Optimization analysis of time frequency spectrum enhancement of tunnel blasting vibration signal

Fu Xiaoqiang; Ma Yan; Yu Jin; Dai Liangyu; Huang Lingjun

doi:10.19606/j.cnki.jmst.2023.03.008

Volume 8 Issue 3

Jun. 2023

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Article Navigation > Journal of Mining Science and Technology > 2023 > 8(3): 348-356

Fu Xiaoqiang, Ma Yan, Yu Jin, Dai Liangyu, Huang Lingjun. Optimization analysis of time frequency spectrum enhancement of tunnel blasting vibration signal[J]. Journal of Mining Science and Technology, 2023, 8(3): 348-356. doi: 10.19606/j.cnki.jmst.2023.03.008

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Optimization analysis of time frequency spectrum enhancement of tunnel blasting vibration signal

doi: 10.19606/j.cnki.jmst.2023.03.008

Fu Xiaoqiang^{1, 3, 4
,},
Ma Yan²,
Yu Jin^{3
,
,},
Dai Liangyu⁴,
Huang Lingjun¹

1.
College of Architecture and Civil Engineering, Sanming University, Sanming Fujian 365004, China
2.
Fujian Sanming Yihong Construction Engineering Co., Ltd., Sanming Fujian 365499, China
3.
Fujian Research Center for Tunneling and Urban Underground Space Engineering, Huaqiao University, Xiamen Fujian 361021, China
4.
Sanming Coffer Fine Chemical Industrial Co., Ltd., Sanming Fujian 365500, China

Received Date: 2022-07-19
Rev Recd Date: 2022-09-08
Publish Date: 2023-06-30

Abstract

Abstract

Aiming at the problem of insufficient time-frequency resolution of tunnel blasting vibration signal, a time-frequency image enhancement algorithm based on convolutional neural network is applied, through the time-frequency image enhancement of the measured tunnel blasting signal, the aggregation range of the blasting signal energy in the time-frequency domain is captured, and the real signal reflecting the blasting characteristics is reconstructed; according to the real signal, the initiation time of detonator in blasting network is accurately distinguished, and the characteristics of tunnel blasting detonator disaster source are identified.The analysis shows that the time-frequency image enhancement algorithm based on convolutional neural network can effectively suppress the cross-terms in the signal, retain the auto-terms of the signal to the greatest extent, and improve the energy aggregation and time-frequency resolution of the blasting signal; The mixed use of different batches of detonators is the main disaster causing factor of tunnel safety.Supervision should be strengthened to realize safe and efficient tunnel construction.
- tunnel blasting,
- time frequency spectrum,
- disaster source,
- feature extraction,
- blasting safety

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References(25)

