Imaging of train noise with heavy traffic events recorded by distributed acoustic sensing

Abstract

Train noise is a kind of green, non-destructive and strong-energy artificial seismic sources, which is widely used in railway safety monitoring, near-surface imaging and urban underground space exploration. Distributed acoustic sensing is a new seismic acquisition technology, which has the advantages of dense sampling, simple deployment and strong anti-electromagnetic interference ability. In recent years, distributed acoustic sensing has been gradually applied in the fields of urban traffic microseism monitoring, crack detection and underground space imaging. However, previous studies mainly focused on microseism interferometry using train event coda noise, and there is limited research on the workflow of interferometry imaging using distributed acoustic sensing–based heavy train events noise (with short coda windows), which produces an abundant of near-source interference. Aiming at proving the effectiveness of this idea, we investigated a process workflow to get underground shear-velocity structure based on distributed acoustic sensing recorded heavy traffic noise near Qinhuangdao train station. A weighted sliding absolute average method is used to weaken the strong amplitude to the coda wave level and reduce the near-source influence. We demonstrated that the cross-coherence interferometry method, after spectral whitening, has the best effect on sidelobe suppression in the virtual source surface wave shot gathers, through a comparative analysis of cross-correlation and cross-coherence results. For obtaining concentrated energy and strong continuity in phase velocity spectra, we selected the time windows with high spatial coherence and signal-to-noise ratio not less than 1.2 for stacking from 720 time windows in F–K domain. When dividing subarrays to extract pseudo-two-dimensional profile, we set the overlap rate between adjacent time windows to 80% to increase stacking times, enhancing the precision of phase velocity spectra and reducing the errors of picking dispersion curve. Our results show that heavy traffic train events noise (non-pure coda) can be used to detect underground velocity structure with clear dispersion and high inversion reliability. This research provides a new processing flow for distributed acoustic sensing train noise imaging and can be applied in future urban underground space exploration.