基于经验模态分解(EMD)的小波熵阈值地震信号去噪

doi:10.13278/j.cnki.jjuese.201601303

Abstract

Abstract:

In view of the threshold of empirical mode decomposition (EMD) threshold denoising algorithm selected by experience, and its inability to effectively distinguish useful information in the intrinsic mode function, the authors use the wavelet transform of the intrinsic mode function to process each layer of wavelet coefficients so as to highlight the effective information and suppress noise. After the effective signals and the mutation points of the detail coefficients are set to zero, the new detail coefficients are equally divided into several intervals. We select the wavelet entropy as the high frequency wavelet coefficients of the architectural interval average noise variance, and calculated the threshold value. The threshold selection method is based on the characteristics of the wavelet entropy, adapted to the energy characteristics of the corresponding scale signal itself in determination of the scale of the threshold. The algorithm is applied to the simulation signals and real seismic signal denoising. The results show that the method is better than that of the wavelet threshold denoising based on EMD; at the same time it can better protect the effective signals.

Key words: empirical mode decomposition (EMD), wavelet entropy threshold, random noise suppression, SNR

CLC Number:

P631.4

Liu Xia, Huang Yang, Huang Jing, Duan Zhiwei. Wavelet Entropy Threshold Seismic Signal Denoising Based on Empirical Mode Decomposition (EMD)[J].Journal of Jilin University(Earth Science Edition), 2016, 46(1): 262-269.

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