基于自适应噪声完全集合经验模态分解算法和Hurst指数的地震数据去噪方法  

A seismic data denoising method based on complete ensemble empirical mode decomposition with adaptive noise and Hurst exponent

作　　者：毛世榕 史水平 玉壮基 苏梅艳 李莎 何嘉 幸符 衡张清 Mao Shirong;Shi Shuiping;Yu Zhuangji;Su Meiyan;Li Sha;He Jiaxing;Fu Heng;Zhang Qing(Earthquake Agency of Guangxi Zhuang Autonomous Region,Nanning 530022,China)

机构地区：[1]广西壮族自治区地震局,中国南宁530022

出　　处：《地震学报》2023年第2期258-270,共13页Acta Seismologica Sinica

基　　金：中国地震局监测、预备、科研三结合课题(3JH-2021036)资助。

摘　　要：在地震观测中,地震数据中普遍包含有噪声信号。由于噪声信号的干扰,地震分析的效率会受到不同程度的影响。传统的去噪方法通常需要噪声的先验知识,并且滤波时会造成部分有效信号丢失。针对这一问题,本文提出一种将自适应噪声完全集合经验模态分解(CEEMDAN)算法与Hurst指数相结合的地震数据去噪方法。首先通过CEEMDAN方法将信号分解为一系列本征模函数(IMF),然后利用Hurst指数对滤波后的IMF分量进行识别,最后对地震数据IMF分量进行重构,从而实现数据去噪。与传统方法的去噪效果对比表明,本文方法可将低信噪比波形的去噪效果提高32%,将高信噪比波形的去噪效果提高6倍。同时对地磁数据的去噪结果表明,本文方法能够较完整地将地铁噪声从地磁信号波形中滤除。In seismic observation,seismic data generally contain ambient noise,which reduces the efficiency of seismic analysis.Traditional denoising methods usually need a priori know-ledge of noise,and some effective data will be lost when filtering.To solve this problem,this paper proposes a seismic data denoising method based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and Hurst exponent.Firstly,the signal is decomposed into a series intrinsic mode functions(IMF)by CEEMDAN method.Secondly,the Hurst exponent is used to identify the filtered IMF component.Finally,the IMF component of seismic data is reconstructed to realize data denoising.Compared with the denoising effect of traditional methods,the filtering ability of this method for low SNR waveforms is improved by 32%,and the filtering ability for high SNR waveforms is 6 times higher.At the same time,as shown in the denoising results of geomagnetic data,this method can completely filter subway noise from geomagnetic signal waveform.

关 键 词：地震数据去噪 地磁数据去噪 自适应噪声完全集合经验模态分解 HURST指数 

分 类 号：P315.61[天文地球—地震学]