## 5 Result and Discussion
The presence of interbedded argillaceous stone with low resistivity causes significant fluctuations in the resistivity curve for this region, while the GG and GR curves remain relatively stable, consistent with the characteristics of limestone. Compared to Well No. 1, Random Forest demonstrates lower accuracy in predicting limestone in Well No. 2, with considerable discrepancies from the test set results (Table 3). This highlights the uncertainty in RF's predictions across different wells, whereas the neural network predictions exhibit greater consistency. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Coal** & **Sandstone** & **Limestone** & **Others** \\ \hline Number & 131 & 1101 & 173 & 2565 \\ Precision & 0.70/0.85/0.94 & 0.57/0.61/0.59 & 0.46/0.46/0.74 & 0.81/0.80/0.76 \\ Recall rate & 0.88/0.75/0.67 & 0.46/0.41/0.33 & 0.66/0.78/0.97 & 0.84/0.87/0.90 \\ F1-Score & 0.78/0.80/0.78 & 0.51/0.49/0.42 & 0.54/0.57/0.84 & 0.82/0.83/0.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Precision, Recall, and F1-Score of different lithofacies predictions for Well No. 101. Black, blue and red correspond to CNN, RESNET and RF respectively. Figure 9: Comparison of predictions of different models and input logs in well No. 2. The top section of Well No. 3 exhibits characteristics similar to those of Well No. 2 (Figure 10, Table 4); however, the overall prediction performance for Well No. 3 is superior to that of Wells No. 1 and No. 2. The accuracy, recall, and F1-Score for Well No. 3 are generally higher than those for Wells No. 1 and No. 2. In addition, there is no obvious correlation between geological age and logging data. This indicates a significant variability in our models' prediction performance across the different wells, highlighting the need for further data expansion to achieve a more generalized model. Additionally, the performances of the CNN and ResNet are comparable, suggesting that more complex models do not provide a significant advantage for feature extraction from the current training set. While RF performs similarly to the neural networks in predicting sandstone, others, and coal, it outperforms the neural networks in predicting limestone, but its performance is less stable. It is important to note that borehole diameter and mud density have a significant impact on the count rate (CPS) of a single detector, and resistivity logging reflects the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Coal** & **Sandstone** & **Limestone** & **Others** \\ \hline Number & 221 & 1835 & 123 & 4557 \\ Precision & 0.91/0.77/1.0 & 0.64/0.85/0.89 & 0.62/0.65/1.0 & 0.87/0.85/0.87 \\ Recall rate & 0.65/0.63/0.63 & 0.51/0.65/0.69 & 0.81/0.97/1.0 & 0.93/0.93/0.96 \\ F1-Score & 0.75/0.70/0.78 & 0.57/0.74/0.78 & 0.70/0.78/1.0 & 0.90/0.89/0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The same as Table 1 but for Well No. 3. Figure 10: Comparison of predictions of different models and input logs in well No. 3.  
water content or mineral composition of the rock and has little information about lithology. However, we believe that resistivity and count rate still have a correlation with lithology, at least for the area where the data was trained. Although there is significant uncertainty in the correspondence between the data and lithology, this is precisely the kind of problem that deep learning aims to solve. Due to the considerable uncertainty and the limited types of logging data available, our prediction accuracy for sandstone and limestone is not very high. Nevertheless, our deep learning model provides a preliminary lithology classification model, which is meaningful for identifying target layers using logging curves. Figure 9 shows the bottom coal with a high gamma ray, but the coal seams typically exhibit low natural gamma values. In our work area, which is mainly characterized by Permian deltaic to coastal facies coal, the most significant difference compared to coal seams in most other regions is that the bottom coal seam is underlain by a layer of clay.