Shortwave infrared (1.0–2.5 µm) hyperspectral imaging of the Athabasca West Grand Rapids Formation oil sands
Bibliographic record
Abstract
ABSTRACT Previous work on the reflectance spectroscopy of oil sands has focused exclusively on the McMurray Formation of the Athabasca deposit. However, as industrial development expands into other formations, spectral research on Alberta’s other bitumen reservoirs is warranted. This study presents the first investigation of shortwave infrared (SWIR; 1.0–2.5 μm) hyperspectral imaging of Grand Rapids Formation oil sands. Comparing the spectral properties of Grand Rapids oil sands to that of the McMurray Formation reveals some key differences. Grand Rapids oil sands have lower overall reflectance and deeper 1.4- and 1.9-μm absorption features than McMurray oil sands of the same ore grade. At ore grades greater than 10 wt. %, Grand Rapids oil sands also have shallower bitumen absorption features. These spectral differences may be attributed to the mineralogical differences between the two formations, namely, structural water in K-feldspar. Hyperspectral analysis techniques that have been previously developed for McMurray oil sands were tested on a Grand Rapids Formation core. Using contrast stretching and the three-band combination of R = 2.05 µm, G = 2.13 µm, and B = 2.28 µm (where R, G, and B refer to red, green, and blue channels), the visibility of sedimentological features was enhanced in SWIR imagery. With a simple recalibration, an established spectral model for the estimation of total bitumen content (TBC) produced results in close agreement with Dean–Stark analysis data (coefficient of determination [R2] = 0.92, ±1.1 wt. %) and of similar accuracy to what has been previously demonstrated for the McMurray Formation. The results of this study suggest that with minor adjustments, oil sands spectral analysis techniques are transferable between different geological formations.
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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 | 0.000 | 0.000 |
| 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".