Characterizing the Surrounding Medium of an Optical Fiber Network Using Distributed Acoustic Sensing (DAS)

Optical fiber sensing method, such as Distributed Acoustic Sensing (DAS), are increasingly emerging as a versatile sensing platform in geosciences, offering high-resolution, continuous monitoring across large areas. Leveraging existing deployed fibers from telecommunication networks provides a cost-effective solution for large-scale applications, ranging from seismic monitoring and environmental surveillance to structural health assessment.

However, when utilizing pre-existing near-surface fiber infrastructure, the mechanical coupling between the fiber cable and the surrounding medium is highly variable and completely uncontrolled. This fluctuating coupling strongly influences the detected signal amplitude and frequency domain response. While it presents a challenge for quantization of  amplitude analysis, assessing these local variations also offers a unique opportunity to probe and characterize the immediate shallow surroundings of the cable.

In this study, we investigate the impact of local environmental conditions on DAS signal using an urban telecommunication fiber network. Controlled surface seismic experiments were conducted across various deployment configurations, including sections buried in sand, passing through technical chambers (manholes), and embedded beneath asphalt. The acquired active-source data were analyzed in both the time and frequency domains to isolate the signatures of each distinct environment and extract the specific acoustic response of the cable’s surroundings.

Our preliminary results demonstrate that different deployment media introduce distinct spectral fingerprints and attenuation patterns in the DAS records. This analysis provides valuable insights into how unconstrained coupling filters the seismic wavefield.  Ongoing and future work will focus on expanding the dataset, refining the transfer functions between the soil and the cable, and decoupling the intrinsic cable properties from the medium response to improve the reliability of urban DAS ambient noise and event monitoring.