Improving Data Quality of Three-Component Measurements of Noise in Urban Environments Using Polarization Analysis

Seismic interferometry (SI) is widely used to retrieve surface waves for linear arrays from anthropogenic noise in urban environments. It is difficult to accurately retrieve the surface-wave dispersion information extracted from cross correlation functions using a linear array because of the spatial and temporal variation of noise sources. It is also challenging to suppress the influence of offline noise sources by stacking noise cross correlation functions (NCFs). Data segment selection is an existing method for removing the segments with offline noise sources and utilizing the segments with in-line noise sources. We propose a two-step selection method to retain data segments that are useful for surface-wave retrieval based on polarization analysis. We retain data segments whose azimuths of noise sources are stable for a linear array in the first step. We then retain data segments whose noise sources are located within the stationary-phase zones (SPZs). The retained data segments are used to retrieve accurate high-frequency surface waves. A synthetic test is conducted to prove the superiority of the proposed method for improving data quality of Rayleigh and Love waves. Two field examples in Hangzhou also demonstrate the feasibility of the proposed method in improving the virtual shot gathers (VSGs) and dispersion images. Furthermore, the proposed method is applicable to noise sources with different azimuths and intensities. Our scheme is an option for improving data quality in urban environments where the distribution of noise sources is complex.