A digital face mapping case study in an underground hard rock mine
Bibliographic record
Abstract
This paper presents a case study of a digital discontinuity mapping system used as a rock mass characterization tool in an underground hard rock mine. This mapping system allows for a fast acquisition of information that can best characterize the geological structural regime without exposing workers to potentially unsafe conditions. This method can be used to overcome some of the shortcomings of traditional mapping methods, such as limited access to rock exposures. Photographic images of the exposed rock mass are introduced into a software package that has been developed to extract potential discontinuity traces using detection algorithms. Detected features that do not describe discontinuity traces are removed from the images using artificial neural networks. Operator intervention can improve the reliability of the system by linking incomplete discontinuity segments. This developed process results in the construction of a discontinuity trace map that can be used for rock mass characterization purposes. The system was employed to construct discontinuity trace maps of twenty 1.8 m by 1.8 m mapping windows from two locations in an underground hard rock mine. The ability of the system to quantify the geomechanical characteristics of the rock mass was evaluated by comparing the results with those of manually drawn discontinuity trace maps. The results of this study have helped to evaluate the digital face mapping system and identify its limitations.Key words: rock mass characterization, image processing, discontinuity networks, neural networks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".