Reconstruction of surface waves Green's function using array-derived rotaional motion

The characterization of seismic wavefields demands a comprehensive understanding of both translational and rotational motions. In this study, rotational motions were indirectly derived from seismic arrays through the array-derived rotational (ADR) method and combined with the translational components from the central station of each array, resulting in six-component (6C) seismic data. Using these 6C stations, cross-correlation functions were computed to retrieve Empirical Green’s Functions (EGFs) for surface waves, with a focus on Rayleigh and Love waves. The methodology was applied to ambient noise data from six seismic arrays monitoring geothermal stimulation near Helsinki, Finland. Our results reveal that integrating rotational and translational data improves the retrieval of surface wave dispersion characteristics, particularly in identifying group and phase velocities. Comparing ballistic arrivals between array pairs, we observed that rotational components effectively enhance the resolution of both Rayleigh and Love wave features. These findings underscore the potential of combining ADR and translational data for advanced seismic wavefield analysis in ambient noise studies.