On research of dispersion characteristics of multi-component surface waves from traffic-induced seismic ambient noise

As one of the commonly used sources in passive surface wave exploration, traffic-induced seismic ambient noise is widely used in urban underground velocity structure detection at present. However, characteristics of the dispersion curve extracted from such kind of ambient noise lack systematical research, especially for the multicomponent surface wave. Due to the complex character of the real-world underneath structures, dispersion characteristics concluded from real-world traffic-induced seismic ambient noise that is hard to verify. The numerical simulation technique is a good choice for systematical research of characteristics of dispersion curves extracted from multi-component traffic-induced seismic ambient noise. Most of the numerical examples that appeared in the present literature, however, are based on the dispersion-curve-based numerical simulation technique. Hence, most of these can only simulate the vertical component. In this paper, the two-dimensional finite difference method is used to simulate multi-component traffic seismic ambient noise. The characteristics of original traffic noise are analyzed, and the effect of trace intervals on surface wave imaging of different types of traffic-induced noise is studied. Rayleigh wave dispersion energy image is extracted by the passive surfacewave frequency wavenumber transformed method. The differences in Rayleigh-wave dispersion images in traffic noise with different components are analyzed and compared. The results show that Rayleigh wave dispersion energy images appearing different characteristics in different components. The surface wave dispersion information presented by different types of traffic noise is frequency-dependent. The trace spacing also strongly affect the surface-wave dispersion energy images extracted from different types of traffic-induced noise. The numerical results are significant for guiding the traffic-induced seismic noise data acquisition and processing.