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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".