Geospatial Interpretation of Onshore Hydrocarbon Micro–Seepage Induced Alterations in Soils and Sediments by Spectral Enhancement Techniques
Bibliographic record
Abstract
Hydrocarbon micro – seepages are light hydrocarbon that cause oxidation – reduction reaction on the earth’s surface, resulting in alterations and anomalies such as red bed bleaching, ferrous iron enrichment and increase in the concentration of clay minerals and carbonate in overlying soils and sediments. Remote sensing has become a valuable tool in hydrocarbon micro – seepage studies and have been successfully used to interpret surface alterations and anomalies of minerals. In this study, Landsat 7 ETM+ remotely sensed data was utilized for interpreting the onshore hydrocarbon micro – seepage induced alterations zone in Ugwueme. Spectral enhancements techniques such as the principal component analysis (PCA), band ratio (BR) and false color composite (FCC) were adopted for delineating alteration zones. With Landsat 7 ETM+ band selection, and for PCA, the 1457PC3, 1345PC2 and 3457PC4 are the most suitable PC image for spectral enhancement of ferric iron, ferrous iron and clay minerals. Band ratio index such as (3/1), (7/5) and (2+5)/(3+4) also yields better enhancement for anomalous micro – seepage. The study shows that PCA, BR, FCC are good spectral enhancement techniques for interpreting hydrocarbon micro – seepage alterations in overlying soils and sediments.
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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 | 0.002 | 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".