## 3 Results and Discussion
### Classification of Methods  
Based on existing studies, we further summarized the methods used in the field of microseismic event waveform recognition and classification. Specifically, we categorized these methods into seven categories: time domain analysis methods, frequency domain analysis methods, statistical feature analysis methods, machine learning methods, deep learning methods, cluster analysis methods, and hybrid methods. The explanations and analysis regarding the advantages and limitations of different types of methods are presented in Table 2, aiming to help readers better understand the characteristics and applicability of these methods. Time domain analysis focuses on the variations in time and waveform characteristics of a signal. By observing the amplitude, duration, periodicity, and other characteristics of the signal, intuitive perceptions and time-related information about the signal can be obtained. This information is very useful for the identification and location of microseismic events. Common time domain analysis methods include waveform plots, envelope analysis, autocorrelation functions, and cross-correlation functions. Time domain analysis methods are suitable for detecting transient events, impulse signals, and time correlations. On the other hand, frequency domain analysis is concerned with the components and \begin{table}
\begin{tabular}{c c c c} \hline
**Method** & **Description** & **Advantage** & **Disadvantage** \\ \hline Time domain analysis [54] & Classification based on time domain features such as the amplitude and duration of the waveform. & Simple and intuitive & Limited information richness, cannot capture complex patterns \\ Frequency domain analysis [55] & Extraction frequency features using spectrum analysis, such as dominant frequency and bandwidth, for classification. & Captures frequency-related information & Ignores time domain information, not suitable for non-stationary signals \\ Statistical feature analysis [56] & Extracts statistical features like mean, variance, skewness, etc., to describe waveform characteristics. & Simple computation, low resource consumption patterns & Limited information richness, difficulty in capturing complex patterns \\ Machine learning methods [18] & Includes SVM, Random Forests, etc., mapping waveform features to a feature space for classification. & Suitable for complex relationships, handles high-dimensional data & Relies heavily on feature engineering, may require significant labeled data \\ Deep learning methods [57] & Uses deep learning models like CNNs for feature learning and classification, suitable for complex image information. & Automatically learns features, high adaptability & Requires large amounts of data, complex model parameter tuning, high computational resource consumption \\ Clustering analysis [58; 59] & Clusters waveforms based on similarity, discovering natural groupings in the data. & Can identify unknown categories & Results are highly dependent on the choice of distance measure, clustering algorithm, etc. \\ Hybrid methods [43] & Combines multiple methods, e.g., integrating machine learning with deep learning, or combining time–frequency analysis with statistical feature analysis. & Considers multiple aspects, improves classification accuracy & Requires more data and computational resources, increased algorithm complexity \\ \hline \end{tabular}
\end{table} 
Table 2: Classification and comparison of commonly used methods for microseismic signal identification and classification. spectral characteristics of a signal in terms of frequency. By converting the signal to a frequency domain representation, the frequency distribution, frequency components, and corresponding energy distribution of the signal can be analyzed. Common frequency domain analysis methods include Fourier transform, power spectral density, and spectrogram. Frequency domain analysis methods are suitable for analyzing the frequency characteristics of signals, the interrelationships between frequency components, etc.