Multigrid Spatially Constrained Dispersion Curve Inversion for Distributed Acoustic Sensing (DAS)

Surface wave methods, commonly applied in diverse fields, encounter challenges in complex subsurface environments due to limitations inherent in traditional inversion techniques. Conventional 1-D inversion (1DI), with its reliance on fixed grids and deterministic linear approaches, often introduces biases, diminishing lateral resolution. Laterally constrained inversion (LCI) improves robustness by addressing lateral coherency but falls short in delineating arbitrary interfaces due to its dependence on fixed grid models. The advent of distributed acoustic sensing (DAS) technology offers extensive seismic data, yet its potential for high-resolution imaging remains underutilized. We introduce a multigrid spatially constrained dispersion curve inversion (MCI) method to overcome these challenges, aiming to harness high-resolution DAS surface wave imaging capabilities. This article details the MCI scheme, evaluates its efficacy through synthetic tests, and applies it to a DAS field study in Imperial Valley, CA, USA. Our findings demonstrate a refined, higher resolution S-wave velocity model, offering new insights into the region's fault system and emphasizing the necessity of improved spatial resolution in large-scale geophysical studies.