## 2 Microseismic Monitoring Method under Tunnel Construction
### Classification of Microseismic Signals  
The monitoring environment around an engineering rock mass is complex and dynamic, and the microseismic system can record signals from different sources, including the microfracture signal, rockburst signal, blast signal, and noise (e.g., drilling noise, construction background, and electromagnetic interference), as shown in Figure 3. In rockburst warning, the focus is on microfracture signal. Typically, it has a clear wave head and a decaying wave tail, and the frequency domain peak is typically between 50 and 500 Hz. However, in engineering construction, the waveform of the microfracture signal and noise are often similar or intertwined. The focus of the microseismic signal analysis and research is to accurately distinguish microseismic signals and eliminate interference signals. Scholars have conducted considerable research on the classification of microseismic signals. Conventional methods use characteristic parameters as the basis for classification, which can be the characteristics of the original signal in the time and frequency domains, such as the dominant frequency, amplitude, and duration, and can also be source parameters, such as the magnitude, energy, and corner frequency. For example, [PERSON] and  
Figure 3: Typical signals received from different sources. [PERSON] [18] extracted 16 features from 71 features in the time and frequency domains and introduced them into a support vector machine to establish a classification model to distinguish noise events from microseismic events. [PERSON] et al. [19] employed the peak amplitude ratio (S/P ratio) and the complexity value as input parameters and inputted them to feedforward neural networks (FFNNs), adaptive neural fuzzy inference systems (ANFIS), and probabilistic neural networks (PNNs) to discriminate between earthquakes and quarry blasts. [PERSON] et al. [20] used the principal component analysis to convert source parameters into unrelated variables and inputted them to an artificial neural network to classify microtremors and explosions. [PERSON] et al. [21] used five source parameters and probability density functions related to the blasting time as classification indicators. They used the Fisher classifier, naive Bayesian classifier, and logistic regression as classifiers to distinguish blasts and events. This type of method is simple and flexible, requires fewer training samples, and can be deployed quickly. However, it relies heavily on the artificial selection of the characteristic parameters, and the classification results are typically dichotomies, making it difficult to cope with the complex engineering monitoring environment. In recent years, the development of deep learning has provided a new idea for the classification of microseismic signals. [PERSON] et al. [22] combined a deep convolution neural network (DCNN) and support vector machine (SVM) to extract features from multichannel waveforms and classify them. Based on the time-frequency analysis of typical microseismic signals obtained from the Hanjiang-to-Weihe River Diversion Project, [PERSON] et al. [23] proposed a convolutional neural network (CNN) model for tunnel microseismic signal recognition based on the depth space and channel information, which is used to identify and classify complex signals. [PERSON] et al. [24] treated the original signal as the characteristic matrix as the input, trained a CNN with 35 layers on 20,000 samples, and classified the microseismic signals as microseismic events, blasting, ore extraction, mechanical noise, and electromagnetic interference. These studies used the supervised learning method, with a large number of samples for training, through self-learning to identify the depth characteristics of the original signal. The prediction accuracy and robustness of this method are superior to those of conventional methods. However, the method of supervised learning requires a large number of labeling samples. With the changes in the engineering environment, the accuracy of deep learning method may decrease.