PAC, a User-Friendly App for Hybrid Active-Passive MASW along Linear Geotechnical Infrastructures: Application to Advanced Seismic Diagnosis of Railway Embankments

In a context where low-carbon transport is becoming increasingly essential, the diagnosis and maintenance of railway infrastructure have become critical issues. 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 weather 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 the mechanical properties of soils. Over the past decade, major advances have been made in seismic acquisition, processing, and interpretation. We present an overview of our recent contributions, mainly based on surface wave-methods, which require low energy sources and are well suited to railway environments (Cunha Teixeira et al., 2025a). We have developed high-efficiency acquisition strategies using landstreamers, combined with conventional active sources (weight drop or hammer) and passive sources (induced by trains or traffic). We present PAC (Cunha Teixeira et al., 2025b), a user-friendly application designed for processing multichannel analysis of surface waves (MASW). It integrates stacking and interferometry-based approaches to extract multimodal dispersion images, enabling the detection of lateral variations within embankments or continuous site monitoring. Deep learning supports semi-automatic picking, while Bayesian inversion (Burzawa et al., 2025) facilitates the interpretation of mechanical models and aids reliable decision-making in railway infrastructure management.

References:

Burzawa, A., Bodet, L., Dangeard, M., Barrett, B., Byrne, D., Whitehead, R., Chaptal, C., Cunha Teixeira, J., Cárdenas, J., Sanchez Gonzalez, R., Eriksen, A., Dhemaied, A. (2025). Efficient mechanical evaluation of railway earthworks using a towed seismic array and Bayesian inference of MASW data. arXiv preprint https://doi.org/10.48550/arXiv.2507.16491

Cunha Teixeira, J., Bodet, L., Rivière, A., Solazzi, S.G., Hallier, A., Gesret, A., El Janyani, S., Dangeard, M., Dhemaied, A., Boisson Gaboriau, J. (2025a). Neural machine translation of seismic ambient noise for soil nature and water saturation characterization. Geophysical Research Letters, 52(13) https://doi.org/10.1029/2025GL114852

Cunha Teixeira, J., Burzawa, A., Bodet, L., Hallier, A., Decker, B., Lin, F., Dangeard, M., Boisson Gaboriau, J., & Dhemaied, A. (2025b). Passive and Active Computation of MASW (PAC). Zenodo. https://doi.org/10.5281/zenodo.17639980