卢 俊1,2,吴建星1,2.基于EEMD方法的地下矿山微震信号去噪研究[J].有色金属(矿山部分),2019,71(4):12-18.
基于EEMD方法的地下矿山微震信号去噪研究
Denoising of microseismic signal in underground mines based on EEMD method
  
DOI:10.3969/j.issn.1671-4172.2019.04.003
中文关键词:  微震  去噪  聚合经验模态分解  小波  信噪比
英文关键词:microseismic  denoising  ensemble empirical mode decomposition  wavelet  signal-to-noise ratio
基金项目:
     
作者单位
卢 俊1,2 (1. 武汉科技大学 资源与环境工程学院,武汉 430081
2. 冶金矿产资源高效利用与造块湖北省重点实验室,武汉 430081)
吴建星1,2 (1. 武汉科技大学 资源与环境工程学院,武汉 430081
2. 冶金矿产资源高效利用与造块湖北省重点实验室,武汉 430081)
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中文摘要:
      对地下矿山实时在线监测的微震信号进行微震事件特征提取和识别分类研究时,识别的效率往往取决于训练样本和测试样本的质量,为提高数据样本的质量,去除信号中掺杂的噪声,采用聚合经验模态分解(EEMD)方法对地下矿山微震信号进行预处理。通过采用EEMD分析方法对矿山微震信号进行预处理,获得从高频到低频铺展的一组固有模式分量(IMF)及一个残余分量,通过计算各分量能量占比把IMF中的噪声部分及残余项去除,再将包含矿山微震信号主要信息的剩余分量进行重构,从而得到去噪后的微震信号。通过信号仿真实验及实例分析,对比小波预处理方法,结果表明:该方法利用EEMD自适应分解的特性不但克服了小波阈值和分解函数选取困难等弊端,而且能显著提高信号的信噪比,较好地保留了信号形态,获得较为理想的去噪效果。
英文摘要:
      In the study of feature extraction and classification of microseismic events for real-time on-line monitoring of underground mines, the recognition efficiency often depends on the quality of training samples and test samples. In order to improve the quality of data samples and remove the noise doped in signals, the ensemble empirical mode decomposition (EEMD) method was used to pre-process the microseismic signals of underground mines. A series of intrinsic mode functions (IMF) and a residual component spread from high frequency to low frequency were obtained by using EEMD analysis method. The noise and residual components in IMF were removed by calculating the energy proportion of each component, and then the residual components containing the main information of the mine microseismic signal were reconstructed to obtain the microseismic signal after denoising. Through signal simulation experiment and case analysis, the wavelet pretreatment method was compared. The results indicated that the method not only overcomed the disadvantages of wavelet threshold and decomposition function selection, but also significantly improved signal-to-noise ratio, retained signal shape and achieved ideal denoising effect by using EEMD adaptive decomposition characteristics.
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