Remote Sensing‐based Machine Learning Techniques for Mapping Gold‐Mineralized Alteration Zones in the Fatira Mine Area, Egypt
Bibliographic record
Abstract
Abstract In the Fatira (Abu Zawal) mine area, located in the northern Eastern Desert of Egypt, fieldwork and mineralogical analysis, integrated with machine learning techniques applied to Landsat‐8 OLI, ASTER, and Sentinel‐2 multi‐spectral imagery (MSI) data delineate gold‐sulfide mineralization in altered rocks. Gold (Au) anomalies in hydrothermal breccias and quartz veins are associated with NE‐oriented felsite dykes and silicified granitic rocks. Two main alteration types are identified: a pyrite‐sericite‐quartz and a sulfide‐chlorite‐carbonate assemblage, locally with dispersed free‐milling Au specks. Dimensionality reduction techniques, including principal component analysis (PCA) and independent component analysis (ICA), enabled mapping of alteration types. Sentinel‐2 PC125 composite images offered efficient lithological differentiation, while supervised classifications, i.e., the support vector machine (SVM) of Landsat‐8 yielded an accuracy of 88.55% and a Kappa value of 0.86. ASTER mineral indices contributed to map hydrothermal alteration mineral phases, including sericite, muscovite, kaolinite, and iron oxides. Results indicate that post‐magmatic epigenetic hydrothermal activity significantly contributed to the Au‐sulfide mineralization in the Fatira area, distinguishing it from the more prevalent orogenic gold deposits in the region.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".