Sequential Indicator Simulation with Locally Varying Anisotropy – Simulating Mineralized Units in a Porphyry Copper Deposit
Bibliographic record
Abstract
The definition of mineralized units is key to the predictive capacity of a resource model, since these units define homogeneous volumes where spatial estimation or simulation of the relevant grades can be performed. In this paper, we adapt sequential indicator simulation to model mineralization units in a large porphyry copper deposit, accounting for the weathering profile that defines the vertical zoning of these units. A locally varying anisotropy field is created from the geological interpretation of the contact between the mixed mineralized unit, where the rock mineralization transitions from supergene to hypogene sulphides. A sequential indicator simulation routine is modified to account for the local variations of the units, and all distances are computed through these folded surfaces. Sensitivities related to the main parameters of the simulation algorithm that accounts for the locally varying anisotropy are performed to select the optimum parameters. The final result is compared with conventional sequential indicator simulation, against the geological units logged in blast holes, at a much denser grid, showing an increase in the accuracy in predicting the mineralized unit from the drillhole logged data.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".