Calibration of a seismic hazard assessment tool using velocity fields and geotechnical data
Bibliographic record
Abstract
The majority of seismic hazard assessment tools are solely based on statistical analyses of several seismic source parameters such as event rate and time, and seismic moment. These analyses are often applied to the entire mining area which can impact the accuracy and reliability of the hazard assessment tool in each zone. Experience has shown that mining geomechanical risk is complex and its mitigation needs a broad understanding of other geotechnical factors such as rock mass properties, geological structures, mining method, stress regime, etc. Since all the contributing parameters and their impact are not entirely understood, it is critical to apply a range of geotechnical/geomechanical analyses in correlation to each other and quantify the changes in the rock mass behaviour. The goal of this paper is to develop a seismic hazard assessment tool calibrated for each geotechnical domain within the mine. To develop the tool, we incorporated mine geotechnical and geological data, seismic source parameters, and tomography analyses from a hard rock underground mine in North America. There exist several sub-vertical faults and one horizontal structure in the mine which create clear contrasts in rock mass behaviour across the structure. The results show good correlation among the different datasets, and a calibrated seismic hazard tool has been developed that provides ongoing updates to the mine operation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".