Rockfall forecasting at a British Columbia mine using meteorological and thermal imaging data
Bibliographic record
Abstract
It is generally accepted within the mining geotechnical community that rockfall initiation is influenced by meteorological conditions, such as rainfall and freeze-thaw cycles. Rockfall typically occurs rapidly and without warning, making it difficult to predict the timing and location of individual events that would pose a risk to mine workers. However, quantifying the links between weather patterns and rockfall mechanisms provides an opportunity to forecast periods of increased risk based on prevailing meteorological conditions. Previous research from the University of Arizona’s Geotechnical Center of Excellence (GCE) analysed rockfall occurrences at a mine in southern Arizona, where strong solar irradiance, monsoonal rainfall, and diurnal temperature variations are likely drivers of rockfall activity. This study builds on that work by exploring the relationship between verified rockfall events and freeze-thaw processes at a steelmaking coal mine in British Columbia. Although data were collected between 27 January–13 April 2022, the analysis focused on a narrower window (24 March–11 April) selected for its pronounced freeze-thaw cycling. Rockfall events were identified from thermal imaging, using a computer vision algorithm and verified by a trained practitioner. Statistical and machine learning models, including logistic regression and tree-based classifiers, were applied to identify key meteorological variables associated with rockfall occurrence and to develop preliminary predictive models that forecast periods of elevated rockfall risk at this location. By comparing results from sites in contrasting climates, this study advances our understanding of how varying environmental conditions influence rockfall in open pit mines, and supports the development of predictive models that enable a more refined and higher-confidence approach to rockfall risk management and mitigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".