## 1 Introduction
Through in-depth analysis of signal characteristics, we can obtain rich source information, which can be used as the basis for evaluating the stability of rock mass [7]. Compared with other waveform parameters, the frequency-domain characteristics are often unique [8] and relatively stable [9]. The spectrum distribution is an important parameter that can fully reflect the rock failure information carried by the acoustic emission waveform [10]. The MS signal frequency contains rich source information, and the change characteristics of different sequence frequency components can reflect different activity states inside surrounding rocks [11; 12]. It has unique advantages and uses the frequency characteristics to conduct rockburst early warning and stability analysis for deep-buried tunnels. Some studies have shown that the high frequency of an MS event corresponds to a small-scale crack [13]. Therefore, the study on the change characteristics of the MS event frequency spectrum has great significance for rockburst early warning and obtaining the MS activity laws. The spectrum of acoustic emission/MS can better reflect the characteristics of rock damage [14]. An increase in the low frequency of acoustic emission can indicate an imminent rockburst [15; 16]. Some studies have shown that lower frequency or a trend of spectrum moving to the low-frequency band is rockburst precursor signals [17; 18; 19; 20; 21]. With the rapid development of signal processing technology, a lot of time-frequency analysis theories have been proposed. In recent years, the HHT has been widely used to process nonlinear signals. [PERSON] et al. [22] first applied the HHT method to ocean analysis. Some scholars [23; 24; 25] applied the HHT to the spectrum research of seismic signals. [PERSON] et al. [26] applied the HHT to speech signal denoising with different noise levels, and it was proved that the method was effective. Based on the HHT theory, [PERSON] et al. [27] improved the empirical mode decomposition, improved the decomposition accuracy, and proved the effectiveness of the improved HHT method for low SNR nonstationary signal analysis. [PERSON] [28] applied the HHT to image restoration for the first time and achieved good results. [PERSON] et al. [29] introduced the HHT into gear fault diagnosis for the first time and proposed the local Hilbert energy spectrum concept. Good test results have been obtained. [PERSON] [30] applied the HHT method to seismic phase analysis of seismic signals and achieved good results. [PERSON] et al. [31] proposed a signal analysis method for acoustic nondestructive detection of full-length bonded bolts based on HHT. [PERSON] et al. [32] used the HHT method to analyze the blasting vibration signal in a lead-zinc mine in South China, and accurately identified the detonator initiation time of seven time delay levels. [PERSON] and [PERSON] [33] applied the HHT method to identify different components of the spectra of real sea waves. [PERSON] et al. [34] used the HHT for the extraction of a new relevant damage descriptor to be adopted for acoustic emission (AE) pattern recognition in order to help understand the damage process. [PERSON] et al. [35] showed that complementing the HHT with techniques such as zero-phase filtering, kernel density estimation, and Fourier analysis allows it to be used effectively to detect and characterize signals with low signal-to-noise ratios. [PERSON] et al. [36] analyzed the cutting force amplitude and frequency by means of wavelets and HHT. Numerous studies have shown that the HHT method is a relatively scientific signal processing method. Therefore, we can also introduce this method into rockburst prediction. This paper focuses on rockburst prediction of the tunnels of the Shuangjiangkou hydropower station. The on-site construction environment is very complex, and fracture characteristics inside surrounding rocks are also complex. Therefore, rockbursts occurred frequently and were difficult to predict. The MS signals released contained rich information.