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物探与化探  2019, Vol. 43 Issue (5): 1083-1089    DOI: 10.11720/wtyht.2019.0196
  方法研究·信息处理·仪器研制 本期目录 | 过刊浏览 | 高级检索 |
一种集成经验模态分解的样本熵阈值微地震信号降噪方法
王亚娟1,2, 李怀良1,2,4, 庹先国1,2,3,4, 沈统1,2,3,4
1. 西南科技大学 国防科技学院,四川 绵阳 621010
2. 西南科技大学 核废物与环境安全国防重点学科实验室,四川 绵阳 621010
3. 四川轻化工大学,四川 自贡 643002
4. 成都理工大学 地质灾害防治与地质环境保护国家重点实验室,四川 成都 610059
A denoising method for microseismic signal based on the ensemble empirical mode decomposition of sample entropy threshold
Ya-Juan WANG1,2, Huai-Liang LI1,2,4, Xian-Guo TUO1,2,3,4, Tong SHEN1,2,3,4
1. National Institute of Defense Technology,Southwest University of Science and Technology,Mianyang 621010,China
2. Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory,Southwest University of Science and Technology,Mianyang 621010,China
3. Sichuan University of Science & Engineering,Zigong 643002,China;
4. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology,Chengdu 610059,China
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摘要 

微地震信号的采集过程中,会不可避免地混合非平稳随机噪声,传统的线性滤波和频谱分析方法对这种混合信号的去噪效果并不理想。针对这一需求,本文提出了一种新的降噪方法。首先对含噪声的微地震信号执行集成经验模态分解(EEMD),获取一系列不同频率成分的本征模态函数(IMF);为了区分这些IMF分量中的信号和噪声,文中通过计算各个IMF分量的样本熵,根据所设置的样本熵阈值来提取符合微地震信号特征的IMF分量,并对这些IMF分量进行信号重构,由此达到抑制随机噪声的目的。将提出的方法应用于模拟数据和实测微地震数据,均表明该方法具有理想的降噪效果。

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王亚娟
李怀良
庹先国
沈统
关键词 微地震信号集成经验模态分解(EEMD)样本熵降噪    
Abstract

It is inevitable to mix up non-stationary random noise in the process of microseismic signal acquisition.However,the practice shows that the traditional linear filtering and spectrum analysis methods are not idealistic for this mixed signal.In view of such a situation,this paper presents a new method to suppress nonstationary random noise.Firstly,the Ensemble Empirical Mode Decomposition (EEMD) is carried out for noise-containing microseismic signals,and a series of Intrinsic Mode Functions (IMF) with different frequencies components are obtained.In order to accurately identify the signal and noise in these IMF components,the authors calculated the sample entropy of each IMF in this paper.The threshold value of sample entropy was used to extract the IMF components conformable to the characteristics of microseismic signal,and these IMF components are reconstructed in order to suppress random noise.The proposed method has been applied to simulated data and measured microseismic data,and it is indicated that the method has ideal effect for noise reduction.

Key wordsmicroseismic signal    Ensemble Empirical Mode Decomposition (EEMD)    sample entropy    denoising
收稿日期: 2019-04-08      出版日期: 2019-10-25
:  P631.4  
基金资助:国家自然科学基金面上项目(41774118);国家自然科学基金青年科学基金项目(41604088);国家自然科学基金青年科学基金项目(41604153);四川省科技厅项目(2017JY0006);四川省科技厅项目(2019YFG0294)
通讯作者: 李怀良
作者简介: 王亚娟(1993-),女,硕士研究生,主要研究方向为信号与信息处理。
引用本文:   
王亚娟, 李怀良, 庹先国, 沈统. 一种集成经验模态分解的样本熵阈值微地震信号降噪方法[J]. 物探与化探, 2019, 43(5): 1083-1089.
Ya-Juan WANG, Huai-Liang LI, Xian-Guo TUO, Tong SHEN. A denoising method for microseismic signal based on the ensemble empirical mode decomposition of sample entropy threshold. Geophysical and Geochemical Exploration, 2019, 43(5): 1083-1089.
链接本文:  
https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2019.0196      或      https://www.wutanyuhuatan.com/CN/Y2019/V43/I5/1083
Fig.1  强噪声微地震信号的EEMD分解结果(信噪比为-5 dB,微地震信号主频为120 Hz,采样间隔为6 KHz)
Fig.2  IMF的样本熵值
Fig.3  上述合成微地震数据与重构数据的时频分析比较
a—合成微地震信号;b—文中方法;c—带通滤波方法
低信噪比信号
(信噪/dB)
信噪比/dB 均方根误差
带通滤波 小波多尺度分析 文中方法 带通滤波 小波多尺度分析 文中方法
-6 -0.6685 3.4813 4.2578 1.2541×10-6 8.4185×10-7 7.4916×10-7
-7 -0.7374 2.4833 3.2462 1.3264×10-6 9.4437×10-7 8.4995×10-7
-8 -0.8780 1.4848 2.2462 1.3323×10-6 1.0594×10-6 9.4804×10-7
-9 -0.8968 0.4861 1.2818 1.3358×10-6 1.8850×10-6 1.0692×10-6
-10 -1.2649 -0.5130 0.3064 1.3999×10-6 1.3334×10-6 1.1989×10-6
Table 1  不同信噪比合成微地震信号去噪后信噪比与均方根误差
Fig.4  实测微地震数据频谱分析与时频分析
a—合成微地震信号;b—文中方法;c—带通滤波方法
Fig.5  实测微地震监测数据去噪效果对比
a—文中方法去噪效果;b—小波多尺度分析去噪效果;c—带通滤波方法;d—低通滤波方法
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