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Open AccessArticle

Microseismic Signal Denoising via Empirical Mode Decomposition, Compressed Sensing, and Soft-thresholding

by Xiang Li 1, Linlu Dong 1, Biao Li 2, Yifan Lei 1 and Nuwen Xu 1,*
1
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China
2
College of Petroleum and Natural Gas Engineering, Southwest Petroleum University, Chengdu 610500, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(6), 2191; https://doi.org/10.3390/app10062191
Received: 29 February 2020 / Revised: 12 March 2020 / Accepted: 13 March 2020 / Published: 24 March 2020
(This article belongs to the Section Civil Engineering)
Microseismic signal denoising is of great significance for P wave, S wave first arrival picking, source localization, and focal mechanism inversion. Therefore, an Empirical Mode Decomposition (EMD), Compressed Sensing (CS), and Soft-thresholding (ST) combined EMD_CS_ST denoising method is proposed. First, through EMD decomposition of the noise signal, a series of Intrinsic Mode Functions (IMF) from high frequency to low frequency are obtained. By calculating the correlation coefficient between each IMF and the original signal, the boundary component between the signal and the noise was identified, and the boundary component and its previous components were sparsely processed in the discrete wavelet transform domain to obtain the original sparse coefficient θ. Second, θ is filtered by ST to get the reconstruction coefficient θnew after denoising. Then, CS was used to recover and reconstruct θnew to get the denoised IMFnew component and then recombined with the remaining IMF components to get the signal after denoising. In the simulation experiment, the denoising process of EMD_CS_ST method is introduced in detail, and the denoising ability of EMD_CS_ST, DWT, EEMD, and VMD_DWT under 10 different noise levels is discussed. The signal-to-noise ratio, signal standard deviation, correlation coefficient, waveform diagram, and spectrogram before and after denoising are compared and analyzed. Moreover, the signals obtained from the underground cavern of the Shuangjiangkou hydropower station were denoised by the EMD_CS_ST method, and the signals before and after denoising were analyzed by time-frequency spectrum. These results show that the proposed method has better denoising ability. View Full-Text
Keywords: microseismic signal denoising; empirical mode decomposition; compressed sensing; soft thresholding microseismic signal denoising; empirical mode decomposition; compressed sensing; soft thresholding
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Li, X.; Dong, L.; Li, B.; Lei, Y.; Xu, N. Microseismic Signal Denoising via Empirical Mode Decomposition, Compressed Sensing, and Soft-thresholding. Appl. Sci. 2020, 10, 2191.

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