Data driven frequency ratio modeling for iron-ore exploration using aster and aeromagnetic datasets in parts of Nasarawa, Northcentral Nigeria
Bibliographic record
Abstract
This study used an integrated approach combining aeromagnetic, geological and remote sensing methods to analyze subsurface structures associated with Iron Ore mineralization in part of Akwanga, Nasarawa Northcentral Nigeria. The frequency ratio model (FR) was used to assign weights to different layers of evidence and develop a conceptual model of mineralization potential. Magnetic data enhancement techniques, including reduction to equator (RTE) and upward continuation (UC), were applied using Oasis Montaj™ software. Subsurface geological structures were revealed, and Euler Deconvolution estimated depths to magnetic sources. Band ratio analysis using ASTER Bands 2/1 (ferric oxide), 5/3+1/2 (ferrous oxide) and 7/5 (clay mineral) was employed to determine the hydrothermal alteration of Iron Ore. Principal Component Analysis (PCA) was applied to ASTER bands 1, 3, 5, 8 for propylitic alteration, Bands 1, 3, 4, 6 for Argillic alteration, and Bands 1, 2, 3, 4 for iron oxide alteration. A predictive model for mineralization potential was developed using a data-driven approach, considering critical factors such as lithology, hydrothermal alteration, lineament density, magnetic anomalies, and slope, and implemented using ArcMap 10.8. The model was trained on 70% of the Iron Ore exposure data and tested on the remaining 30%. Validation was performed using the area under curve (AUC) method, achieving an accuracy of 72%. The Iron Ore potential map generated from the model demarcated the study area into five potential zones: very low, low, moderate, high, and very high potential zones. The study successfully identified areas with high potential for Iron Ore mineralization, primarily structurally controlled. The developed model serves as a valuable reference and guide for future exploration and planning activities, and recommendations are made for further refinement and improvement.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".