References

[1]	付晓强, 俞缙, 刘纪峰, 等. 隧道爆破振动信号畸变校正与混沌多重分形特征研究[J]. 振动与冲击, 2022, 41(6): 76-85. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202206011.htm Fu Xiaoqiang, Yu Jin, Liu Jifeng, et al. Distortion correction and fractal characteristics of vibration signals of a tunnel blasting[J]. Journal of Vibration and Shock, 2022, 41(6): 76-85. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ202206011.htm
[2]	马晨阳, 吴立, 孙苗. 自由面数量对水下钻孔爆破振动信号能量分布及衰减规律的影响[J]. 爆炸与冲击, 2022, 42(1): 145-156. https://www.cnki.com.cn/Article/CJFDTOTAL-BZCJ202201013.htm Ma Chenyang, Wu Li, Sun Miao. Influence of free surface numbers on the energy distribution and attenuation of vibration signals of underwater drilling blasting[J]. Explosion and Shock Waves, 2022, 42(1): 145-156. https://www.cnki.com.cn/Article/CJFDTOTAL-BZCJ202201013.htm
[3]	Fu X Q, Yu J, Dai L Y, et al. Cross-term suppression method for time-frequency spectrum of engineering blasting signals[J]. Journal of Testing and Evaluation, 2022, 50(1): 20210094. doi: 10.1520/JTE20210094
[4]	Zhao C J, et al. Blast responses of shallow-buried prefabricated modular concrete tunnels reinforced by BFRP-steel bars[J]. Underground Space, 2022, 7(2): 184-198. doi: 10.1016/j.undsp.2021.07.004
[5]	Jiang N, Jia Y, Yao Y, et al. Experimental investigation on the influence of tunnel crossing blast vibration on upper gas pipeline[J]. Engineering Failure Analysis, 2021, 127: 105490. doi: 10.1016/j.engfailanal.2021.105490
[6]	张日强, 闫大洋, 曹洋, 等. 矿山爆破振动信号时频分析的研究进展综述[J]. 世界有色金属, 2021(16): 130-131. doi: 10.3969/j.issn.1002-5065.2021.16.058 Zhang Riqiang, Yan Dayang, Cao Yang, et al. Review on the research progress of time-frequency analysis of mine blasting vibration signals[J]. World Nonferrous Metals, 2021(16): 130-131. doi: 10.3969/j.issn.1002-5065.2021.16.058
[7]	Xu S, Zhang C, Chen Z, et al. Accurate identification of microseismic waveforms based on an improved neural network model[J]. Journal of Applied Geophysics, 2021, 190: 104343. doi: 10.1016/j.jappgeo.2021.104343
[8]	孙苗, 吴立, 袁青, 等. 基于CEEMDAN的爆破地震波信号时频分析[J]. 华南理工大学学报: 自然科学版, 2020, 48(3): 76-82. https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG202003009.htm Sun Miao, Wu Li, Yuan Qing, et al. Time-frequency analysis of blasting seismic signal based on CEEMDAN[J]. Journal of South China University of Technology: Natural Science Edition, 2020, 48(3): 76-82. https://www.cnki.com.cn/Article/CJFDTOTAL-HNLG202003009.htm
[9]	Baydar N, Ball A. A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-ville distribution. [J]. Mechanical Systems and Signal Processing, 2001, 15(6): 1091-1107. doi: 10.1006/mssp.2000.1338
[10]	李清, 徐文龙, 张迪, 等. 爆破振动信号分析中模态混叠和虚假分量消除的改进方法[J]. 振动与冲击, 2019, 38(17): 197-204. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201917028.htm Li Qing, Xu Wenlong, Zhang Di, et al. An improved method to eliminate modal aliasing and false component in blast vibration signals[J]. Journal of Vibration and Shock, 2019, 38(17): 197-204. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201917028.htm
[11]	单仁亮, 宋永威, 白瑶, 等. 基于小波包变换的爆破信号能量衰减特征研究[J]. 矿业科学学报, 2018, 3(2): 119-128. http://kykxxb.cumtb.edu.cn/article/id/129 Shan Renliang, Song Yongwei, Bai Yao, et al. Research on the energy attenuation characteristics of blasting vibration signals based on wavelet packet transformation[J]. Journal of Mining Science and Technology, 2018, 3(2): 119-128. http://kykxxb.cumtb.edu.cn/article/id/129
[12]	李桐, 陈明, 叶志伟, 等. 不同耦合介质爆破爆炸能量传递效率研究[J]. 爆炸与冲击, 2021, 41(6): 4-14. https://www.cnki.com.cn/Article/CJFDTOTAL-BZCJ202106001.htm Li Tong, Chen Ming, Ye Zhiwei, et al. Study on the energy transfer efficiency of explosive blasting with different coupling medium[J]. Explosion and Shock Waves, 2021, 41(6): 4-14. https://www.cnki.com.cn/Article/CJFDTOTAL-BZCJ202106001.htm
[13]	赵明生, 张建华, 易长平. 基于小波分解的爆破振动信号RSPWVD二次型时频分析[J]. 振动与冲击, 2011, 30(2): 44-47. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201102011.htm Zhao Mingsheng, Zhang Jianhua, Yi Changping. Blasting vibration signal RSPWVD quadratic time-frequency analysis based on wavelet decomposition[J]. Journal of Vibration and Shock, 2011, 30(2): 44-47. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201102011.htm
