Modeling of mineralization using minimum/maximum autocorrelation factor: case study Sury Gunay gold deposit NW of Iran
Bibliographic record
Abstract
The importance of modeling geological units and mineralization factor, as a key step in evaluating mineral deposits, is undeniable. Multivariate geostatistical methods are very useful tools in the case of geochemical modeling. In this study, the minimum/maximum autocorrelation factor (MAF), a multivariate geostatistical method, and the sequential indicator simulation (SIS) were used in order to model the Sury Gunay epithermal gold deposit, NW of Iran. The analyses were done using core samples obtained from drill holes. The MAF is a geospatially modified version of principal component analysis, which decorrelates factors for all lag distances. By applying the MAF on alr-transformed data, four MAF were obtained and the fourth factor represents the mineralization factor. Joint simulation of the mineralization elements was carried out by applying the sequential Gaussian simulation to the fourth factor scores. The main rock types and alterations of the deposit were also modeled using SIS and the probabilistic models of the rock types, and alterations were obtained using E-type maps resulting from 20 realizations. The results indicate that there are better relations with higher values of the mineralization factor and the existence of the volcanogenic breccia and dacite porphyry rock types. Furthermore, the simulated alterations demonstrate that the higher probability of silicification existence may have better correlation with higher concentrations of the mineralization elements.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".