基于经验模态分解互信息熵与同步压缩变换的微地震信号去噪方法研究

发布时间：2018-05-09 07:02

本文选题：微地震信号 + 经验模态分解　；参考：《石油物探》2017年05期

【摘要】：针对微地震信号具有随机性、非平稳性与时频耦合的特点以及经验模态分解(Empirical Mode Decomposition,EMD)的模态混叠问题,提出了基于经验模态分解互信息熵与同步压缩变换(Synchrosqueezing Transform,SST)的微地震信号去噪方法。首先对微地震信号进行经验模态分解,获得从高频到低频排列的固有模态函数(Intrinsic Mode Function,IMF)分量;然后求取相邻固有模态函数分量之间的互信息熵,从而辨识出高频与低频部分的分界;最后利用同步压缩变换提取高频部分的有效信号,将其与低频部分重构,实现微地震信号的有效去噪。利用不同噪声强度的理论模型和实际资料,对本文方法与直接舍弃高频成分的去噪方法进行了对比,结果表明,本文方法能够很好地去除微地震信号中的混叠噪声,并将有效信号从噪声中提取出来,提高了资料的信噪比。
[Abstract]:In view of the randomness, non-stationarity and time-frequency coupling of microseismic signals and the modal aliasing problem of empirical Mode decomposition (EMD), A method for de-noising micro-seismic signals based on empirical mode decomposition mutual information entropy and synchrosqueezing transform (SST) is proposed. Firstly, the intrinsic Mode functions arranged from high frequency to low frequency are obtained by empirical mode decomposition of microseismic signals, and then the mutual information entropy between the components of adjacent inherent modes is obtained. Finally, the effective signal of the high-frequency part is extracted by synchronous compression transformation, and reconstructed with the low-frequency part to realize the effective de-noising of the micro-seismic signal. By using the theoretical models and practical data of different noise intensity, the method is compared with the method of removing the high frequency components directly. The results show that the proposed method can remove the aliasing noise in the microseismic signal very well. The signal-to-noise ratio is improved by extracting the effective signal from the noise.
【作者单位】：中国地质大学(武汉)地球物理与空间信息学院地球内部多尺度成像湖北省重点实验室;
【基金】：国家重点研发计划(2016YFC060110304) 国家自然科学基金(41572116) 中央高校基本科研业务费专项资金(CUG160602)联合资助~~
【分类号】：P631.4

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