A hybrid machine learning model for improving regression of mineral composition estimation using well logging data
Bibliographic record
Abstract
Petrophysical methods are widely used to study physical and chemical properties of rocks and fluids. Due to the complexity of the lithological makeup of different rock types, quantitative analysis of mineral composition is a challenging task. In this study, we describe the use of a hybrid machine learning (ML) model to estimate mineral compositions by combining a convolutional neural network (CNN) and XGBoost algorithm. The selected inputs are preprocessed into a square matrix of format and passed to the convolutional layers of the feature learning section, and the XGBoost is utilized to solve the regression problem. The conventional and geochemical logs from the Horn River Basin, in western Canada, are selected for the model training and validation. Monte Carlo dropout (MCD) is added to model training to decrease the sensitivity and increase repeatability of model prediction. The comparison of metrics and correlation coefficients shows that the hybrid ML is slightly better than U-net CNN and XGBoost individually, which demonstrates the reliability and effectiveness of the proposed algorithm. The presented method could be applied to other exploration settings such as geothermal, unconformity uranium or sediment-hosted mineral deposits.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".