Geotechnical monitoring at the speed of light: New insights from distributed acoustic sensing
Bibliographic record
Abstract
Recent advances in distributed fibre optic sensing enable new opportunities in geotechnical monitoring and characterization. Distributed acoustic sensing (DAS) is a distributed fibre optic sensing technology relying on Rayleigh backscattering of light to detect and locate disturbances in near real-time along tens of kilometres of fibre optic cable. The dynamic strain sensing capabilities of DAS have prompted numerous research initiatives from the seismology community over the past decade. Although research on DAS for seismic applications is well established, studies on DAS for geotechnical monitoring applications are less common. Here, we present a summary of two recent case studies involving DAS for geotechnical monitoring. The first study considers a slow-moving landslide where DAS data were acquired over a three-day period of rainfall. The DAS aseismic strain and strain-rate data support the interpretation of the triggering and retrogression failure mechanism of the landslide. The second study considers an active mine site in northern Canada. Data were acquired from a cable installed ~1m below a tailings dam crest. Passive seismic interferometry was applied to DAS data to infer changes in seismic velocities in the uppermost several meters of the subsurface. These findings represent a first step towards advancing continuous monitoring techniques with fiber optic sensing technologies. However, further research is needed to improve our understanding of DAS performance for geotechnical monitoring applications over longer-term periods.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".