Improved Unet in Lithology Identification of Coal Measure Strata
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Bibliographic record
Abstract
Abstract The lithology of underground formations can be determined using logging data, which is important for a variety of subsurface geoscience and industrial applications. Deep learning technology offers the advantage of discovering a potential relationship between input and output variables, making it a great choice for generating fast and cost-effective lithology classification models. To automatically characterize lithologies, a multiclass image segmentation problem is considered and an improved Unet as a solution is adopted. The model’s input data is two-dimensional images composed of rock feature data at different depths, and the outcome is a result of one-dimensional rock lithology classification. The algorithm’s practicality was tested using the logging data set from the Xinjing mining area in Shanxi Province, in north-central China, and an open-source data set of Canadian strata. Our model is tested against the 1D-convolutional neural network (CNN) and XGBoost algorithms using a good logging data set of the same depth and different depths for testing. The results show that the improved Unet method outperforms the 1D-CNN and XGBoost algorithms in the classification of rock lithologies. This algorithm has high application potential in the automatic interpretation of rock lithologies.
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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 | 0.001 | 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 it