基于EMD和互信息熵的微震信号自适应去噪  

Self-adaptive denoising for microseismic signal based on EMD and mutual information entropy

作　　者：梁喆[1,2] 彭苏萍[1] 郑晶[1] 

机构地区：[1]中国矿业大学(北京)煤炭资源与安全开采国家重点实验室,北京100083 [2]安徽理工大学电气与信息工程学院,安徽淮南232001

出　　处：《计算机工程与应用》2014年第4期7-11,32,共6页Computer Engineering and Applications

基　　金：国家973重点基础研究发展计划项目(No.2010CB226800);国家"十二五"科技支撑计划(No.2012BAB13B01;No.2012BAC10B03);煤炭资源与安全开采国家重点实验室开放基金项目(No.SKLCRSM10KFA04)

摘　　要：针对强干扰背景下的微震信号提取,提出一种基于经验模态分解(Empirical Mode Decomposition,EMD)和互信息熵的自适应提取算法。通过EMD对微震信号进行分解,得到高频和低频两部分信号,并对分解得到的各阶固有模态分量求出能量和能量熵值。根据互信息准则,通过依次计算相邻分量能量熵之间的互信息值来区分高频和低频信号。将经过自适应阈值滤波后的高频信号和低频信号一起进行信号重构,得到新的微震信号。仿真结果表明,在对微震信号去噪时,该方法可以有效地去除噪声信号,信噪比均提升了10 dB以上。工程上的微震信号通过该方法处理后,也取得了较好的效果。An adaptive extraction algorithm of microseismic signal based on empirical mode decomposition（Empirical Mode Decomposition, EMD）and mutual information entropy is presented to extract the microseismic signal under strong interferences. Firstly, high frequency and low frequency signal are obtained after the microseismic signal is decomposed with the EMD method, and energy and the energy entropy of the intrinsic mode components achieved through decomposition are calculated. According to the mutual information criterion, the high and low frequency signals are distinguished with the mutual information values which are sequentially calculated between adjacent component energy entropy. Filtered by adaptive threshold, the high frequency signal is reconstructed together with the low frequency signal, and a new microseismic is produced. The simulation results show that, this method can suppress the noise signals efficiently. The SNR can be im-proved more than 10 dB. The method can achieve good performance even when applied on the field signals.

关 键 词：经验模态分解 微震信号 能量熵 互信息 

分 类 号：P315.4[天文地球—地震学] TN911.6[天文地球—固体地球物理学]