Application of six-component ambient seismic noise data for high-resolution imaging of lateral heterogeneities

Abstract

SUMMARY We develop a novel approach for imaging subsurface lateral heterogeneities using six-component (6C) ambient seismic noise data, consisting 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 the 6C CCFs for surface wave dispersion curves. The phase velocities of Rayleigh and Love waves can be directly calculated from the 6C CCFs of a single pair of stations. Traditional array-based surface wave methods derive the surface wave phase velocity based on the presumption of a horizontally layered model. When this assumption breaks down due to the presence of, for example, dipping layer or heterogeneities, the resulting phase velocity can be severely smeared. By contrast, the proposed approach is not limited by the layered model assumption because it relies on single-point measurements to calculate the localized dispersion relations of the formation right beneath a receiver. Our numerical modelling results demonstrate that this approach is applicable to heterogeneous models and can image small-scale subsurface anomalies with very high lateral resolution.