# Recognition of Earthquake Surface Ruptures Using Deep Learning
## 4 Results and Analysis  
After determining the deep learning network and training method, we used the data from the study area to train and test the network. The experimental test platform was a Dell Precision T3610 motherboard with Intel(R) Xeon(R) CPU E5-1660 [EMAIL_ADDRESS] GHz (12 CPUs), \(\sim\)3.7 GHz, and an NVIDIA Quadro K600 graphics card. The environment for deep learning was built using the Google deep learning framework TensorFlow with a primary programming language, Python. In addition, GPU acceleration was used to perform high-performance computing. The experimental data of two regions, maduoDom1 and maduoDom2, were used for testing. As shown in Figure 6, the red lines are artificially marked surface cracks. In the maduoDom1 data, we cropped 1155 high-resolution UAV images and removed the data without cracks. Finally, we extracted 507 images with cracks, randomly selected 480 UAV images with crack labels for training, and used the remaining 27 UAV images for testing. Similarly, for the maduoDom2 data, we extracted 176 images with cracks from the cropped 348 high-resolution UAV images and randomly selected 150 and 26 UAV images and their crack labels to train and test the model. While training the crack recognition model with two datasets, we set the learning rate to 0.001. For the maduoDom1 array,12 blocks were selected as a batch, and the entire training data set was trained for 26 epochs. For the maduoDom2 data, ten blocks were selected as a batch, and 18 training epochs were performed. The entire model training process randomly selected training images to test the results after several epochs. The training process of the maduoDom1 data is shown in Figure 7. Each row depicts a high-resolution UAV image, a label, the current prediction crack probability, and the prediction results. Figure 7a is a comparison of the predicted results after all learning parameters are initialized by [PERSON]. Figure 7b shows the crack identification result after the eighth epoch training. Here, the crack location has been identified, but many parts are still missing in the result. Figure 7c shows the crack identification results after 16 epochs. Most cracks could be identified, and the missing parts gradually became smaller. Figure 7d shows the crack identification result after the 26 epochs, which could accurately identify the crack location and is similar to the actual situation. After training, we used the trained models to predict cracks in the two datasets and test the crack identification effect of the model. In addition, we randomly selected two image identification results from the prediction results for comparison (Figure 8). Unfortunately, the crack details were still lacking, and the results were not entirely consistent with the original labels. The trained model can, however, accurately determine the location of the cracks, and the required computational time is very short (a high-resolution crack prediction could be made within 1 s). In addition, we quantitatively evaluated the recognition results of the two datasets (Table 2). The commonly used evaluation indicators for deep learning semantic segmentation, such as evaluation index accuracy rate, F1 value, and average intersection ratio, were calculated. Compared with the original labels in the test data, the trained model had a reasonable accuracy rate of more than 97%. Furthermore, the F1 value exceeded 0.51, and the mIoU value exceeded 0.50. These evaluation results showed that the deep learning model obtained by training could perform well in the crack identification problem. In order to compare the differences in the recognition results of different network structures, we constructed a model where the HDC and ASPP structures were removed, the Conv3 layer and FC4 layer were replaced by ordinary convolution, and other structures remained unchanged. The model was trained to identify cracks under the same conditions.