Surface wave imaging with stationary-phase noise sources based on ultrashort traffic-induced data: An application in karst cave detection

Abstract In urban environments, abundant traffic-induced noise data are analyzed through crosscorrelation to retrieve high-frequency (> 1 Hz) surface waves, providing a cost-effective technique for detecting near-surface structures. The isotropic noise source distribution is an essential prerequisite for the correct reconstruction of the Green’s function. The azimuth of traffic noise sources, however, can change with human activities in highly populated urban areas, resulting in non-random distributions in time and space. Due to the uneven distribution of traffic noise sources, spurious signals are generated in the noise crosscorrelation functions and phase velocities calculated from the retrieved surface waves are overestimated, leading to incorrect S-wave velocity profiles. By analyzing the noise source distribution of each segment, we selected the stationary-phase segments to improve the retrieval of surface waves. We processed approximately one-day ultrashort continuous recordings to obtain virtual shot gathers with larger multichannel-coherency coefficients and dispersion images with more surface-wave dispersion data. S-wave velocity profiles for different arrays, including a 3D S-wave velocity model, were produced by inverting the surface-wave dispersion data to reveal the distribution of karst caves beneath the surface. The results demonstrate the effectiveness of the strategy of the stationary-phase segment selection and the great potential of traffic-induced surface waves in monitoring subsurface changes in urban areas.