Structural models a weak link in managing the risk from large seismic events
Bibliographic record
Abstract
In caving and stoping mines, very large seismic events (i.e. moment magnitude 3.0+ events) are an important risk in terms of safety and production loss. A significant portion of these very large events are fault-slip type events. In spite of this, many seismically active mines do not have a good understanding of their structural geology from a seismic standpoint. Structural models are typically developed in the early project stages and focused on structures controlling orebody formation and mineralisation, rather than being created with a focus on the potential for generating seismicity. Commonly, the structural models available to the geotechnical team are out of date and can include hundreds of structures which do not influence seismicity, making the interpretation of fault-slip seismicity extremely challenging. Structural geology resources at mine sites are almost entirely focused on exploration and orebody delineation. Geotechnical departments rarely have access to structural geologists before seismicity becomes a major problem. When they do, these personnel often dedicate only a fraction of their time to update the structural model and they lack training or experience in mine seismicity. This paper details the importance of well-constructed, targeted structural models when managing seismicity and analysis methods that can be used to better understand the character and risks of fault-slip seismicity.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".