Predictive lithology mapping using semisupervised learning: Practical insights using a case study from New South Wales, Australia
Bibliographic record
Abstract
ABSTRACT We develop a comprehensive study involving three different types of machine learning (unsupervised, supervised, and semisupervised, which we emphasize) for bedrock-lithology classification using a publicly available data set from New South Wales, Australia. The goal of this work is to demonstrate (1) the value each different type of machine learning can provide and (2) which machine learning type(s) may be preferable under different circumstances. Training data are characteristically limited for geoscience problems, which makes supervised techniques susceptible to overfitting; we explore if semisupervised methods can perform better in these circumstances. Using the geophysical data and geologic map provided for the study area, we compare the performance of two supervised methods (the Light Gradient Boosting Machine and eXtreme Gradient Boosting) with one semisupervised algorithm (label propagation [LP]) in three scenarios with varied limited a priori lithologic constraints (i.e., the training data). Hyperparameter tuning is an essential component of supervised and semisupervised techniques, and the default procedure is to choose the hyperparameter combination with the largest mean cross-validation score. However, we use a new hyperparameter selection strategy that simultaneously uses the mean and standard deviation scores, and we test this new tactic for supervised and semisupervised methods. The results indicate (1) that the new hyperparameter selection technique can slightly improve the performance for supervised and semisupervised methods by 1%–2% compared with the standard selection approach and (2) that LP can outperform the two supervised methods by up to 10%, but it depends on how the training data are distributed. As for the unsupervised analysis, the clusters indicate heterogeneous regions that correlate well with the high-entropy areas in the supervised and semisupervised results. The clustering provides complementary results to the other two types of machine learning and is a source of supporting evidence for suggesting where more in-depth field mapping may be needed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".