Predicting Rock Hardness and Abrasivity Using Hyperspectral Imaging Data and Random Forest Regressor Model
Bibliographic record
Abstract
This study aimed to develop predictive models for rock hardness and abrasivity based on hyperspectral imaging data, providing valuable information without interrupting the mining processes. The data collection stage first involved scanning 159 rock samples collected from 6 different blasted rock piles using visible and near-infrared (VNIR) and short-wave infrared (SWIR) sensors. The hardness and abrasivity of the samples were then determined through Leeb rebound hardness (LRH) and Cerchar abrasivity index (CAI) tests, respectively. The data preprocessing involved radiometric correction, background removal, and staking VNIR and SWIR images. An integrated approach based on K-means clustering and the band ratio concept was employed for feature extraction, resulting in 28 band-ratio-based features. Afterward, the random forest regressor (RFR) algorithm was employed to develop predictive models for rock hardness and abrasivity separately. The performance assessment showed that the developed models can estimate rock hardness and abrasivity of unseen data with R2 scores of 0.74 and 0.79, respectively, with the most influential features located mainly within the SWIR region. The results indicate that integrated hyperspectral data and RFR technique have strong potential for practical and efficient rock hardness and abrasivity characterization during mining processes.
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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 | 0.001 |
| 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".