## 4 Model Establishment
### Model Train and Test  
Figure 6 showed the training process of the transfer learning. The model was evaluated by train accuracy, validation accuracy, and cross-entropy. In Figure 6, train accuracy, and validation accuracy both increased gradually. Then train accuracy converged to about 97.0% and validation accuracy converged to about 90.0%. Cross-validation decreased gradually and converged to about 0.2. Finally, the test accuracy based on grayscale and color images were 91.0% and 92.6%, respectively. The small difference between the two models indicates that color had little influence on the model identification for geological structures, which means textures are more important in identification.  
The patches from the same image are similar using data augmentation. At the same time, we want to apply the model to identify geological structure images from the geological survey. So we chose another 60 images which were from an engineering project to test the model accuracy. Top-1 and top-3 accuracy were used in model evaluation. Top-1 accuracy means  
Figure 6: Train accuracy, validation accuracy and cross-entropy variation in transfer learning process: (**a**) Grayscale dataset; (**b**) Color dataset.  
highest probability matches the right label. Top-3 accuracy means any one of predictions with the three highest probability matches the right label. In the model testing, the top-1 accuracy was 83.3% and the top-3 accuracy was 90.0%. The test images in Figure 7 are the same as those in Figure 1. In Figure 7, the result showed top-3 prediction probability. In the identification of faults, the top-1 and top-3 result were wrong. The number of fault images should be increased. In the identification of boudin images, the top-1 prediction was wrong. However, we found that the probability of boudin was 15.7%, which ranked third. As a consequence, it is better to apply top-1 and top-3 accuracy to evaluate the result comprehensively in predictions.  
Figure 7: Identification of geological structures images results.  
The training process of CNN was shown in Figure 8. Train accuracy, validation accuracy, and cross-entropy were also used to evaluate the model. Figure 8a-c are the results of the CNN with two, three and four convolutional layers on the color dataset; Figure 8d is the result of the CNN with three convolutional layers on the grayscale dataset. The effects of the three-layer CNN was the best, and the grayscale data was also trained by the CNN architecture, as shown in Figure 8d. The train accuracy was almost 100.0%, while the validation accuracy was about 85.0%, which indicated the model was overfitting.  
Figure 8: _Cont._  
The results of the five methods were summarized in Table 3. The accuracies of KNN, ANN, and XGBoost models were less than 40%, while the KNN and XGBoost also showed better performance in color images with histogram features. The test accuracy of CNN model based on grayscale and color images was 80.1% and 83.3%, but the model was overfitting; the test accuracy of the transfer learning model based on Inception-v3 model reached 91.0% and 92.6%, which was the best in all the methods. The result indicated the convolutional layers and pooling layers in Inception-v3 were able to extract features from geological structures images effectively. As a consequence, the transfer learning method was chosen to identify the geological structure image from engineering. The top-1 and top-3 accuracy were 83.3% and 90.0%, respectively.  
Figure 8: Train accuracy, validation accuracy and cross-entropy variation in the convolutional neural network (CNN) training process: (**a**) Two layers using color dataset; (**b**) Three layers using color dataset; (**c**) Four layers using color dataset; (**d**) Three layers using grayscale dataset.