Joint inversion of surface wave dispersion data derived from subarrays and two-station methods

Ambient noise surface wave imaging has become a powerful tool for mapping subsurface velocity structures. Recent advancements in seismology, including the widespread deployment of high-density arrays such as nodal seismometers and Distributed Acoustic Sensing (DAS) systems, have facilitated the use of subarray-based methods for surface wave dispersion data extraction, such as phase-shift, F-K, and F-J methods. Alternatively, dispersion data can also be derived from two-station approaches, such as the FTAN method. However, integrating dispersion data extracted from subarrays and two-station methods remains challenging. In this study, we propose a joint inversion framework that combines these two types of surface wave dispersion data to achieve improved constraints on subsurface structures. We demonstrate its accuracy and practical applicability by conducting numerical experiments and applying the method to field data. The proposed approach introduces intrinsic spatial smoothing constraints. It effectively integrates subarray and two-station dispersion measurements, resulting in better imaging of subsurface shear-wave velocity structures compared to using either dataset alone. The versatility and potential of this method highlight its promising applications in a wide range of geophysical scenarios.