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

Abstract

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 first derive the 6C cross-correlation functions (CCFs) from ambient seismic noise data for surface waves and then apply the modified 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 influenced 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.