Condition monitoring |
New paradigms for tailings dam monitoring
Dr Susanne Ouellet from the University of Calgary has been researching how integrating strain and seismic sensing with fibre optics can help monitor the performance of tailings dams
Right – Figure 1. Drone aerial image of the tailings facility where a fibre optic cable was installed. The approximate layout of the cable, buried one metre below the crest, is shown as a red dashed line
Above – Figure 2. Installation of geophones near the installed fibre optic cable to evaluate CWI at the tailings facility in Saskatchewan
Below – Figure 3. Example DAS spectrogram from a single location along the fibre optic cable showing 24-hours of noise characteristics at an active tailings facility in northern Canada
TAILINGS DAMS ARE AMONG the largest engineered structures on the planet, yet they are often monitored using discrete, point-based sensors. This approach can leave uncertainties on dam performance where no sensors are installed. As ore grades decline and demand for critical minerals grows, mines are producing ever-increasing volumes of tailings. In turn, this increased production in tailings is driving an increasing number of tailings dam failures (Hudson-Edwards et al. 2024). With each failure, the mining industry faces increasing pressure to maintain its social license to operate globally. Recent advances in distributed fibre optic sensing have made it possible to transform standard telecommunications-grade fibre optic cables into thousands of strain and seismic sensors extending over kilometres. Fibre optic sensing works by sending pulses of light along an optical fibre. As these pulses travel, natural imperfections in the glass fibre scatter a portion of the light back toward the recording device, known as an interrogator. Distributed acoustic sensing (DAS) is one form of fibre optic sensing that uses the phenomenon known as Rayleigh backscattering. By analysing the backscattered light pulses, DAS enables continuous, high-resolution monitoring of physical changes in the surrounding environment. Lumidas, a technology startup founded on Susanne Ouellet’s doctoral research at the University of Calgary, is harnessing the power of DAS to monitor the performance of tailings dams. Her work, supported by collaborations
with multidisciplinary leaders in seismology, fibre optic sensing, and geotechnical engineering, is informed by over a decade of experience in both research and consulting. Ouellet was driven to explore DAS technology in greater depth following the catastrophic 2019 Brumadinho tailings dam failure in Brazil, which claimed over 270 lives. Although the dam was equipped with an array of monitoring systems, none detected signs of instability prior to the collapse.
At the time, Ouellet was working as a geotechnical engineer at BGC Engineering, evaluating technologies to support an emergency response system at a tailings storage facility in northern Canada. The Brumadinho disaster served as a catalyst for her decision to investigate the potential of DAS to fill critical monitoring gaps in the industry.
Seismic Coda waves Ouellet’s four-year PhD research programme, in
partnership with a mine operator, BGC Engineering Inc., LUNA OptaSense, and Mitacs, tested DAS technology in real-world conditions at an upstream tailings facility in Saskatchewan. Passive seismic techniques like coda wave
interferometry (CWI), initially performed at the site using geophones (Ouellet et al. 2022), were shown to work equally well with fibre optics (Ouellet et al. 2025). CWI helps geotechnical engineers to monitor tiny changes in subsurface properties over time. It works by analysing the “coda” portion of seismic signals. These are the later-arriving waveforms produced by multiple scattering events as waves travel through a heterogeneous medium. These scattered waves are highly sensitive to small changes in the material, such as shifts in mean effective stress or variations in moisture content (Snieder, 2002; Grêt et al., 2006). A key advantage of CWI is that it can leverage the ambient seismic wavefield, generated by natural or anthropogenic sources such as machinery, traffic, or wind, as its energy source. By cross-correlating seismic recordings between pairs of sensors, an approximation of the response of the medium can be extracted. When subsurface conditions change, these alterations appear as small shifts in the travel times of coda waves, which can
34 | May 2025 |
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