[14]	史秀志, 薛剑光, 陈寿如. 爆破振动信号双线性变换的二次型时频分析[J]. 振动与冲击, 2008, 27(12): 131-134, 185. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ200812031.htm Shi Xiuzhi, Xue Jianguang, Chen Shouru. Quadratic time-frequency distribution analysis of blasting vibration signal based on bilinear transformation[J]. Journal of Vibration and Shock, 2008, 27(12): 131-134, 185. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ200812031.htm
[15]	郑江韬, 齐子豪, 刘佳存, 等. 基于卷积神经网络的煤岩微裂隙提取方法[J]. 矿业科学学报, 2022, 7(6): 680-688. doi: 10.19606/j.cnki.jmst.2022.06.005 Zheng Jiangtao, Qi Zihao, Liu Jiacun, et al. Segmentation of micro-cracks in fractured coal based on convolutional neural network[J]. Journal of Mining Science and Technology, 2022, 7(6): 680-688. doi: 10.19606/j.cnki.jmst.2022.06.005
[16]	付晓强, 刘纪峰, 黄凌君, 等. 城市地铁区间隧道爆破振动信号趋势项和噪声消除方法[J]. 铁道科学与工程学报, 2020, 17(9): 2328-2337. https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD202009019.htm Fu Xiaoqiang, Liu Jifeng, Huang Lingjun, et al. Trend term removal and de-noising method for blasting vibration signal of urban subway tunnel excavation[J]. Journal of Railway Science and Engineering, 2020, 17(9): 2328-2337. https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD202009019.htm
[17]	韦啸, 高文学, 王林台, 等. 基于EEMD的地铁隧道爆破振动信号分析与应用研究[J]. 隧道建设(中英文), 2019, 39(8): 1293-1300. https://www.cnki.com.cn/Article/CJFDTOTAL-JSSD201908014.htm Wei Xiao, Gao Wenxue, Wang Lintai, et al. Analysis and application of metro tunnel blasting vibration signal based on EEMD[J]. Tunnel Construction, 2019, 39(8): 1293-1300. https://www.cnki.com.cn/Article/CJFDTOTAL-JSSD201908014.htm
[18]	温廷新, 陈晓宇, 刘天宇, 等. 基于优化IGA-ELM模型的爆破振动特征参量预测研究[J]. 中国安全科学学报, 2017, 27(11): 37-42. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201711008.htm Wen Tingxin, Chen Xiaoyu, Liu Tianyu, et al. Predicting research on characteristic parameters of blast-induced vibration based on optimized IGA-ELM model[J]. China Safety Science Journal, 2017, 27(11): 37-42. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK201711008.htm
[19]	付晓强, 黄凌君, 张仁巍, 等. 基于匹配追踪算法的立井爆破信号时频特征提取[J]. 爆破器材, 2020, 49(6): 54-60. https://www.cnki.com.cn/Article/CJFDTOTAL-BPQC202006010.htm Fu Xiaoqiang, Huang Lingjun, Zhang Renwei, et al. Time-frequency feature extraction of shaft blasting signal based on matching pursuit algorithm[J]. Explosive Materials, 2020, 49(6): 54-60. https://www.cnki.com.cn/Article/CJFDTOTAL-BPQC202006010.htm
[20]	梅比, 汪旭光, 杨仁树. 基于改进MP-WVD算法的核电厂建设爆破振动信号处理方法[J]. 爆炸与冲击, 2019, 39(4): 152-162. https://www.cnki.com.cn/Article/CJFDTOTAL-BZCJ201904019.htm Mei Bi, Wang Xuguang, Yang Renshu. Blasting vibration signal analysis technology of construction of nuclear power plant based on improved MP-WVD algorithm[J]. Explosion and Shock Waves, 2019, 39(4): 152-162. https://www.cnki.com.cn/Article/CJFDTOTAL-BZCJ201904019.htm
[21]	谢全民, 贾永胜, 姚颖康, 等. 城市高架桥拆除爆破振动信号的非线性特征分析[J]. 工程力学, 2022, 39(10): 79-87. https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX202210008.htm Xie Quanmin, Jia Yongsheng, Yao Yingkang, et al. Nonlinear characteristic analysis of blasting vibration signals in the demolition of urban viaducts[J]. Engineering Mechanics, 2022, 39(10): 79-87. https://www.cnki.com.cn/Article/CJFDTOTAL-GCLX202210008.htm
[22]	Liu X H, Shi G F, Liu W N. An improved empirical mode decomposition method for vibration signal[J]. Wireless Communications and Mobile Computing, 2021, 2021: 5525270.
[23]	孙兵, 彭亚雄, 苏莹. 基于自适应CEEMD-MPE算法的矿山爆破振动信号去噪研究[J]. 爆破, 2022, 39(2): 153-158, 185. https://www.cnki.com.cn/Article/CJFDTOTAL-BOPO202202023.htm Sun Bing, Peng Yaxiong, Su Ying. Denoising of mine blasting vibration signal based on adaptive CEEMD-MPE algorithm[J]. Blasting, 2022, 39(2): 153-158, 185. https://www.cnki.com.cn/Article/CJFDTOTAL-BOPO202202023.htm
[24]	Wang Z Y, et al. A comparative study of delay time identification by vibration energy analysis in millisecond blasting[J]. International Journal of Rock Mechanics and Mining Sciences, 2013, 60: 389-400.
[25]	付晓强, 俞缙. 隧道爆破信号交叉项抑制及雷管延期时间研究[J]. 中国安全科学学报, 2021, 31(12): 53-61. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK202112008.htm Fu Xiaoqiang, Yu Jin. Study on cross terms suppression of tunnel blasting vibration signals and time of detonator delay[J]. China Safety Science Journal, 2021, 31(12): 53-61. https://www.cnki.com.cn/Article/CJFDTOTAL-ZAQK202112008.htm

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Optimization analysis of time frequency spectrum enhancement of tunnel blasting vibration signal

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