## 4 Experiments
### Experimental Results of Model Performance  
We selected four model structures, which were: a single LightGBM (LGB) model; a model stacking LSTM and 1D-CNN (NN-1D); a model stacking LSTM, 1D-CNN, and 2D-CNN (NN-2D); and finally, using the prediction results of LGB and NN-2D as input, employing a linear regression model as the meta-model for model fusion (stacking). Train-MAE and Test-MAE correspond to the scores of the training data and the testing data, respectively. The final results are based on the testing data. Evaluating and comparing the performance of models on the training and validation datasets is crucial. By observing the MAE curves of different models, we can quickly identify which models perform better for specific tasks. Moreover, if the training MAE continues to decrease, but the validation MAE begins to rise after reaching the minimum value, it is usually an indication of overfitting in the model. Here, the MAE comparisons of different neural network structures in the two sets of experiments separately are shown in Figures 6 and 7. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Feather Name** & **Feather Description** \\ \hline
1 & NN\_Mels\_mean4 & original signal in the \\
5 & 5 th Mel frequency band \\
5 & 5 th original signal in the \\
5 & 5 th Mel frequency band \\
5 & 5 th original signal in the \\
5 & 5 th Mel frequency band \\  
The experimental results show that the performance of the NN-2D neural network was better than that of the NN-1D under the same dataset. As shown in Tables 2 and 3, we can see that the MAE of NN-2D was higher than that of NN-1D during the training process, but NN-2D achieved better results in the testing process. This is because in our work, we used 13% of the data as training and validation data and 87% as test data, so there is some overfitting in the final results. This proves that the NN-2D model has better generalizability. Comparing the same model in both tables shows that the model scores under Dataset 2 are better than Dataset 1. This result demonstrates that the mel spectrogram is beneficial for extracting effective features from earthquake signals. As shown in Table 3, our best  
Figure 6: For the first experiment, the training and validation MAE for the two neural network structures (NN-1D and NN-2D) are shown. Solid lines marked with blue circles and star-shaped markers represent the training MAE and the validation MAE of the NN-1D model, respectively, while solid lines marked with red circles and star-shaped markers represent the training MAE and validation MAE of the NN-2D model, respectively. It can be clearly seen that the performance of the two models is very similar in this dataset, and there are no overfitting issues for either model. Figure 7: In the second experiment, the training and validation MAE for the two neural network structures (NN-1D and NN-2D) are shown. The information represented by the four curves in the figure is the same as in Figure 8. It is found that NN-2D converges faster to fitting on the validation set compared to NN-1D. structure, and the model fusion. These optimizations offer valuable insights for handling similar problems in practical applications. However, accurately predicting earthquakes remains a challenging task. Laboratory earthquake data, while valuable, are a relatively idealized form of data and cannot fully simulate real earthquakes. The complex and variable nature of fault zones constitutes the most significant factor affecting the accuracy of models. In future work, we will continue to explore more feature extraction methods, more efficient model structures, and fusion strategies to achieve even better prediction performance. Conceptualization, W.C.; methodology, T.X. ; software, C.Z. ; writing--original draft preparation, B.Z. ; writing--review and editing, B.Z. All authors have read and agreed to the published version of the manuscript.