Broadband Rayleigh-wave imaging of railways using train vibrations

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ID: 331491
2026
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Abstract
Summary Passing trains act as correlated sources in both space and time, generating powerful waves that illuminate the near-surface below critical infrastructure. We propose an automated workflow for surface wave imaging below railway tracks that uses passing trains as the seismic source, in a context where train signals are far from an ideal isotropic source distribution. We exploit a dense 2D seismic array situated a few meters from the tracks for 3D imaging. To accommodate the complex moving source, we retain only correlations that form a limited angle with the moving train. This extends existing methods for a single line of receivers, either parallel or perpendicular to the tracks. We split the data into two cases, where the train is either far from or close to the acquisition setup. When the train is far from the array, sensor pairs mainly parallel to the tracks are important, while sensor pairs perpendicular to the tracks are decisive otherwise. In each case, the signals are time-windowed before computing the cross-correlations. Local dispersion panels are obtained through a Matched Field Processing algorithm. The dispersion curves are computed for various subsets of the array and referred to as patches. Extracting the dispersion curve of the surface wave below many patches results in a three-dimensional model of the Rayleigh wave phase velocity of the near-surface. Analyzing correlations for trains both near and far from the array yields broadband Rayleigh wave dispersion curves with high-frequency content (above 70 Hz), which is uncommon in passive methods. The velocity model is robust for wavelengths as short as 3 m and sensitive to soils at around 1 meter depth, allowing to image what may be a thicker ballast layer formed by previous track settlement issues.
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Authors Théo Rebert, Thomas Bardainne, Caifang Cai, T. Allemand, Renaud Tarnus, Lilas Vivin, Nicolas Deladerrière, Hervé Chauris
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Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag223
URL
https://doi.org/10.1093/gji/ggag223
Keywords Keywords not found

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