In a context where low-carbon transportation is increasingly essential, the diagnosis and maintenance of railway infrastructures have become critical challenges. Current assessment techniques still rely heavily on destructive testing of embankments, sublayers, and underlying soils. These structures are also exposed to more frequent and less predictable extreme climatic events, threatening their mechanical integrity and long-term stability. High-density, high-resolution geophysical methods therefore offer a compelling non-destructive alternative, particularly for characterizing and monitoring soil mechanical properties. Over the past decade, major advances have been made in seismic acquisition, processing, and interpretation. We present an overview of our recent contributions, primarily based on surface-wave methods, which require low-energy sources and are well adapted to railway environments. We developed high-yield acquisition strategies using landstreamers, combined with classic active and passive (train) sources. Stacking and interferometry-based approaches extract multimodal dispersion images, enabling detection of lateral variations within embankments and continuous site monitoring. Deep learning supports semi-automatic picking and interpretation, while Bayesian inversion and physics-aware AI models integrate geotechnical and hydrogeological data to rapidly infer petro- and hydro-facies and track water-table dynamics. We conclude with a roadmap for future developments, including the integration of distributed acoustic sensing.
Bodet et al. (Mon,) studied this question.
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