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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. [ABSTRACT FROM AUTHOR]

Abstract Acoustic models of railway vehicles in standstill and pass-by conditions can be used as part of a virtual certification process for new trains. For each piece of auxiliary equipment, the sound power measured on a test bench is combined with measured or predicted transfer functions. It is important, however, to allow for installation effects due to shielding by fairings or the train body. In the current work, fast-running analytical models are developed to determine these installation effects. The model for roof-mounted sources takes account of diffraction at the corner of the train body or fairing, using a barrier model. For equipment mounted under the train, the acoustic propagation from the sides of the source is based on free-field Green’s functions. The bottom surfaces are assumed to radiate initially into a cavity under the train, which is modelled with a simple diffuse field approach. The sound emitted from the gaps at the side of the cavity is then assumed to propagate to the receivers according to free-field Green’s functions. Results show good agreement with a 2.5D boundary element model and with measurements. Modelling uncertainty and parametric uncertainty are evaluated. The largest variability occurs due to the height and impedance of the ground, especially for a low receiver. This leads to standard deviations of up to 4 dB at low frequencies. For the roof-mounted sources, uncertainty over the location of the corner used in the equivalent barrier model can also lead to large standard deviations.

Acoustic models of railway vehicles in standstill and pass-by conditions can be used as part of a virtual certification process for new trains. For each piece of auxiliary equipment, the sound power measured on a test bench is combined with measured or predicted transfer functions. It is important, however, to allow for installation effects due to shielding by fairings or the train body. In the current work, fast-running analytical models are developed to determine these installation effects. The model for roof-mounted sources takes account of diffraction at the corner of the train body or fairing, using a barrier model. For equipment mounted under the train, the acoustic propagation from the sides of the source is based on free-field Green's functions. The bottom surfaces are assumed to radiate initially into a cavity under the train, which is modelled with a simple diffuse field approach. The sound emitted from the gaps at the side of the cavity is then assumed to propagate to the receivers according to free-field Green's functions. Results show good agreement with a 2.5D boundary element model and with measurements. Modelling uncertainty and parametric uncertainty are evaluated. The largest variability occurs due to the height and impedance of the ground, especially for a low receiver. This leads to standard deviations of up to 4 dB at low frequencies. For the roof-mounted sources, uncertainty over the location of the corner used in the equivalent barrier model can also lead to large standard deviations. [ABSTRACT FROM AUTHOR]