The intrinsic mode confusion of empirical mode decomposition(EMD) and the ensemble empirical mode decomposition(EEMD) can only suppress mode confusion to a limited extent, as the white noise added by EEMD cannot be fully neutralized, which compromises the completeness of the original signal. Additionally, both methods fail to avoid interference from endpoint effects. Modal confusion and endpoint effects lead to distortions in the time-frequency analysis results obtained from the Hilbert transforms of EMD and EEMD. A complete ensemble empirical mode decomposition with adaptive noise and endpoint processing(EP-CEEMDAN) is proposed to address these issues. Simulation experiments were conducted to compare EMD, EEMD, and EP-CEEMDAN decomposition results on simulated vibration signals. Through multiscale permutation entropy detection and marginal spectral analysis, it was verified that EP-CEEMDAN has better control over endpoint effects and mode confusion, proving that EP-CEEMDAN is a more effective adaptive algorithm than EMD and EEMD. Finally, EP-CEEMDAN was applied to the processing of measured non-stationary vibration signals, where adaptive white noise was added at the endpoints of the vibration signals during each stage of decomposition. The method successfully generated various intrinsic mode functions(IMF) by calculating a unique residual signal. The EP-CEEMDAN algorithm effectively suppresses IMF endpoint divergence and modal confusion, while the time-frequency spectrum obtained through the Hilbert transform offers high resolution in both time and frequency domains. This result can be used for vibration feature recognition in non-stationary vibration signals.

Distortion phenomenon would occur in time-frequency analysis when Hilbert-Huang Transform (HHT) is used to process the blasting seismic signal mixed with noise. In order to improve the analysis performance for noisy blasting seismic signals, the factors affecting the accuracy of time-frequency analysis of HHT were improved through an improved algorithm. Firstly, the empirical mode decomposition (EMD) was improved by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to suppress the low-frequency trend terms. Furthermore, the multiscale permutation entropy (MPE) code was added to control the high-frequency noise. Finally, the normalized Hilbert transform (NHT) was performed on the IMFs obtained by CEEMDAN·MPE. Through the above three steps, the problem of insufficient precision in the time-frequency analysis of noisy blasting seismic signals by the traditional HHT can be improved. In order to verify the accuracy of the CEEMDAN·MPE-NHT algorithm, a comparative study on the HHT and CEEMDAN·MPE-NHT algorithm was carried out, and the CEEMDAN·MPE-NHT algorithm was used for underwater drilling blasting seismic signals. The results show that the time-frequency spectrum of the IMF decomposed by CEEMDAN·MPE and processed by NHT has a greatly improved high resolution compared with HHT in both time and frequency domain. The research results can be used for identifying and controlling the hazards of blasting seismic waves.

The seismic wave signal acquisition will result in the mixed noise in the measured signal due to the monitoring environment, test system and other factors, and the existence of noise will lead to the distortion of the time-frequency analysis results of the signal Hilbert-Huang Transform. There are two reasons. One is that the empirical mode decomposition(EMD) algorithm will obtain the intrinsic mode function(IMF) component with modal confusion phenomenon when processing the blasting seismic wave signal containing noise; The other reason is that because the Hilbert transform is constrained by the Bedrosian theorem, which will produce negative instantaneous frequencies when dealing with modal confusion components. These lead to huge analytical errors. In order to obtain real blasting vibration properties, HHT should be improved. Complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) can be obtained by adding adaptive noise signal to EMD. Then normalized Hilbert transform is performed on the IMF obtained by CEEMDAN, and an improved normalized Hilbert transform(INHT) is obtained. Through the above two steps, the CEEMDAN-INHT time-frequency analysis algorithm can be established. In order to verify that the algorithm can effectively improve the time-frequency analysis accuracy of the noise-containing blasting seismic wave vibration signal, a comparative study on the time-frequency analysis of the HHT and CEEMDAN-INHT noise-containing simulated vibration signals is carried out. Finally, CEEMDAN-INHT is used in the time-frequency analysis of blasting seismic wave signals in an underground cavern, and it is found that the algorithm can effectively overcome the inherent mode confusion of EMD, and at the same time obtain the time-frequency-energy characteristic parameters reflecting the real blasting vibration attributes. It is of practical significance to carry out resonance analysis of blasting excavation in caverns from the perspective of frequency and energy, and to realize blasting seismic wave hazard control.