## 2 Materials and Methods
### Literature Summary  
This study's resources were obtained from credible academic databases such as Google Scholar, Web of Science, Scopus, and PubMed. The study information was indexed using a preset set of keywords such as \"microseismic event\", \"microseismic waveform\", \"machine learning\", \"deep learning\", \"image recognition\", and \"image classification\". An extensive search of Google Scholar was undertaken and relevant studies were categorized according to subject significance. A thorough analysis of the existing literature is crucial for understanding methodologies in microseismic event waveform recognition and classification. By examining current research, we can draw insights from predecessors to guide future studies. Table 1 summarizes the important discoveries, limitations, and pros and cons of various methods used in this field. It provides a comprehensive reference for researchers, encompassing statistics, spectral analysis, traditional machine learning, and deep learning approaches. These findings offer valuable insights into the strengths and limitations of different methods of addressing the challenges of microseismic event recognition and classification. \begin{table}
\begin{tabular}{c c c c} \hline
**Scholars** & **Method/Objectives** & **Key Findings** & **Limitations/Gaps** \\ \hline [PERSON] et al., 2018 [29] & Using a deep convolutional neural network with spatial pyramid pooling (DCNN-SPP) method for joint recognition and classification of multichannel microseismic waveforms. & Automatic updating of network parameters was achieved by directly processing multichannel waveforms through end-to-end training and testing. The classification accuracy of the test set was 91.13\%. & Training data are limited, and classification accuracy can be further improved. \\ [PERSON] and [PERSON], 2019 [30] & Using CNNs to detect microseismic events in a comprehensive distributed acoustic sensing (DAS) strain wavefield. & Neural networks provide low-cost, automated detection of microseismic events. & The study only compared the STA/LTA algorithm, lacking comparisons with other advanced algorithms. \\ [PERSON] et al., 2019 [32] & Using a hybrid technique of DCNN and support vector machine (SVM) to identify and classify multichannel microseismic waveforms. & The DCNN–SVM method outperformed random forests (RF) and k-nearest neighbors (KNN), with an accuracy rate of 98.18\%. & Preprocessing of the dataset, including noise reduction and filtering, required a significant amount of time and effort. \\ [PERSON] et al., 2019 [32] & Using a hybrid technique of DCNN and support vector machine (SVM) to identify and classify multichannel microseismic waveforms. & The DCNN–SVM method outperformed random forests (RF) and k-nearest neighbors (KNN), with an accuracy rate of 98.18\%. & The data preprocessing process is relatively cumbersome. \\ [PERSON] et al., 2019 [32] & Using a hybrid technique of DCNN and support vector machine (SVM) to identify and classify multichannel waveforms. & The DCNN–SVM method outperformed random forests (RF) and k-nearest neighbors (KNN), with an accuracy rate of 98.18\%. & The study lacks stability and robustness testing of the model using actual data from different mines. \\ [PERSON] et al., 2019 [32] & Using a hybrid technique of DCNN and support vector machine (SVM) to identify and classify multichannel microseismic waveforms. & The model outperformed the accuracy obtained with SVM and Fisher classifiers, achieving 94.4\%. & The study lacks stability and robustness testing of the model using actual data from different mines. \\ [PERSON]