# An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference
## 3 Example
### Test Using Real-World Seismic Events  
To assess the generalization ability of the chosen model, seismic events No. 3 and No. 4 were utilized for testing. The confusion matrixes of the test results are presented in Figure 10. It is shown that the neural network achieves similar detection performance on the two seismic events, reaching over 83%. Forcing on seismic event No. 3, the pattern Seismic exhibited a precision of 62.0% and a recall of 80.2%. In the predicted results, 134 samples of the pattern Normal were misclassified as Seismic, causing the relatively low precision. Moreover, the pattern Normal had the highest precision of 97.5%, while the \"Bias\" pattern was less satisfactory, with a precision of 37.7%. This result may be attributed to the model's inability to properly distinguish between \"Normal\" and \"Bias\" or the model's focus on only one of conditions despite the coexistence of both conditions in a certain time interval.  
Figure 8: Train loss and validation accuracy.  
So far, the classification performance has been exhibited and discussed. Classification results were provided without additional comprehensible information. Therefore, an interpretation algorithm for neural networks was employed in this paper to further investigate the classification mechanism. Specifically, the Grad-CAM algorithm was used to visualize the interest region of the correctly classified samples at different depths of neural networks. As shown in Figure 11, the utilized ResNet34 consists of convolutional layers and four residual blocks, apart from the conventional input and output layers. Usually, only the gradient information from the last convolutional layer is used to generate the model's interest region. In this manuscript, the first and last convolutional layers of each residual block were selected for comprehensive visualization. It is shown that the interest regions in the shallow layers, i.e., Block 1 and Block 2, are relatively scattered, mostly appearing as points or lines. Additionally, both temporal features and frequency-domain features were considered by the neural network. The visual explanations denote that the shallow layers of the neural network mainly extracted low-level features. Meanwhile, it can be observed that the interest region in layers of Block 3 and Block 4 converge to form a continuous focusing surface, which is highly similar to the way human eyes focus on images. The globally most representative features in the images are captured. Further, Block 4 has a broader area of interest than Block 3; the reason for this phenomenon is that the deeper the convolutional layer, the smaller the image size, so the range of the interest region relatively expanded. By visualizing the interest regions of neural networks, this approach reveals the  
Figure 10: Confusion matrix of seismic events No. 3 and No. 4.  
Figure 9: The confusion matrix on the validation set.  
local and global features for each sample's classification, helping human users understand the decision basis of neural networks and enhancing trust in their decision-making process.