## 5 Results and Discussion  
### Models Evaluation  
For facies classification, evaluation metrics encompass accuracy and similarity-based measures, such as the Jaccard similarity score (ranging from 0 to 1, where values closer to 1 indicate higher prediction accuracy) and the Matthews correlation coefficient (MCC) (ranging from \(-1\) to 1, where 1 represents perfect prediction, 0 indicates random guessing, and \(-1\) denotes total inversion). These measures are essential for evaluating the performance of the CNN model in classifying facies accurately and effectively. In addition to these measures, the confusion matrix serves as a comprehensive evaluation tool, providing a detailed breakdown of the model's performance across different classes. This tool is particularly useful for identifying any potential biases in the model's predictions, which can be addressed through further optimization and fine-tuning. The model evaluation results for the CNN-2D architecture are presented in Table 4, which provides a comprehensive overview of the performance metrics for this model. The model exhibits high accuracy on both training and test datasets, achieving average accuracies of approximately 89.13% and 83.49%, respectively, indicating strong overall performance. Jaccard similarity coefficients remain elevated (>0.89) across both datasets, demonstrating substantial overlap between predicted boundaries and ground-truth labels. While the test set achieves marginally higher average accuracy (0.894478 vs. 0.906416), the difference is negligible. Conversely, the training set displays significantly higher Matthews correlation (0.9220971 vs. 0.906416 for the test set), suggesting slight overfitting to the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **Jaccard Similarity** & **Matthews Correlation** & **Number of** & **Average** \\  & **Coefficient** & **Coefficient** & **Samples** & **Accuracy** \\ \hline Training Set & 0.890081 & 0.9220971 & 334 & 0.8913 \\ \hline Test Set & 0.894478 & 0.906416 & 144 & 0.8349 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model evaluation metrics results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & \begin{tabular}{c} **Cross-** \\ **Validation** \\ **Fold 1** \\ \end{tabular} & \begin{tabular}{c} **Cross-** \\ **Validation** \\ **Fold 2** \\ \end{tabular} & \begin{tabular}{c} **Cross-** \\ **Validation** \\ **Fold 3** \\ \end{tabular} & \begin{tabular}{c} **Cross-** \\ **Validation** \\ **Fold 4** \\ \end{tabular} & \begin{tabular}{c} **Cross-** \\ **Validation** \\ **Fold 5** \\ \end{tabular} & \
\begin{tabular}{c} **Validation** \\ **Fold 5** \\ \end{tabular} &
\begin{tabular}{c} **Mean** \\ **Fold 5** \\ \end{tabular} \\ \hline Training Set & 0.8879 & 0.7916 & 0.8045 & 0.8084 & 0.8294 & 0.8243 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cross-validation model evaluation results. training data. In conclusion, the model demonstrates robust performance but retains room for optimization to enhance generalization capabilities. We implemented standard 5-fold cross-validation with randomized sample allocation to ensure robust performance evaluation while maintaining computational efficiency. The choice of 5 folds was deliberate to provide sufficient training data in each split given our limited sample size and yield more stable performance estimates, as 2-fold validation would halve the training set and potentially introduce higher variance in results. This approach aligns with common practice in machine learning studies with medium-sized datasets. The average accuracy on the training set is 0.8243 (approximately 82.43%), indicating that the model performs well overall. Fold 1 performs significantly better than the other folds, while Fold 2 performs relatively weaker. Overall, the model performs well, but the performance differences among the folds need attention to ensure the reliability and generalization ability of the model. The issue of imbalanced class distribution in datasets can be addressed through oversampling techniques.