Numerical study of the application of six-component ambient seismic noise data for high-resolution imaging of lateral heterogeneities

We develop a novel approach for imaging subsurface lateral heterogeneities using six component (6C) ambient seismic noise data, which consists of three translational components and three rotational components. We fifirst derive the 6C cross-correlation functions (CCFs) from ambient seismic noise data for surface waves and then apply the modifified frequency Bessel (MF-J) transform to extract the 6C CCFs for surface wave fundamental modes. We only consider the fundamental modes in this study because high-order modes are easily inflfluenced by near-surface heterogeneities’ scattering. The phase velocities of Rayleigh and Love waves’ fundamental modes can be directly calculated from the 6C CCFs of a single pair of stations. Unlike traditional array-based surface wave methods that deliver some sort of average phase velocity among stations, the proposed approach gives the localized dispersion relations of the formation right beneath a receiver. Our numerical modeling results demonstrate that this approach is applicable to heterogeneous models and can image small-scale surface anomalies with very high lateral resolution.