Optimizing Infill Drill Hole Decisions While Capturing the Spatial Continuity of Geochemical and Geometallurgical Properties: Application to Gol Gohar Iron Ore Mine, Iran
Bibliographic record
Abstract
This paper addresses the problem of infill drill hole placement for mineral resource estimation and classification. The placement is considered optimal when it maximizes an objective function that accounts for ore grades, mineral resource classes, extraction priorities, and block volumes, where the grade and resource classes are defined on the basis of a set of geostatistical simulations. To expedite the identification of the optimal solution within a condensed timeframe, modifications to the random search (RS) algorithm are introduced, including a partition of the region targeted for drilling and the definition of a maximum distance to existing drill holes. The modified RS divides the study area into smaller areas and examines all these areas to find the optimal solution, in order to reduce the search time and to reach the best possible solution. This approach, furthermore, eliminates the impact of different random starting points and the risk of getting trapped in certain areas of the solution space. Also, the incorporation of a geometallurgical parameter (recovered metal) instead of the ore grade represents an innovation that signifies the consideration of mineral processing perspectives to optimize the drill hole placement. The proposed modified RS algorithm is applied to a dataset from an Iranian iron deposit consisting of 240 exploration drill holes, and resulted in 11% to 21% of the indicated resources being converted into measured resources after locating nine infill drill holes accounting for the iron grade and the recovered metal, respectively. The modified RS also compares favorably to other traditional optimization techniques.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".