基于APO-VMD联合小波阈值的爆破振动信号去噪方法研究

PDF(3085 KB)

振动与冲击 ›› 2025, Vol. 44 ›› Issue (15) : 249-258.

冲击与爆炸

基于APO-VMD联合小波阈值的爆破振动信号去噪方法研究

李洪超1,2，张继1，石玉莲3，黄国泉4，屈晨亮5，衣佳欣6，文义明*6

作者信息 +

Denoising method for blasting vibration signals based on APO-VMD combining wavelet threshold

LI Hongchao1,2, ZHANG Ji1, SHI Yulian3, HUANG Guoquan4, QU Chenliang5， YI Jiaxin6， WEN Yiming*6

Author information +

文章历史 +

摘要

爆破过程中受复杂地质条件的影响，导致振动信号掺杂大量噪声，一定程度掩盖了信号的真实信息。为有效降低爆破振动信号的噪声部分，提出一种结合人工原生动物优化器算法（Artificial Protozoa Optimizer，APO）、变分模态分解（Variational Mode Decomposition，VMD）和小波阈值的去噪方法，以获得振动信号的真实时域和频域信息。该方法通过APO算法，寻找使的VMD分解效果最佳的本征模态数K和惩罚因子α，然后利用VMD对含噪信号进行自适应分解，剔除方差贡献率较小的含噪模态分量，对保留下来的模态分量进行小波阈值去噪处理，最终得到重构后的去噪真实信号。分别以仿真信号和现场实测爆破振动信号为初始信号，利用APO-VMD联合小波阈值、经验模态分解、小波阈值去噪、VMD等方法对其进行去噪处理，结果表明APO-VMD联合小波阈值方法能有效地去除噪声信号，使之最大保留信号真实信息，对爆破振动信号去噪研究具有理论意义和工程价值。

Abstract

Due to the influence of complex geological conditions in the blasting process, the vibration signal is mixed with a large amount of noise, which covers the real information of the signal to a certain extent. In order to effectively reduce the noise part of blasting vibration signal, a denoising method combining Artificial Protozoa Optimizer (APO), Variational Mode Decomposition (VMD) and wavelet threshold is proposed to obtain the real time domain and frequency domain information of vibration signal. This method uses the APO algorithm to find the intrinsic mode number K and the penalty factor α that make the best VMD decomposition effect. Then, VMD is used to adaptively decompose the noisy signal, and the noisy modal components with small variance contribution rate are eliminated. The retained modal components are subjected to wavelet threshold denoising processing, and finally the reconstructed denoised real signal is obtained. The simulation signal and the field measured blasting vibration signal are used as the initial signals, and the APO-VMD combined wavelet threshold, empirical mode decomposition, wavelet threshold denoising, VMD and other methods are used to denoise them. The results show that the APO-VMD combined wavelet threshold method can effectively remove the noise signal and make it maximally guaranteed.

导出引用

李洪超1, 2, 张继1, 石玉莲3, 黄国泉4, 屈晨亮5, 衣佳欣6, 文义明6. 基于APO-VMD联合小波阈值的爆破振动信号去噪方法研究[J]. 振动与冲击, 2025, 44(15): 249-258

LI Hongchao1, 2, ZHANG Ji1, SHI Yulian3, HUANG Guoquan4, QU Chenliang5, YI Jiaxin6, WEN Yiming6. Denoising method for blasting vibration signals based on APO-VMD combining wavelet threshold[J]. Journal of Vibration and Shock, 2025, 44(15): 249-258

