Recent Advances in Fiber Optic Sensing for Geotechnical Monitoring: Practical Considerations for the Mining Industry
Bibliographic record
Abstract
ABSTRACT: The safety of tailings dams and auxiliary mining infrastructure requires monitoring solutions capable of detecting relevant geotechnical performance indicators. Recent advances in the fibre optic sensing technology of distributed acoustic sensing (DAS) enable continuous dynamic strain measurements over long distances (i.e., tens of kilometers) with meter-scale spatial resolution. This presentation provides practical perspectives on considerations for DAS within the mining industry, illustrated with two recent studies. In the first study, four months of DAS observations at a northern Canadian tailings facility were used to infer changes of shear-wave velocity inside the dam, relying on energy from the ambient seismic wave field. As shearwave velocities can be used as a proxy for soil stiffness, this method helps understand changes in tailings dam performance. The second study involved three-day DAS recordings at a slow-moving landslide observatory in the United Kingdom. Here, lowfrequency (<1 Hz) DAS measurements revealed near-surface slope failure processes with unprecedented nanostrain-rate sensitivity and spatiotemporal resolution. The results capture rupture zone initiation, retrogression, and flow-lobe activity. While these results demonstrate how geotechnical parameters can be derived from DAS data, key challenges remain. These include limited knowledge about instrument response and cable coupling, environmental effects and the management of large data volumes. This presentation will explore practical strategies to overcome these challenges, needed to support broader adoption of DAS technology in the mining sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".