Improved Unet in Lithology Identification of Coal Measure Strata
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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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 | 0.001 |
| 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".