## 1 Introduction  
Microseismic technology, as a method for the efficient monitoring of ground pressure in mines and other dynamic disasters, has been widely used in many engineering fields, such as mines, deep tunnels and side slopes. Microseismic monitoring aims to realize the surveillance of the stable state of rocks by analyzing the microseismic events caused by rock fractures. However, due to the nonlinear and nonstationary nature of microseismic signals, the complexity of the signal acquisition environment and the presence of abundant noise in the signals collected by geophones, the identification of effective signals is very difficult [1, 2]. The current microseismic monitoring system for automatic identification has a poor effect regarding the identification of microseismic signals and interfering signals, is easily affected by the characteristics of each signal, and requires human intervention for the identification of effective signals, resulting in low efficiency. In addition, the waveforms of microseismic signals containing noise are similar to those of noise signals. This makes it more difficult to automatically identify effective signals and seriously affects the system's accurate monitoring of microseismic events, which may further lead to misdiagnosis and a loss of early warning opportunities [3; 4]. Therefore, accurately distinguishing effective microseismic signals from abundant signals is particularly important and is a basis for utilizing microseismic technology. In recent years, experts and scholars at home and abroad have made satisfactory achievements in the research on the identification of microseismic signals. Currently, common methods for identifying effective microseismic signals include parametric analysis, waveform analysis, wavelet transform, pattern recognition, etc. But most of these methods require manual processing, which is subject to the unstable classification efficiency and affected by the prior experience of processors [5; 6; 7]. Among the time-frequency analysis methods, the fast Fourier transform (FFT) and wavelet transform are characterized by multi-resolution image analysis, but their parameter settings are based on practical application scenarios and experience, causing some problems [8]. The empirical mode decomposition (EMD) method can adaptively decompose a signal into intrinsic mode functions (IMFs) step by step, but there are problems of modal aliasing and boundary effects. Improved methods such as ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD) solve some of these problems, but problems such as a heavy calculation burden and incomplete spectral decomposition still exist. Variational mode decomposition (VMD) [9], as a new non-recursive signal decomposition method, effectively overcomes the problems of modal aliasing and boundary effects that are difficult to be solved by traditional methods such as EMD, and boasts better noise robustness and noise reduction [10]. However, it is still difficult to screen IMF components. Manual screening has a high accuracy but is very slow and costly. Traditional screening methods are fast but have some flaws. Existing methods include alternating direction method of multipliers (ADMM), energy entropy, etc., but parameters need to be set according to different signal conditions, which affects the self-adaptability of VMD and leads to the impossible complete automatic operation of the noise suppression process [11; 12]. Genetic Algorithm-based Support Vector Machine (GA-SVM) integrates the advantages of genetic algorithm and support vector machines. By introducing global search through genetic algorithms, GA-SVM searches for better model parameters in the parameter space of SVMs and takes full advantage of the excellent generalization performance of SVMs in the training set to ensure good adaptation to unseen data.