HDAS (High-Fidelity Distributed Acoustic Sensing) as a seismic surface wave monitoring tool along trackside dark fibers

Distributed Acoustic Sensing (DAS) is a fiber-optic sensing technology that transforms optical fiber telecommunication cables into arrays of thousands of broadband strain meters. The emergence of DAS has spurred significant advancements across various scientific domains, including geophysics, seismology, hydrology, and engineering. Modern DAS systems offer spatial resolutions of several meters, ranges extending tens of kilometers, sensitivities below 1 nε, and sampling rates of up to several kHz.

Focusing on systems utilizing chirped pulse distributed acoustic sensing (as implemented by High-Fidelity Distributed Acoustic Sensing (HDAS) from Aragon Photonics Labs), these techniques demonstrate enhanced performance, achieving an optimal balance between range and sensitivity, particularly at low frequencies. In seismology, these capabilities enable the high-resolution detection of seismic waves originating from events such as local and teleseismic earthquakes, as well as micro-seismic vibrations induced by trains or vehicles.

DAS has proven effective in railway monitoring, enabling vehicle tracking and rail condition assessments. Its spatial and temporal density makes it especially promising for monitoring and control in high-speed railway systems. This work applies methods adapted from array seismology to visualize seismic surface waves generated by trains and other vehicles near a trackside dark fiber. These relatively simple methodologies efficiently extract and characterize surface waves propagating along the railway superstructure.

The DAS data collected from trackside fibers provide substantial information about terrain features and the condition of railroad superstructures. These findings highlight the potential of DAS systems for monitoring seismic surface waves and superstructure conditions using pre-installed fibers. Moreover, the evolution of this information over time can offer valuable insights for infrastructure owners, particularly in critical scenarios such as high-speed railway systems. Additionally, the local dispersion relation for surface waves reveals further details about the superstructure, which could support preventive maintenance efforts.