Train-induced seismic interferometry for monitoring pore-water pressure changes along railway embankments

This study investigates the feasibility of using train-induced seismic interferometry to monitor shallow subsurface pore-water pressure (PWP) variations of shallow soft soil conditions alongside railways embankments. The research was conducted at a dedicated test site equipped with an array of pore-water pressure transducers and a pair of tri-axial accelerometers, enabling simultaneous monitoring. The triaxial accelerometers and pore-water pressure transducers were installed at two locations, close to each other, along the railways to capture the effects of passing trains on the shallow subsurface. The accelerometers, placed at a depth of 3.6 meters, were used to monitor ground vibrations, specifically the propagation of Rayleigh waves in the 1–30 Hz frequency range. The pore-water pressure transducers were positioned at multiple depths between 2 and 7 meters, with approximately 1-meter intervals between them. These transducers recorded pore-water pressure (PWP) values every hour at each specific depth.

The methodology employs seismic interferometry to extract surface waves from ambient vibrations induced by passing trains. These velocity variations are then correlated with modeled PWP changes at different depths. The results demonstrate promising correlations between measured and modeled PWP, particularly at sensitive depths where soil behavior is most critical for infrastructure stability. While the approach successfully captures general trends in PWP dynamics, discrepancies in prediction accuracy were observed, primarily due to limitations in model parameterization, frequency band selection, and data resolution.

The findings highlight the potential of seismic interferometry as a non-invasive, scalable technique for geotechnical monitoring. However, improvements in several areas are recommended to enhance reliability. Refining model parameters to better represent site-specific soil properties, optimizing frequency selection to target depth-sensitive wave modes, and increasing temporal and spatial resolution of seismic data could significantly improve predictive performance.

Furthermore, integrating Distributed Acoustic Sensing (DAS) technology offers an opportunity for real-time, large-scale monitoring by utilizing existing fiber-optic infrastructure. This integration could transform current practices by enabling continuous observation of soil-water interactions without the need for extensive sensor deployment.

This research demonstrates the viability of using train-induced seismic signals for monitoring subsurface hydromechanical processes, offering a practical alternative to conventional methods. By addressing current limitations and incorporating emerging technologies, the proposed framework has the potential to advance infrastructure monitoring, mitigate geotechnical risks, and support sustainable development in areas with challenging soil conditions.