Design and management processes involved with extracting regional pillar stopes in a seismic setting at Darlot Gold Mine
Bibliographic record
Abstract
This paper outlines the protocols introduced for the management of potential seismic risk, including monitoring, numerical modelling, extraction sequencing and the necessary support upgrades to mitigate the risks at Darlot Gold Mine. The Walters Lode is located 400 m below surface and uses a longhole mining method retreating back to a regional pillar. The lode is hosted within a strong dolerite host rock (uniaxial compressive strength (UCS) = 220 MPa+), with a UCS that often exceeds 200 MPa, with the main mineralised lode following along a structure called the first slip fault. This structure pinches and swells at varying depths and is usually identified by quartz intrusions and a 0.5-1.0 m sheared zoned along the plane. This fault dips at approximately 50° to the west. In addition to the first slip fault, there are multiple intersecting faults throughout the area. Originally the ground support for the development drives was not designed for seismic conditions and predominantly utilised resin bolts installed over fibrecrete. As the mining front began to retreat towards the pillar, mining induced stresses began acting upon the rock mass and the structures in the area. The rock mass response to these changes included an increase in seismic activity and deformation. This increased activity led to a review of the seismic management risk procedures at Darlot. Initial changes included upgrading the ground support regime to a yielding system capable of retaining the rock mass in response to predicted levels of seismicity. Expected levels of seismicity were obtained by undertaking a review of historical seismic data in the area. Non-linear elastic modelling was also undertaken to determine the potential seismic response and predicted damage to the rock mass based on the proposed extraction sequence. Based on these results, a geotechnically optimised stoping sequence was proposed. Internal numerical modelling has been conducted to account for newly found stope shapes relative to the original extraction sequence. The results that show potential depths of failure are used to strategically plan and account for additional overbreak or brow damage in the mining plan. Ongoing seismic monitoring and analysis is used to determine seismic exclusion zones and re-entry times. All of the above changes are used to continually calibrate the existing models, and ongoing data collection has resulted in an optimal extraction sequence, therefore allowing a safe and productive extraction of the Walters Lode.
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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".