Tailings Dam Performance Monitoring by Combining Coda Wave Interferometry with Distributed Acoustic Sensing
Bibliographic record
Abstract
Advances in distributed fiber optic sensing technologies are enabling new methods to monitor changes in tailings dam performance. Distributed acoustic sensing (DAS), a distributed fiber optic sensing technology relying on Rayleigh light backscattering, can provide continuous spatial and temporal coverage along the length of a fiber optic cable extending tens of kilometers. In 2019, nearly six kilometers of fiber optic cable were installed at ∼1 m depth along an active upstream tailings dam in northern Canada. DAS seismic data were acquired at 400 Hz over a four-month period, from April to August 2021. We applied coda wave interferometry to a 120 m cable segment to obtain relative changes in seismic velocities (dv/v). Such coda waves are typically dominated by Rayleigh surface waves and dv/v can be used as a proxy for shear wave velocity changes. The dv/v estimates decrease by up to ∼1.9% over an initial two-month period of spring thaw and rainfall. Subsequently, dv/v recover by ∼1%, and generally show an inverse correlation with tailings pond levels up until the end of data acquisition. This correlation is supported by a known power-law relationship between shear wave velocity and effective stress. Rayleigh surface wave sensitivity kernels incorporating nearby seismic cone penetration testing data are used to estimate the approximate depths of dv/v sensitivity at ∼10 m. Despite active construction causing noise contamination, we obtain stable cross-correlation waveforms with as little as one hour of data per day. Overall, our results demonstrate how DAS can be used to augment geotechnical monitoring networks by providing in situ estimates of dv/v to inform changes in tailings dam performance over time.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".