Rock type classification of drill core using continuous wavelet analysis applied to thermal infrared reflectance spectra
Bibliographic record
Abstract
This study investigates a core logging methodology to map rock type using thermal infrared reflectance (TIR) spectra (500–4000 cm–1 or 2.5–20.0 μm) for 74 samples encompassing 11 rock types exposed in various mines of the Sudbury Basin, Canada. A continuous wavelet transform (CWT) was used to represent the original reflectance spectra as a suite of wavelets, each capturing spectral features of different scales with the low-scale components containing mineral spectral features and the high-scale components capturing the overall continuum. Classification was driven by the use of endmember spectra and the spectral angle mapper (SAM). Modelling and validation suites were developed and the mapping accuracy evaluated iteratively for random data splits. The results were compared for reflectance and wavelets of low components of power and significance. We found that the variability amongst measurements observed for varying orientation of a sample or due to variable surface roughness can be greatly minimized with the use of low-scale components, thus improving rock type classification. The average accuracy computed for the 11 rock types is highest for the low-scale component of power (72%) data as opposed to the reflectance data (55%). The highest average accuracy per rock type is obtained using the low-scale components (average value of 82%) for seven rock units that are relatively texturally homogeneous and of uniform modal mineralogy. Lower accuracy values are observed for rock units that display pronounced textural heterogeneity at the scale of observation, or variability in modal mineralogy, or are spectrally similar to other rock types.
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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 | 0.000 | 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".