and mantle (kilometer-scale), high-resolution integrated imaging across different scales remains underexplored due to limitations in observational configurations and inversion techniques. In this study, we fully exploit the potetial of Distributed Acoustic Sensing (DAS) for cross-scale ultra-high-density observations and develop a compatible and pragmatic multi-scale surface wave imaging strategy based on Voronoi tessellation with grid cells adapted to dispersion data sensitivity kernels. A 2D dispersion curve inversion kernel and multigrid constrained update strategy are also integrated to this strategy to improve inversion accuracy and computational efficiency. More importantly, the strategy allows for the quantitative assessment of inversion result uncertainties and resolution, enhancing model reliability and guiding interpretation. The efficacy of this framework is validated through synthetic tests and applied to a seabed DAS field study in Monterey Bay, California. We demonstrate a refined S-wave velocity model with higher resolution and deeper illumination depth, offering new insights into the fault system and paleogeographic history of the region, especially paleochannel evolution. The results contribute to reconstructing past geological processes and understanding their influence on contemporary geohazards and subsurface dynamics. Our findings emphasize the necessity for multiscale imaging in large-scale geophysical studies.