参考文献

[1] 付晓强, 张仁巍, 雷振, 等. 立井爆破信号趋势项和噪声消除方法[J]. 兰州工业学院学报, 2020, 27(1): 57－62．
FU Xiaoqiang, ZHANGＲenwei, LEI Zhen, et al. iminating trend for vertical shaft blasting signal and themethod of de-noising [J]. Journal of Lanzhou Institute of Technology, 2020, 27(1): 57－62．
[2] 康怡泽, 姚颖康, 董润龙, 等. 基于ICEEMDAN-多尺度排列熵的拆除爆破振动信号降噪研究[J]. 振动与冲击, 2024, 43(13): 275-287.
KANG Yize, YAOYingkang, DONG Runkong, et al. De-noising of demolition blasting vibration signals based on ICEEMDAN ultiscale permutation entropy [J]. Journal of Vibration and Shock, 2024, 43(13): 275-287.
[3] ZHONG G S, DENG Y X, AO L P, et al. Study and application oftranslation invariant wavelet de-noising for blasting seismicsignals[C]//IEEE International Conference on Multimedia Technology. Hangzhou, 2011.
[4] 孙远, 杨峰, 郑晶, 等. 基于变分模态分解和小波能量熵的微震信号降噪[J]. 矿业科学学报, 2019, 4(6): 469－479.
SUN Yuan, YANG Feng, ZHENG Jing, et al. search onmicro-seismic signal denoising based on variational modedecom-position and wavelet energy entropy [J]. Journal ofMiningScience and Technology, 2019，4(6): 469－479.
[5] 谢全民, 龙源,钟明寿, 等. 小波包与分形组合技术在爆破振动信号分析中的应用研究[J]. 振动与冲击，2011, 30(1): 11－15.
XIE Quanmin, LONG Yuan, ZHONG Mingshou, et al. Application of wavelet packet and fractal combination technology in blasting vibration signal analysis [J]. Journal of Vibration and Shock, 2011, 30(1): 11－15.
[6] 黄智刚, 吕虎波, 林一庚, 等. 基于CEEMDAN-MPE算法的隧道爆破地震波信号降噪方法及应用[J]. 爆破, 2020, 37(4): 138－144．
HUANG Zhigang, LvHubo, LIN Yigeng, et al. Denoising method of tunnel blasting seismic wave signal based on CEEMDAN-MPE algorithm and its application [J]. Blasting, 2020, 37(4): 138－144.
[7] 易文华, 刘连生, 闫雷, 等. 基于EMD改进算法的爆破振动信号去噪[J]. 爆炸与冲击, 2020,40(09):77-87.
YI Wenhua, LIU Liansheng, YAN Lei, et al.Vibration signal de-noising based on improved EMD algorithm [J]. explosion and Shock Waves, 2020, 40(09): 77-87.
[8] 王勃, 申思洪任, 蔚立元, 等. 基于VMD和GA-SVM的矿井地震自适应噪声压制方法[J]. 煤炭学报, 2024, 49(03): 1530-1538.
WANG Bo, SHEN Sihongren, YU Liyuan, et al. Mine seismic adaptive noise suppression method based on VMD and GA-SVM [J]. Journal of China Coal Society, 2024, 49(03):1530-1538.
[9] 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(03): 76-82.
SUN Miao, WU Li, Yuan Qian 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(03): 76-82.
[10] 苟倩倩, 赵明生, 张光熊, 等. 基于MEEMD分解的楔形掏槽爆破振动信号分析[J]. 矿业研究与开发, 2019, 39(10): 11-5．
GOU Qianqian, ZHAO Mingsheng, ZHANG Guangxiong et al. Analysis on blasting vibration signal in wedge-shaped cut based on MEEMD Decomposition [J].Mining Research and Development，2019，39(10):11－15.
[11] 赵建平，林杭．基于时频能分析技术的爆炸波能量分布及分离[J]. 岩石力学与工程学报, 2021, 31(S1): 3278-3285.
ZHA0 Jian-ping, LIN Hang. Energy distribution and separation of blast wave based on time-frequency energy analysis technology [J]. Chinese Journal of Rock Mechanics and Engineering, 2012, 3l(Sl): 32783285.
[12] 熊正明, 中国生, 徐国元. 基于平移不变小波爆破振动信号去噪的应用研究[J]. 金属矿山, 2006(2): 12-14．
XIONG Zhengming, ZHONG Guosheng, XUGuoyuan. Application of denoising for blasting vibration signals based on translation invariant wavelet [J]. Metalmine, 2006(2): 12-14.
[13] 闵海根, 方煜坤, 吴霞, 等. 弱GNSS信号下基于EMD和LSTM的车辆位置预测方法研究[J]. 中国公路学报, 2021, 34(7): 128-139.
