Control Measures to Manage Seismic Risk at the LaRonde Mine, a Deep and Seismically Active Operation
Bibliographic record
Abstract
ABSTRACT: The LaRonde mine is a world-class deposit of gold-copper and zinc-silver along the Cadillac Fault in the Abitibi-Témiscamingue region of Quebec, Canada. Since its inauguration in 1988, the LaRonde mine has produced over 7 M ounces of gold. Mining operations currently extend 3.2 km below the surface with plans to reach 3.4 km. The LaRonde mine has been dealing with induced seismicity daily since 2003. The seismic activity can exceed MRichter 2, and the pro-active management of seismic risk is a key element to operate this mine at that depth and under these seismically active conditions. Seismic risk at the LaRonde mine is currently managed through a combination of control measures, including a ground-control-driven mining sequence and level design, dynamic ground support systems, and procedures to limit workforce exposure. These control measures have evolved over time and been adapted to the seismic risk as the operation becomes deeper and larger. The main objective is to maintain a safe work environment while meeting production requirements. Seismic risk management is an ongoing concern at the LaRonde mine. This paper presents the current state of the strategic and tactical control measures implemented on site to manage seismic risk at the LaRonde mine, and it documents some of the impacts on operational flexibility and performance. The effectiveness of control measures must be quantified to measure improvements year after year and to identify and correct observed deficiencies. 1. INTRODUCTION Mining at great depth generally involves challenging geotechnical conditions and management of seismicity. Innovation and continuous improvement of mining practices are required to face evolving challenges and maintain a safe work environment. An example of this process at the LaRonde mine is the development of innovative ground support solutions to manage the challenging ground conditions in a strongly foliated and highly deformable rock mass. Seismicity was recorded in the early stages of mining from the Penna shaft, with the first damaging seismic events observed in 2002 as described in Mercier-Langevin and Hudyma (2007). Seismic risk management has been an integral part of mine planning since that 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".