## 3 Results and Discussion
### Classification of Methods
Therefore, time domain and frequency domain analyses are two commonly used methods in microseismic signal processing. Time domain analysis focuses on the temporal variations and waveform characteristics of the signal, providing intuitive time-dependent information, whereas frequency domain analysis focuses on the frequency components and energy distributions of the signal, revealing the frequency characteristics of the signal. These two methods complement each other and together provide a comprehensive description and understanding of the signal, which helps to identify microseismic events and study their frequency characteristics and time correlation. Statistical feature analysis involves extracting and calculating various statistical features from a signal. These statistical features include mean, standard deviation, peak, energy, etc., and can be used to characterize the signal as a whole by analyzing its statistical properties. Statistical feature analysis methods are usually based on mathematical-statistical theory, can be computed quickly, and do not require a large number of training samples. These features can be used to construct rule- or threshold-based classification models. Machine learning methods are a data-driven approach that learns signal features and performs classification using training models. Common machine learning methods include SVM, RF, and naive Bayes. These methods typically require manual feature design and use the extracted features to train classification models. Machine learning methods are suitable for moderate-sized datasets and relatively simple problems, but they may have limitations in dealing with complex signal patterns. Deep learning methods, on the other hand, are based on neural networks and can automatically learn features and patterns through multilayered networks for classification. The core of deep learning methods is ANN [60], such as CNNs and RNNs. These methods optimize network weights using large-scale training data and backpropagation algorithms to automatically extract complex signal features and perform advanced classification. Deep learning methods excel in handling large-scale datasets and complex problems. The difference between machine learning and deep learning methods lies in the former requiring manual design of feature extraction processes and using the extracted features to train classification models, whereas the latter automatically learns to signal features and patterns through multilayer neural networks without the need for manual feature definition. Deep learning methods typically require more training data and computational resources, but they can handle more complex signal patterns and have stronger generalization capabilities. Therefore, in microseismic signal recognition and classification, machine learning methods are suitable for moderate-sized datasets and relatively simple problems, whereas deep learning methods are suitable for large-scale datasets and complex problems. Researchers can choose the appropriate method based on the dataset size, problem complexity, and available resources. Clustering analysis is an unsupervised learning method that divides data samples into different groups or clusters, grouping similar samples together. It focuses on discovering inherent patterns and structures in the data without the need for prior labels or category information. Common clustering algorithms include k-means, hierarchical clustering, and DBSCAN. Clustering analysis can help identify similar patterns and clusters within microseismic signals for further investigation and classification. Hybrid methods combine multiple techniques or methods to leverage their respective strengths. In microseismic signal recognition and classification, hybrid methods often integrate multiple techniques or methods, such as combining clustering analysis with machine learning methods or combining clustering analysis with statistical feature analysis.