摘要
针对矿山采集微震信号中混杂的噪声,提出基于变分模态分解(VMD)和小波阈值去噪的VMD联合小波阈值去噪方法提取有效的微震信号,首先利用VMD分解获得一系列高频至低频的本征模态分量,然后对由高频噪声主导的模态分量进行小波阈值去噪,去噪后的高频信号分量与原先的低频分量信号进行重构,完成信号的去噪步骤。工程实例表明,与EEMD方法和单纯的VMD方法相比,VMD联合小波阈值去噪拥有更好的降噪效果。用该方法对200组岩体破裂信号和200组爆破振动信号进行去噪,再以0~100Hz频段能量比值高于80%作为岩体破裂信号的判别条件,结果表明,其对岩体破裂信号的识别准确率达到99%,对爆破振动信号的识别准确率达到98%。
The collected microseismic signals in mine are usually mixed with certain degree of noise.In order to solve this problem,a VMD-WTD method based on variational mode decomposition(VMD)and wavelet threshold denoising was proposed to extract effective microseismic signals.Firstly,VMD was used to obtain a series of eigenmode components distributed from high frequency to low frequency,and then wavelet threshold denoising was performed on the modal components dominated by high frequency noise.The high frequency signal components after denoising and the original low frequency component signal were reconstructed to complete the signal denoising step.A case study showed that,compared with the EEMD method and the simple VMD method,VMD-WTD method hada better noise reduction effect.By using this method,200 sets of rock burst signals and 200 sets of blasting vibration signals were denoised,and the energy ratio of 0~100 Hz band higher than 80% was set as the discriminating condition of rock burst signal.The experimental results showed that the recognition accuracy of the rock burst signal reached 99%,while the recognition accuracy of the burst vibration signal reached 98%.
作者
刘玉桥
邓红卫
吴路波
申一鹏
田玉起
虞松涛
LIU Yuqiao;DENG Hongwei;WU Lubo;SHEN Yipeng;TIAN Yuqi;YU Songtao(Zhaojin Mining Industry Co.,Ltd,Zhaoyuan,Shandong 265406,China;School of Resource and Safety Engineering,Central South University,Changsha,Hunan 410083,China)
出处
《矿业研究与开发》
CAS
北大核心
2020年第2期98-103,共6页
Mining Research and Development
关键词
变分模态分解
小波阈值
微震监测
信号去噪
矿山地压
Variational mode decomposition
Wavelet threshold denoising
Microseismic monitoring
Signal denoising
Mine ground pressure
