Extraction of surface-wave phase velocities from ambient noise in the presence of local noise sources based on matched-field processing

Abstract

Passive surface wave methods have gained much attention from geophysics and civil engineering communities with the advantage of lower costs and deeper penetration depths compared to active seismic methods. The successful extraction of surface waves from ambient noise relies on the assumption of a homogeneous random distribution of noise sources. In urban environments, there are many strong noise sources located in localized areas (i.e., local sources) that violate the ideal noise distribution conditions, which causes errors in surface-wave dispersion extraction. To solve this problem, we propose a new passive surface wave analysis method in the presence of strong local noise sources (local-source passive surface wave analysis, LSPSW). We use the matched field processing (MFP) algorithm to capture noise source distributions and then extract reliable surface wave phase velocities. The MFP algorithm can accurately estimate the distribution of noise sources located either close to or far from the receiver array. Synthetic and field examples demonstrate the feasibility of using LSPSW to estimate phase velocities in complicated noise environments with a pseudo-linear array. Comparisons with other passive surface wave methods (seismic interferometry, the spatial autocorrelation method, pseudo-linear array analysis of passive surface waves based on beamforming) further illustrate the superiority of the MFP-based LSPSW method.