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| 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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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.
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Received: 08 April 2019
Published: 25 October 2019
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Corresponding Authors:
Huai-Liang LI
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The EEMD decomposition result of microseismic signals containing high-noise (The signal to noise ratio is -5 db, the frequency of microseismic signal is 120 Hz, and the sampling interval is 6 KHz)
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The sample entropy of IMF
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Comparision of time-frequency analysis beween the above synthesized microseismic data and reconstructed data
a—the synthesized microseismic data;b—the reconstructed signal;c—the result of ban-dpass filter
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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 |
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Signal to noise ratio and root-mean-square error after micro seismic signal synthesized under different signal to noise ratio
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The spectrum analysis and time-frequency analysis of actual microseismic datasets
a—the synthesized microseismic data;b—the reconstructed signal;c—the result of ban-dpass filter
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Comparison of denoising effect of actual microseismic data
a—the result of the method in this paper;b—the result of wavelet multi scale analysis;c—the result of ban-dpass filter;d—the result of low-dpass filter
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