## 2 HHT Theory and Signal Recognition
### Signal Recognition  
A drilling and blasting method was adopted at tunnels of the Shuangjiangkou hydropower station, which made the construction noise more complex. The MS system installed can capture a lot of signals. Figures 1-5 show some signals in the time domain and their HHT spectrum. The amplitude unit of the amplitude-time curve is set to V (Volt). The vertical coordinate of the HHT spectrogram represents the frequency and the color represents the amplitude. HHT spectrum maps have high temporal resolution and display a lot of effective information.  
Figure 1 shows a signal monitored during the drilling hole. Four cyclic waveforms occurred within 800 ms. With the change of frequency over time, there were also four energy concentration areas, which indicated that there were four cyclic waveforms. The frequency distribution (FD) range was about 300-600 Hz. The noise frequency was not obvious in the figure, because the noise amplitude was small, and it was automatically shielded. Due to on-site construction, many drilling signals can be monitored.  
An excavation blasting event is shown in Figure 2. The HHT spectrum showed that the rock failure process was extremely complex during excavation blasting. The FD range was about 100-1300 Hz, and the energy distribution of 400 Hz to 900 Hz was relatively concentrated. There was no obvious energy concentration area over time. The frequency points were discrete. Only a part of the blasting excavation signal was intercepted in the figure. There was a clear difference between the blasting signal HHT spectrum and other signals.  
Figure 1: Waveform analysis of a drilling hole signal in the time domain and the HHT spectrum.  
Figure 2: _Cont_.  
Figure 3 shows a signal when a truck whistled. The FD range of the event narrowed over time. The event energy was not obviously distributed below 400 Hz. There was also a small amount of information distribution in relatively high-frequency bands (800-900 Hz). The signal duration is relatively long. The frequency points were discrete.  
Figure 3: Waveform analysis of a truck horn signal in the time domain and the HHT spectrum.  
Figure 2: Waveform analysis of an excavation blasting signal in the time domain and the HHT spectrum.  
Figure 4 shows a typical MS signal on 3 January 2018. The information of the MS signal was mainly distributed in 0.06-0.09 s, with a short duration of 0.03 s. The FD range was about 300-600 Hz, the FD had a downward trend over time, and there was an obvious energy concentration area in the figure. The frequency of energy concentration areas was relatively low.  
Figure 5 shows the waveform of a rockburst occurred on 13 January 2018. The information of the rockburst signal was mainly distributed in 0.045-0.095 s, with a relatively long duration of 0.05 s. The FD range was about 100-600 Hz, which was relatively wide. In the low-frequency band, the signal information increased significantly; that is, the energy distribution increased significantly. The signal had obvious coda, and its FD range narrowed over time. For rockburst events, the amplitude was larger, the released energy was higher, and the signal component was more complex.  
Figure 4: Waveform analysis of an MS event signal in the time domain and the HHT spectrum.