CEEMDAN自适应阈值去噪算法在地震方向的应用

提出一种基于自适应完备集合经验模态分解(CEEMDAN,complete ensemble empirical mode decomposition with adaptive noise)的自适应阈值去噪算法。含噪信号经CEEMDAN算法分解成若干个模态分量(IMF,intrinsic mode functions),根据样本熵理论,对IMF分量中高频分量自适应选取,根据噪声和有用信息与原始信号的相关性不同,对高频分量中的噪声系数定位,利用能量熵选取噪声主区间,用高频分量中噪声主区间的噪声系数方差作为阈值,对高频分量进行阈值去噪,进一步去除噪声,保留高频中的有用信息,最后将信噪分离的高频分量和低频分量重构。分别对合成和实际地震信号去噪处理,并与常规去噪算法进行对比。数据仿真和实验结果表明,在原始信号信噪比为0.5dB时,常规与改进算法去噪后信噪比分别为4.55dB和9.97dB,大幅提高信噪比,达到随机噪声压制的目的,实现了高频分量的自适应选取和高频分量中有用信息的再提取。

CEEMDAN adaptive threshold denoising algorithm in application to seismic direction

An adaptive threshold denoising algorithm based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) is proposed in this paper. The noisy signal is decomposed into several modal components(IMF) by CEEMDAN algorithm. Based on the sample entropy theory, the adaptive selection of high frequency components in IMF components is realized, and the noise figure in high frequency components is located according to the different correlation between noise and useful information or the original signal. The main noise interval is selected by energy entropy, and the noise coefficient variance of the main noise interval in high frequency components is used as the threshold. Threshold denoising of high-frequency components is carried out to further remove noise and retain useful information in high-frequency. Finally, high-frequency components and low-frequency components separated from signal-noise are reconstructed. The denoising of synthetic and actual seismic signals is processed separately and compared with conventional denoising algorithms. Data simulation and experimental results show that when the signal-to-noise ratio of the original signal is 0.5 dB, the signal-to-noise ratio obtained by the conventional and improved algorithms is 4.55 dB and 9.97 dB respectively, which indicates significant improvement in the signal-to-noise ratio, achieving the purpose of random noise suppression and realizing the self-adaptive selection of high-frequency components and the re-extraction of useful information from high-frequency components.