Research on the seismic signal denoising with the LMD and EMD method

J. Yu, Ze Zhang
{"title":"Research on the seismic signal denoising with the LMD and EMD method","authors":"J. Yu, Ze Zhang","doi":"10.1109/IAEAC.2017.8054119","DOIUrl":null,"url":null,"abstract":"In the paper, the LMD (Local mean Decomposition) and EMD(Empirical Mode Decomposition) method are selected to denoise the sensible earthquake signal, the paper analyzes resulting conclusions and compares the denoising performance of the two methods. Experimental results show that the LMD and EMD can both achieve capabilities for denoising signals self-adaptively and improve the quality of signals with noise simultaneously. Two parameters, Correlation Coefficient (NC) and Signal to Noise Ratio(SNR), are adopted to evaluate performance of two algorithms. Corresponding data indicates that components obtained in the decomposition of the seismic signal using LMD have higher correlation degree than that using EMD, meanwhile, the filtered signal owns higher SNR value, all above of which show performance of LMD is slightly more superexcellent than that of the traditional EMD in terms of denoising for seismic signals.","PeriodicalId":432109,"journal":{"name":"2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAEAC.2017.8054119","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

In the paper, the LMD (Local mean Decomposition) and EMD(Empirical Mode Decomposition) method are selected to denoise the sensible earthquake signal, the paper analyzes resulting conclusions and compares the denoising performance of the two methods. Experimental results show that the LMD and EMD can both achieve capabilities for denoising signals self-adaptively and improve the quality of signals with noise simultaneously. Two parameters, Correlation Coefficient (NC) and Signal to Noise Ratio(SNR), are adopted to evaluate performance of two algorithms. Corresponding data indicates that components obtained in the decomposition of the seismic signal using LMD have higher correlation degree than that using EMD, meanwhile, the filtered signal owns higher SNR value, all above of which show performance of LMD is slightly more superexcellent than that of the traditional EMD in terms of denoising for seismic signals.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于LMD和EMD方法的地震信号去噪研究
本文选择LMD (Local mean Decomposition,局部均值分解)和EMD(Empirical Mode Decomposition,经验模态分解)方法对感震信号进行去噪,对得到的结论进行分析,并对两种方法的去噪性能进行比较。实验结果表明,LMD和EMD都能实现对信号的自适应去噪,同时提高含噪信号的质量。采用相关系数(NC)和信噪比(SNR)两个参数来评价两种算法的性能。相应的数据表明,利用LMD对地震信号进行分解得到的分量比利用EMD得到的分量具有更高的相关性,同时滤波后的信号具有更高的信噪比值,这些都表明LMD对地震信号的去噪性能略优于传统EMD。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A novel video detection design based on modified adaboost algorithm and HSV model Robustness analysis for rotorcraft pilot coupling with helicopter flight control system in loop Research on text categorization model based on LDA — KNN Commented content classification with deep neural network based on attention mechanism A 10bit 40MS/s SAR ADC in 0.18μm CMOS with redundancy compensation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1