GNSS blasting vibration monitoring and feature recognition method

Abstract

Aiming at the problems of the current blasting vibration monitoring, such as unstable low-frequency performance, vulnerability to environmental interference, complex operation, and easily damaged, a new blasting vibration monitoring and feature recognition method of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multiscale permutation entropy (MPE) and singular spectrum analysis (SSA) is proposed based on global navigation satellite system (GNSS). Initially, a new method of GNSS blasting vibration monitoring is proposed according to the blasting environment and blasting characteristics, it can provide a new reference for GNSS technology to be used in blasting vibration monitoring. Then, signal simulation proved that the proposed feature recognition method can effectively remove the low-frequency and high-frequency background noise as well as trend items of blasting vibration signals and accurately extract the dominant frequency of vibration compared with the traditional processing methods. Finally, the proposed method is demonstrated by factory open blasting and tunnel excavation blasting compared with the monitoring results of the conventional blasting recorder, it can accurately recognize the characteristics of blasting initiation time, particle peak velocity (PPV), initiation period, and dominant frequency of vibration. The RMSE of PPVs is 0.086, and other errors are within 5%, which is highly consistent with the monitoring results of blasting recorder. The GNSS blasting vibration monitoring and feature recognition method can improve the low-frequency stability, field adaptability, simple operation, and information richness of blasting monitoring, which can provide a new approach for blasting monitoring; it can also be an important supplement to existing blasting monitoring methods.