## 5 Experiment
### Feature Extraction Experiment  
To verify the model's ability to extract and reconstruct features, we conducted the following feature extraction experiment to verify the validity of the model. We used the vibration waveforms of the 802 working faces in a certain mining area to construct the dataset, with a sampling frequency of 500 Hz. Firstly, the data was preprocessed, and 500 microseismic event waveforms, 500 noise event waveforms, and 500 blasting event waveforms were extracted. These datasets were standardized as the input of the model. The weight initialization of the model adopts variance scaling initialization. This method can adaptively adjust the variance of the initial distribution according to the number of neurons in each layer, alleviate the problems of gradient explosion or gradient disappearance, and improve the training efficiency of the neural network. Compared with the traditional Gaussian distribution initialization and the truncated Gaussian distribution initialization, using variance scaling initialization enables the model to have better generalization ability. In this model, we used the Adam optimizer, the loss function was set as the mean square error, and the batch size was 128. Table 1 shows the comparison of the model's reconstruction performance under different regularization methods. In this model, using spectral regularization proved to be the best choice, as it outperformed layer normalization and batch normalization in both reconstruction performance and the optimal reconstruction effect after 500 iterations. This confirms that adopting spectral regularization significantly enhances the stability and quality of the reconstruction model. Subsequently, we conducted a series of ablation experiments to verify the positive impact of various modules on the model's performance. We compared the model with  
* **MCAE**, using multi-scale fusion convolution blocks but not using dilated convolution blocks. * **DCAE**, using dilated convolution blocks but not using multi-scale fusion convolution blocks. * **CAE**, an autoencoder that does not use multi-scale fusion convolution blocks or dilated convolution blocks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **500 Iterations of MSE** & **Reach an Optimal Number of Iterations** & **Achieve Optimal MSE** \\ \hline Spectral Normalization & \(2.4254\times 10^{-4}\) & 1399 & \(6.4803\times 10^{-5}\) \\ Layer Normalization & \(5.2606\times 10^{-4}\) & 1754 & \(8.6004\times 10^{-5}\) \\ Batch Normalization & \(3.8187\times 10^{-4}\) & 1263 & \(1.6813\times 10^{-4}\) \\ Without Normalization & \(6.1302\times 10^{-4}\) & 1815 & \(7.1117\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of reconstruction performance of different regularization methods. For consistency, each model employed variance scaling initialization and spectral regularization as the regularization term, with early stopping set to a patience value of 50 to determine if the optimal solution was reached. The detailed experimental results are presented in Table 2.  
Table 2 summarizes the mean square error (MSE) of all ablation experiment models on the dataset. Compared with other models, the MDCAE model proposed in this paper performs best in the reconstruction effect. For a comprehensive comparison, we compared this model with the ExtraMAE [50] model, the LSTM-SAE [51] model, and the Time-VAE [52] model. The results show that although the optimal number of iterations of our proposed MDCAE model is slightly higher than that of other models, it still performs well in the reconstruction effect. Table 3 summarizes the MSE scores of all candidate models and the number of iterations to reach the best state. The ExtraMAE model integrates the concepts of Autoencoder and Matrix Factorization to obtain a compact, low-dimensional representation of time series data and maintain the accuracy of reconstruction.