Determination of mine-wide in-situ stress using numerical back analysis: a case study of Jwaneng mine
Bibliographic record
Abstract
In order to prepare for the transition from open pit to underground mining, Jwaneng Mine has undertaken several in-situ stress measurement campaigns in the past 12 years. Complex geological conditions at the mine site result in significantly scattered measurement data, making it challenging to interpret a coherent trend for the field stress tensor. To address this issue, this study presents a method to determine the mine-wide in-situ stress field using numerical back analysis incorporating geology, field stress measurement data, and mining history. Firstly, to consider the impact of excavation effects and the complex geology of the mining area, a 3DEC numerical model is established that includes major faults and rock mass zones and considers the open-pit excavation history. Thereafter, a back-analysis approach using the least squares method is proposed to find the optimal solution of the stress field. Based on this, the solution of the field stress tensor at Jwaneng Mine is obtained using overcoring stress measurement data obtained from 2013 to 2019. The reliability of the solution is further validated using the measurement data of deformation rate analysis and borehole ovality. This study offers reliable mine-scale in-situ stress conditions for Jwaneng Mine, which is critical for underground mine design. The proposed back-analysis method is useful for estimating mine-wide field stress under complex geological conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".