MIN, Haigen, FANG Yukun, WU Xia, et al.Position pre-diction based on empirical modedecomposition and long short-term memory under global navigation satellite system outages [J]. China Journal of Highway and Transport , 2021, 34(7): 128-139.
[14] 费鸿禄, 刘梦, 曲广建, 等. 基于集合经验模态分解-小波阈值方法的爆破振动信号降噪方法[J]. 爆炸与冲击, 2018,38(01): 112-118.
FEI Honglu, LIU Meng, QU Guangjian, et al. A method for blasting vibration signal denoising based on ensemble empirical mode decomposition-wavelet threshold [J]. explosion and Shock Waves, 2018, 38(01): 112-118.
[15] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition [J]. IEE Transactions on Signal Processing2014, 62(3): 531-544.
[16] 何成兵, 车其祥, 徐振华, 等. 基于参数自寻优变分模态分解的信号降噪方法[J].振动与冲击, 2023,42(19): 283-293.
HE Chenbing, CHE Qixiang, XU Zhenhua, et al. Signal denoising method based on parametric self-optimizing VMD [J]. Journal of Vibration and Shock, 2023,42(19): 283-293.
[17] 莫宏毅,徐振洋,刘鑫,等.基于SSA-VMD的爆破振动信号趋势项去除方法[J]. 振动与冲击, 2023,42(11):304-312+330.
MO Hongyi, XU Zhengyang, LIU Xin, et al.Trend removal method of blasting vibration signal based on SSA-VMD [J]. Journal of Vibration and Shock, 2023, 42(11): 304-312+330.
[18] 高文欣, 刘升, 肖子雅, 等. 柯西变异和自适应权重优化的蝴蝶算法[J]. 计算机工程与应用, 2020, 56(15): 43-50.
GAO Wenxin, LIU Sheng, XIAO Ziya, et al.Butterfly optimization algorithm based on Cauchy variation and adaptive weight [J]. Computer Engineering and Applications, 2020, 56(15): 43-50.
[19] 刘刚, 高成龙, 胡万君, 等. 基于鲸鱼优化算法超参数优化的径向基函数响应面模型的油浸式变压器绕组挡板结构优化[J]. 电工技术学报, 2024,3 9(17): 5331-5343.
LIU Gang, Gao Chenglong, Hu Wangjun, et al. Optimization of Winding Block Washer Structure for Oil Immersed Transformers Based on Radial Basis Function Response Surface Model with Whale Optimization AlgorithmHyper-Parameters Optimization [J]. Transactions of China Electrotechnical Society, 2024,3 9(17): 5331-5343.
[20] 贺亦琛, 师长立, 郭小强,等.基于参数优化多核支持向量机的光伏功率预测算法[J]. 太阳能学报, 2024, 45(09): 394-404.
He YIchen, Shi Changli, Guo Xiaoqiang, et al. Photovoltaic power prediction algorithm based on parameter optimization of multi-kernel svm [J]. Acta Energiae Solaris Sinica, 2024, 45(09): 394-404.
[21] Wang X, Snášel V, Mirjalili S, et al. Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization [J]. Knowledge-Based Systems, 2024: 111737
[22] 王海波, 叶如珊, 杜武. 一种基于EMD和小波阈值的桥梁振动信号混合去噪方法[J]. 公路, 2021, 66(12): 110-116.
WANG Haibo, YE Rushan, DU Wu. Bridge vibration based on EMD and wavelet threshold mixed signal denoising method [J]. Highway, 2021, 66(12): 110-116.
[23] 吴文轩,王志坚,张纪平,等.基于峭度的VMD分解中k值的确定方法研究[J]. 机械传动,2018,42(8):153-157.
WU Wenxuan, WANG Zhijian, ZHANG Ji-ping, et al. Re-search of the method of determining k value in VMD based on kurtosis [J]. Journal of Mechanical Transmission, 2018, 42(8): 153-157.
[24] 钟建军, 宋健, 由长喜, 等. 基于信噪比评价的阈值优选小波去噪法[J]. 清华大学学报(自然科学版), Wavelet de-noising method with threshold selection rules based on SNR evalua-tions [J]. Journal of Tsinghua University (Science & Technology), 2014, 54(2): 259–263.
[25] 蒋田勇,喻晨宇,黄可,等.基于AO算法优化VMD参数联合小波阈值的桥梁信号去噪方法[J]. 中国公路学报,2023,36(07):158-168.
JIANG Tianyong, YU Chenyu, HUANG Ke, et al. Bridge Signal De-noising Method Combined MD Parameters Optimized by Aquila Optimizer with Wavelet Threshold [J]. ChinaJournal of Highway and Transport, 2023, 36(07): 158-168