Managing Seismic Risk Associated to Development Blasting Using Random Forests Predictive Models Based on Geologic and Structural Rockmass Properties
Bibliographic record
Abstract
Abstract As mining activities are expected to develop at greater depths, seismic responses to the blasting of development drift segments are expected to increase and present a greater hazard. A database of 379 development blasts was created for a mining site, recording seismic responses related to these blasts and rock mass structural and geologic properties associated with the drift segment. A random forest, multivariate statistical predictive model was developed with 75% of the drift segments. The model's performance was validated by analyzing 100 drift segments that were not used to create the model. The improved understanding of the variation in the intensity of seismic responses to development blasting through the sum of the seismic moment of the events is a clear benefit of random forest model development for the case study. In addition, the development of the predictive random forest model provides a tool for decision-makers to select performance criteria thresholds that they deem acceptable. The threshold selected would depend on the risk appetite of the decision-makers. The proposed approach provides quantitative data on the distribution of seismic hazards associated with development blasting which managers can rely on. Combining the proposed approach with current seismic protocols used at different mine sites could improve our management of seismic risk associated with development blasting. Using the predictive model for the sector and period studied has shown a potential to increase the accuracy, sensitivity, and precision for anticipating a high-intensity seismic response to a development blast.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".