## 5 Result and Discussion  
The common metrics used to evaluate a model's performance include precision, recall, and F1-Score. Precision is the proportion of correctly predicted positive samples to the total number of samples predicted as positive, defined as follows [36]:  
\[Precision=\frac{TP}{TP+FP}\]  
where TP and FP are true positive and false positive. TP is the number of instances correctly classified as positive, while FP is the number of instances incorrectly classified as positive. Recall, also known as sensitivity, is the proportion of correctly predicted positive samples to the total number of actual positive samples. \[Recall=\frac{TP}{TP+FN}\]  
The F1-Score is the harmonic mean of precision and recall, providing a single metric that balances the two [37]. \[F_{1}=2\times\frac{Precision\times Recall}{Precision+Recall}\]  
We evaluated the performance of various trained models on the test set, with the statistical distributions presented in Figure 7. It is clear that the predictions of other types exhibit the best performance due to the substantial samples, with both the precision and recall for all three models approaching 90%. Following this, the precision and recall of coal seams are approximately 80% across the three models. Sandstone typically achieves precision levels of above 70%, but its lower recall suggests that the models may overlook sandstone instances. Despite the greater number of sandstone samples compared to coal, its performance is inferior, indicating that the three types of well logs are more sensitive to coal seams. Limestone shows the poorest performance, characterized by lower precision due to the limited number of limestone samples, which account for only about one-tenth of the total sample size. Nevertheless, limestone exhibits a high recall, suggesting that all  
Figure 6: The distribution of test set accuracy under different estimators and random states, with the red triangle indicating the location of the highest accuracy. three models are capable of identifying limestone structures of sufficient thickness (e.g., Figure 8). To further assess the performance of the models, we employed additional data from three wells (No. 1, No. 2, No. 3) for lithology classification. These data are independent of both the training and test sets, allowing us to evaluate the models' generalization capabilities. In Well No. 1 (Figure 8), the predictions from all three models are generally consistent. Coal seems are characterized by intermediate resistivity and low density; sandstone is associated with high resistivity, high density, and low gamma values; and limestone, while similar to sandstone, exhibits higher resistivity. Argillaceous' features are more complex, with a natural gamma content varying with clay content, generally higher density, and lower resistivity. All three models demonstrate high accuracy in predicting coal. Among the predictions, the primary sandstone interfaces are largely aligned. However, some sandstone layers are mistakenly identified as limestone due to the similarities in high  
Figure 8: Comparison of predictions of different models and input logs in well No. 1. Figure 7: Lithofacies prediction performance on test dataset. (**a**) CNN (**b**) RESNET (**c**) RF. resistivity, high density, and low radioactivity between sandstone and limestone in well logs, despite limestone's higher resistivity. The models effectively identify thick limestone layers in deeper sections, indicating a high recall for limestone detection. Nevertheless, the CNN and ResNet exhibit lower precision in limestone identification, while RF achieves higher precision for limestone but shows relatively lower precision and recall for sandstone compared to the neural networks (Table 2). The prediction results for Well No. 2 are comparable to those for Well No. 1 (Figure 9). Within the limestone and sandstone intervals above 650 m, there are notable errors in predicting limestone and sandstone.