Numerical model calibration of fault properties using seismic moment for a deep underground mine
Bibliographic record
Abstract
Microseismicity often provides crucial insights into the behaviour of rock masses in deep mining environments, especially concerning damage and the mechanisms affected by stress field changes. By integrating numerical simulations of synthetic microseismicity with field data analysis, a comprehensive understanding of damage initiation, progression, and the interactions among discontinuities can be attained. This holistic approach not only advances our comprehension of rock mass behaviour in deep mining operations but also enables more precise predictions and proactive management strategies to mitigate risks. This study delves into the methodology employed for calibrating numerical models, focusing on the geological structures within a deep mine in Canada. The occurrence of significant seismic events in this deep mine is directly linked to fault slip. With mining operations delving deeper, understanding the stress-induced effects of mining and fault movements becomes paramount for ensuring safe ore extraction. Merely incorporating lithological considerations into the numerical model proved to be insufficient to replicate the full behaviour of the rock mass response to seismic activity. Hence, fault structures were integrated into the model. However, due to the volumetric nature of faults with a thickness exceeding 1.5 m, explicit integration was deemed inadequate for accurately representing their behaviour. To address this challenge, a methodology termed the ‘weak zone’ approach was developed. With this approach, faults are characterised as relatively weaker materials compared to the host rock, and the cumulative plastic shear strain is utilised for calculating seismic moment. Historically recorded seismicity served as a crucial calibration tool for determining the mechanical properties of faults within the model. These properties were then appropriately scaled to ensure that the modelled results provided a reasonable estimation of fault behaviours in relation to seismic moment. This comprehensive approach not only enhances our understanding of fault dynamics in deep mining environments but also aids in optimising safety measures for ore extraction.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".