Tools for validating and creating reliable fault models
Bibliographic record
Abstract
Reliable 3D fault models are critically important for open pit slope design and slope stability assessment. Fault-influenced slope failures are major geotechnical hazards which frequently result in production slowdowns and require mitigation through design changes or depressurisation. Despite its importance in assessing slope stability, 3D fault model development is often left to the mine geologists or external geological consultants, whose focus is commonly the distribution of ore, not geotechnical hazard. This study presents a data-driven, semi-automated method for validating, identifying, and mapping faults that can influence slope stability. The tools are developed using the Python programming language’s freely available scientific computing libraries. These scalable libraries are used to leverage and visualise massive datasets, like blasthole drilling data or rock quality designation (RQD) to assess and improve fault models. Two case studies are provided to illustrate the application of the tools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".