Proactive Identification of Adverse Geological Structure in a Deep Mine Environment
Bibliographic record
Abstract
ABSTRACT: This paper details proactive measures taken to identify adverse geological structures present in the rock mass before mining occurred in the area. During the sinking of Glencore's Onaping Depth winze and subsequent excavation of the shaft stations, a major fault had to be crossed. This structure exhibited erratic behavior ranging from local wedge formation in tunnels to major rock bursting when it had been encountered historically at shallower depths. Through the use of scout holes, diamond drill holes drilled ahead of the mining face, the ground conditions around the fault ahead of the shaft and drift development were characterized. The holes provided a wealth of data for ground condition assessment, from physical examination of the core to in-situ evaluation of the rock mass using acoustic and optical televiewer surveys. Examination of the scout holes identified up to a 50 m zone of anomalous ground conditions below the fault. Based on the proactive identification of the anomalous zone, the ground support was enhanced, and the strategy for mining through the area was modified. Whenever feasible, permanent excavations were relocated further away from the fault zone. For the drives that had to go through the fault, conservative ground control measures were employed. This resulted in a significant risk reduction and improved operator safety associated with the construction of the critical excavations. Seismic data were collected while mining through this area, and the data confirmed the presence of anomalous ground conditions. 1. INTRODUCTION The presence of large geological structures can be a significant factor leading to increased frequency of rockbursts and large seismic events in deep underground mines. Predicting the rock mass behavior associated with geological structures is challenging because the structures are not always easily identified until after an excavation has been made. Many geological structures are benign and cause few ground control problems. On rare occasions, a structure may have locked-in stress, which could potentially cause severe rock mass failures. Seismic monitoring is extensively used to provide real-time feedback on the local rock mass response to mining. To some extent, the data can be used to evaluate geotechnical risks. However, it is important to note that this dataset shows a response to mining activity and is not available until the excavation process begins. Reactionary mitigation efforts are employed after a rockburst or a significant seismic event has occurred, in contrast to proactive efforts aimed at preventing the event or reducing its potential damage severity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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, 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".