## 5 Results and Discussion
### Models Evaluation
Oversampling involves replicating instances from the minority class while maintaining the original quantity of majority class samples, thereby achieving a more balanced distribution between classes. Additionally, model training can be further optimized by assigning appropriate weights to samples from different classes. It is important to note that conventional random oversampling, which simply duplicates minority class samples, may lead to model overfitting. Therefore, in practical applications, we will prioritize model architectures with robust anti-overfitting capabilities and incorporate more advanced oversampling methods (such as SMOTE) to enhance model performance. The confusion matrix experimental results shown in Figure 10 indicate that the classification model exhibits excellent performance in certain categories while facing significant challenges in others. Overall, the model demonstrates high prediction accuracy for \"Distribution channel\", \"Inter-distributary bay\", and \"Sheet sand\", reflecting strong feature extraction capabilities. Due to the similarities in well logging curves between main channels and distributary channels, a portion of distributary channel images were misclassified as main channels. In the delta facies, the main channel and distributary channel are located within the delta plain, and their depositional environments are similar. Both have similar lithofacies characteristics, mainly composed of sandstone, with upward-fining sedimentary sequences, and they commonly develop tabular and trough cross-bedding with asymmetric ripple marks and scour-fill structures. However, there are differences in thickness variations and well logging curve morphologies between the main channel and distributary channel. The main channel is usually thicker than the distributary channel, and its well logging curves typically show high-amplitude bell or box shapes, while the distributary channel's well logging curves may show low-amplitude bell or box shapes. The inter-distributary bay, characterized by uniquely identifiable well logging curves, achieved the highest recognition accuracy. In this study, the limited sample size of natural levee samples led to occasional recognition difficulties. However, the overall model performance underscores its reliability in identifying dominant depositional facies. As depicted in Figure 11, the loss curves demonstrate that both training and validation losses decreased rapidly within the first five training epochs. By epoch 35, the training loss dropped to approximately 0.04, while the validation loss remained at around 0.42. The relatively close proximity of the two curves indicates strong generalization capabilities with no severe overfitting. The accuracy curves reveal an initial low accuracy that improved significantly during training. The training accuracy ultimately reached 0.85, while the validation accuracy stabilized at 0.75. Overall, the CNN model exhibits effective training performance characterized by continuous loss reduction, steady accuracy improve ment, and minimal divergence between training and validation metrics, confirming robust generalization ability. The learning curve graph illustrated in Figure 12 shows the variation trends of the training accuracy (depicted by the blue line) and validation accuracy (depicted by the red line) during the model's training process. The training accuracy rapidly increased from an initial low value to around 0.8, and while experiencing slight fluctuations, it continued to rise steadily, reaching nearly perfect accuracy of 1.0 by the 55 th training epoch. Meanwhile, although the validation accuracy improved in tandem, it consistently fluctuated around 0.6-0.7 without surpassing this upper limit. The divergence between these two curves indicates mild overfitting in the model, though the overall performance remains satisfactory. Figure 11: Loss and accuracy curve diagrams. Figure 10: Confusion matrices for microfacies recognition models.