Due to the complex terrain and geological conditions in the blasting area, as well as errors in monitoring instruments, reflections of vibration propagation medium, and interference from magnetic fields, a significant amount of noise is often present in the original blasting vibration signals collected. To address this issue, a signal noise reduction smooth model based on complementary ensemble empirical mode decomposition(CEEMD) is proposed. Firstly, the measured blasting vibration signal is decomposed using CEEMD and an algorithm for low-pass filtering is established based on the obtained intrinsic mode function(IMF) component from the decomposition. Additionally, an objective function is constructed to calculate the optimal solution according to similarity and smoothness criteria for filtering algorithms. The resulting filtering algorithm model represents an optimal denoising smooth model for blasting vibration signals. To verify our noise reduction smooth model, a simulation signal is constructed and applied to actual open-pit deep-hole blasting vibration signal research. Finally, the noise reduction effects of empirical mode decomposition(EMD) method, wavelet threshold method, CEEMD-wavelet threshold method, and filter algorithm model BP3 are quantified and compared using two indexes: signal-to-noise ratio and root-mean-square error. It has been confirmed that the proposed noise reduction smooth model effectively reduces noise in open-pit blasting vibration signals. The findings demonstrate that our CEEMD-based noise reduction smooth model for open-pit deep-hole blasting vibrations possesses excellent denoising capabilities while preserving essential characteristic information from the original signals. Furthermore, the denoising effect of the proposed model surpasses that of EMD method, wavelet threshold method, and CEEMD-wavelet threshold method.

In view of the problem of noise and information loss in the CEEMDAN method in the denoising process of actual measurement blasting vibration signals, the clustering analysis method is considered to have good data processing ability. Based on the idea of decomposition-clustering-reconstruction, CEEMDAN-K-means algorithm for denoising of blasting vibration signals is proposed. Firstly, this method decomposes the blasting vibration signal by CEEMDAN method to obtain IMF components of different quantity levels. Then, the K-means clustering analysis algorithm is used to classify the IMF components into five different categories, and variance contribution rate verification is used. Finally, the IMF components of high frequency noise category are removed and the reconstructed pure blasting vibration signal is obtained. Taking the blasting vibration signals from an open-pit mine as example, the signal denoising performance of the CEEMDAN-K-means algorithm was evaluated by signal-to-noise ratio and root mean square error indexes. The research results show that compared with the CEEMDAN method and the EMD-wavelet threshold method, the CEEMDAN-K-means signal denoising method has the largest signal-to-noise ratio (20.06 dB), which is increased by 1.26 dB and 7.7 dB, respectively, and the smallest root mean square error (0.22 10^-3), indicating that the method not only has good denoising effect, but also has good fidelity. Through the comparison and analysis of the denoising effect of different methods, it is known that on the basis of effectively retaining the real signal component, the CEEMDAN-K-means method can effectively remove the high-frequency components contained in the measured blasting vibration signal, and has practicality and effectiveness in the field of blasting vibration signal denoising.