| Condition monitoring


DAS strain on January 14, 2021 at 05:00


DAS strain on January 14, 2021 at 10:00


DAS strain on January 14, 2021 at 15:00


DAS strain on January 15, 2021 at 11:00


Figure 4. Illustration to visualize changes in strain observed with DAS data overlain onto lidar bare-earth imagery at the Hollin Hill slow-moving landslide in the UK.


be quantified as relative changes in seismic velocity (dv/v). Tailings storage facilities are rich in anthropogenic noise: haul trucks, conveyors, crushers, drilling, and blasting all produce persistent vibrations. While such variable noise environments might pose a challenge for traditional monitoring techniques, CWI only requires a portion of the noise field to remain stable to reliably track dv/v changes (Hadziioannou et al., 2009). Laboratory experiments have demonstrated that CWI can detect mean effective stress changes smaller than 0.1% in uncemented soils (Dai et al., 2013). This level of sensitivity holds potential for early detection of evolving instability in tailings dams, where small stress variations may signal the onset of structural weakening. Ouellet et al. (2025) applied CWI to a 120 m segment of installed fibre optic cable at the site using DAS to track dv/v changes up to 2% during periods of spring thaw and rainfall. These variations were interpreted in the context of environmental factors such as freeze-thaw cycles, tailings pond level fluctuations and precipitation. Despite the presence of active construction noise, stable ambient noise cross-correlations were obtained using advanced denoising techniques, allowing reliable daily measurements using as little as one hour of data per day. Depth sensitivity analysis suggested that the observed dv/v changes were most pronounced at depths between 7 and 14m, corresponding to the transition zone between the dam fill and underlying foundation. This research is among the first to integrate DAS and CWI for tailings dam monitoring, highlighting the potential of DAS to dramatically increase spatial coverage, offering thousands of sensors along a fibre optic cable. Such monitoring could inform early warning systems and support risk mitigation efforts by detecting precursory changes that conventional point sensors may miss.


Revealing hidden failure In collaboration with the British Geological Survey and


Luna OptaSense, Ouellet’s doctoral research (Ouellet et al. 2024) also showcased the power of DAS to reveal previously undetectable landslide processes at the Hollin Hill Landslide Observatory in the UK. By repurposing a buried 925m fibre optic cable into a dense network of DAS strain sensors, the team achieved nano-strain sensitivity and sub-minute temporal resolution, capturing near-surface deformations with less than 1mm of displacement during a three-day rainfall event.


Five key landslide processes were identified: the initiation of strain at the scarp, triggering of a rupture zone, retrogression toward the slope crest, a flow-lobe surge at the toe, and subsequent stabilisation. These observations were derived from strain-rate spatiotemporal images that resolved dynamic slope behavior at the meter scale, extending beyond the capability of conventional geotechnical or remote sensing methods. The study further validated DAS-derived displacement estimates by comparing them to collocated inclinometer data, with results in strong agreement. These results illustrate how distributed fibre optic sensing could help reveal new insights into tailings dam behaviour, tracking both slow deformations and sudden changes with high fidelity.


Making DAS practical DAS adoption is not yet widespread for geotechnical


monitoring in the mining industry, which may be due to the perceived complexity and high initial investment. That’s where Lumidas comes in. Lumidas is developing purpose-built data processing pipelines to translate DAS data into intuitive dam performance indicators. These include strain anomalies, seismic velocity changes, and other early warning signals. Outputs are visualised through dynamic heat maps and spatiotemporal overlays on a web-based platform. The system is currently under development in a two- year pilot project in collaboration with multiple industry and academic stakeholders, including Teck Resources Limited, BGC Engineering Inc., Norwegian Geotechnical Institute, University of Calgary, Nerve-Sensors and FEBUS Optics. This initiative is focused on creating a scalable, cloud-based monitoring framework adaptable to dams of all sizes and geographies. The Lumidas monitoring system is designed for tailings dam operators, mining companies, engineers, regulators, and researchers. It provides a shared platform for managing risk, improving operational decisions, and increasing transparency with stakeholders. Whether deployed in remote northern Canada or in densely populated mining regions, the system is designed to scale. Future developments will include expanding the range of fibre optic deployments to different dam geometries and automating anomaly detection with AI, alongside future pilots designed to further validate and benchmark the platform under a variety of field conditions.


References


Dai, S., Wuttke, F., & Santamarina, J. C. (2013). Coda wave analysis to monitor processes in soils. Journal of Geotechnical and Geoenvironmental Engineering, 139(9), 1504–1511. https:// doi.org/10.1061/(ASCE)GT.1943- 5606.0000872


Grêt, A., Snieder, R., & Özbay, U. (2006). Monitoring in situ stress changes in a mining environment with coda wave interferometry. Geophysical Journal International, 167(2), 504–508. https://doi.org/10.1111/j.1365- 246X.2006.03097.x


Hadziioannou, C., Larose, E., Coutant, O., Roux, P., & Campillo, M. (2009). Stability of monitoring weak changes in multiply scattering media with ambient noise correlation: Laboratory experiments. The Journal of the Acoustical Society of America, 125(6), 3688–3695. https://doi. org/10.1121/1.3125345


Hudson-Edwards, K., Kemp, D., Torres-Cruz, L.A., Macklin, M.G., Brewer, P.A., Owen, J.R., Franks, D. M., Marquis, E., & Thomas, C.J. (2024). Tailings storage facilities, failures and disaster risk. Nat Rev Earth Environ 5, 612–630 (2024). https://doi. org/10.1038/s43017-024-00576-4


Ouellet, S., Dettmer, J., Mikesell, T. D., Lato, M., & Karrenbach, M. (2025). Tailings dam performance monitoring by combining coda wave interferometry with distributed acoustic sensing. ASCE Journal of Geotechnical and Geoenvironmental Engineering. https://doi.org/10.1061/ JGGEFK.GTENG-13066


Ouellet, S., Dettmer, J., Lato, M. J., Cole, S., Hutchinson, D. J., Karrenbach, M., Dashwood, B., Chambers, J. E., & Crickmore, R. (2024). Previously hidden landslide processes revealed using distributed acoustic sensing with nanostrain-rate sensitivity. Nature Communications, 15, 6239. https://doi. org/10.1038/s41467-024-50604-6


Ouellet, S.M., Dettmer, J., Olivier, G., DeWit, T. & Lato, M. Advanced monitoring of tailings dam performance using seismic noise and stress models. Commun Earth Environ 3, 301 (2022). https://doi. org/10.1038/s43247-022-00629-w


Snieder, R. (2002). Coda wave interferometry and the equilibration of energy in elastic media. Physical Review E, 66(4), 046615. https://doi.org/10.1103/ PhysRevE.66.046615


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