Shortwave Infrared Hyperspectral Imaging: A Novel Method For Enhancing the Visibility of Sedimentary And Biogenic Features In Oil-Saturated Core
Bibliographic record
Abstract
Abstract: A common challenge of logging core from heavy-oil reservoirs is that sedimentary and biogenic features are difficult to see in fine-grained, well-sorted, and oil-saturated strata. In this study, hyperspectral imaging is shown to be an effective method for enhancing the visibility of sedimentary fabric and trace fossils in oil-saturated core. Shortwave infrared (SWIR) hyperspectral imagery of Lower Cretaceous McMurray Formation oil-sands core from northeastern Alberta, Canada, was investigated at various wavelengths and resolutions. Color composite imagery consisting of wavelength bands at 2162, 2199, and 2349 nm in the red, green, and blue channels, respectively, dramatically enhanced the clarity of sedimentary features in structureless-appearing oil sand. In many cases, spectral imagery revealed features that are completely invisible to the unaided eye. In coarser-grained sections (fine to coarse sand), the enhanced contrast of sedimentological features is attributed predominantly to variability in grain size and bitumen saturation. In finer-grained sections (very fine to fine sand), enhanced contrast is mainly ascribed to variability in relative abundance of clays. A spatial resolution of at least 0.25 mm/pixel is required for imaging trace fossils, while lower resolutions (1.2–1.5 mm/pixel) are sufficient for enhancing the visibility of most sedimentary structures.
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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 | 0.001 | 0.000 |
| 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".