## 2 Materials and Methods
### Literature Summary
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scholars** & **Method/Objectives** & **Key Findings** & **Limitations/Gaps** \\ \hline [PERSON] et al., 2021 [38] & Proposed an understandable time–frequency convolutional neural network (XTF-CNN) for microseismic waveform categorization. & XTF-CNN outperforms competing methods (CNN, LSTM, RNN-FCN, and ResNet) in both performance and interpretability. & Larger-scale research is needed to quantify the robustness of this method. \\  & Proposed a method for automatic discrimination between microseismic and blasting events based on time–frequency spectral features. & This method reduced the operator’s sensitivity to classification, and improved the accuracy and efficiency of mass spectrometry signal data identification in spectral monitoring technology applications. & Larger-scale research is needed to quantify the robustness of this method. \\  & Used the deep convolutional neural network inception & The DCNN–inception algorithm & Larger-scale research is needed to quantify the robustness of this method, including considering the influence of different blasting schemes on the identification results. \\ [PERSON] et al., 2021 [40] & (DCNN–inception) algorithm for microseismic data recognition. & The DCNN–inception algorithm outperformed CNN in recognition accuracy. & This method has a long training time and requires a large amount of data to refine the network. \\  & Employed a particle swarm optimization (PSO) algorithm to optimize the ELM artificial intelligence model, PSO–ELM, to discriminate microseismic events and explosions. & Compared with the original ELM model and other commonly used intelligent discrimination models, PSO–ELM demonstrated the best discrimination performance. & The method still requires the manual selection of feature parameters. Additionally, it was tested using data from only one mining site, and its generalizability requires further exploration. \\  & Proposed a CNN architecture with an attention mechanism to automatically identify microseismic events. & This model can improve network performance by enhancing the intermediate information in CNN without many additional parameters or significant computational costs. In addition, the multichannel model can achieve the best results. \\  & Developed a hybrid model combining singular spectrum analysis (SSA), CNN, and long short-term memory network (LSTM) to identify microseismic signals. & Compared with common identification methods, including CNN, LSTM, backpropagation neural network (BP), SVM, decision tree (DT), KNN, and linear discriminant analysis (LDA), this hybrid model achieved higher recognition accuracy. & The performance of this method was tested using data from only one mining site, so its generalizability requires further exploration. \\  & Proposed an improved neural network combined with transfer learning model achieved a recognition accuracy of 95\% for microseismic events. & Further large-scale research is required to quantify the robustness of this method. \\  & Proposed a wavelet scattering decomposition (WSD) transform combined with an SVM algorithm for discriminating microseismic signals with low signal-to-noise ratios. & The scattering coefficients of each signal proved to be suitable as features for training unique models. In experimental samples, this model achieved 92.86\% recognition accuracy. & The method was compared only with the standard STA/LTA algorithm and lacks comparison with other more advanced algorithms. \\  & Designed three earthquake event classifiers referencing the CNN structures VGGnet, ResNet, and Inception. These classifiers were tested and compared using three-channel seismic full waveform time series and spectral data. & The test dataset includes three classes of events: natural earthquakes, explosions, and collapses. The results showed that the classifier achieved recall and accuracy rates exceeding 90\%. \\  & & & Lack of comparison with other commonly used research methods. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._1.