Multidimensional reconstruction of noise correlation functions and its application in improving surface-wave inversion

Abstract

The cross-correlation of the ambient noise recordings, also known as noise correlation functions (NCFs), can converge to Green’s functions (GFs) which describe wave propagation between a pair of stations. However, the NCFs are often biased from the true GFs due to the presence of random noise and spurious arrivals arising from non-diffuse wavefields. Additionally, the limited spatial and temporal coverage of recording stations can lead to large data gaps in the retrieved virtual shot gathers, particularly at large interstation distances (far offsets). Both these factors impose great challenges to retrieving high-quality NCFs and conducting reliable subsurface imaging. In this study, we propose a multidimensional (4-D) reconstruction method to compensate for the insufficient station coverage and simultaneously attenuate incoherent noise in the NCFs. We test the feasibility of the proposed method using a dense seismic array deployed in western Canada. Our results demonstrate that the reconstructed virtual common midpoint gather can greatly improve the stability and reliability of the surface-wave dispersion measurements and subsequent shear velocity inversions compared to the conventional approaches. The proposed ambient noise processing framework enables us to construct accurate 3-D velocity model of the subsurface.