Distortion correction and fractal characteristics of vibration signals of a tunnel blasting

被引：0
|
作者：
Fu X. [1 ,2 ,3 ]
Yu J. [2 ]
Liu J. [1 ]
Yang R. [4 ]
Dai L. [3 ]
机构：
[1] School of Civil Engineering, Sanming University, Sanming
[2] Fujian Research Center for Tunneling and Urban Underground Space Engineering, Huaqiao University, Xiamen
[3] Sanming Coffer Fine Chemical Industrial Co., Ltd., Sanming
[4] School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2022年 / 41卷 / 06期
关键词：
Baseline estimation and denoising with sparsity (BEADS); Blasting signal; Chaos characteristics; Multi-fractal detrended fluctuation analysis (MF-DFA); Time-frequency correlation;
D O I：
10.13465/j.cnki.jvs.2022.06.011
中图分类号：
学科分类号：
摘要：
Affected by the test environment, the monitoring vibration signals of a tunnel blasting generally contain noise and trend interference components. To eliminate the interference items, the distorted blasting signals detected in a typical tunnel were selected as the analysis objects. The approach of baseline estimation and denoising with sparsity (BEADS) was used to extract the noise and trend item and to obtain the calibrated signal that could reflect the true information. The chaotic fractal characteristics of the three components were captured by the multi-fractal detrended fluctuation analyses (MF-DFA), and the time-frequency domain correlations between them and the original signal were accurately characterized according to the wavelet correlation aggregation spectrum. The results show that the chaotic fractal characteristics of the high frequency noise, low frequency trend item and calibrated signal of the tunnel blasting are significantly different. The trajectory of the calibrated signal attractor is characterized by a fractal spectrum with persistent and anti-persistent periodic fluctuation and its recursion graph has periodic pattern. The attractors of the low-frequency trend item have the shape of approximate straight line and the persistent fractal spectrum characteristics, and its recursion graph has the mutation pattern of diagonal distribution. The attractor of the high frequency noise is characterized by random fluctuation and anti-persistent fractal spectrum, and its recursion graph has drift pattern. In the range of wavelet influence cone with confidence of 95%, the calibrated signal, trend item and noise have the characteristics of persistent positive correlation, local negative correlation, and no correlation with the original signal respectively. The effective separation of the three components and the extraction of chaotic fractal features provide objective characterization and quantified indexes for the identification and classification of blasting signal components. © 2022, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：76 / 85
页数：9
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