Main frequency band of blast vibration signal based on wavelet packet transform

Abstract

• The trend component issues, like zero drift and low frequency response, are addressed by the least square method. • Optimal energy fraction of main frequency band of blast vibration signal is 75%. • Frequency-domain energy of blast vibration signals is concentrated mainly in the low frequency segment. As a key parameter in blasting safety criteria, accurately describing the frequency's characteristics is of practical significance. Due to the deficiency of Fourier transform in the analysis of non-periodic and non-stationary signals, this study defined a wavelet frequency domain parameter, referred to as a main frequency band. A computational method associated with the wavelet packet transform is also proposed. To verify the feasibility of main frequency band and the proposed computational method in describing blasting frequency characteristics, an application is exemplified with field blasting vibration signals monitored in a mine. The effects of explosive charge and distance on main frequency band distribution characteristics are also studied. Results show that the main frequency band based on the computational method is a sensitive, accurate and efficient frequency parameter; it can accurately describe the frequency characteristics of blasting signals and effectively overcome the drawbacks in Fourier transform. When the explosive charge is constant, the span of main frequency reduces as a whole as the distance increases, and the frequency domain energy of blast vibration signals are concentrated mainly in the low-frequency range. When the distance is constant, the peak energy of blast vibration signals increase with the increase of explosive charge, without obvious change in main frequency band. To avoid the effects of interferences on frequency characteristics, the least square method is employed to eliminate signal trend components, and the wavelet threshold method with a hard thresholding function and the Birge–Massart strategy is applied in denoising.