Smooth model of blasting seismic wave signal denoising based on two-stage denoising algorithm

IF 1.5 Q3 GEOSCIENCES, MULTIDISCIPLINARY Geosystem Engineering Pub Date : 2020-06-16 DOI:10.1080/12269328.2020.1778543
Miao Sun, Li Wu, Chunjun Li, Qing Yuan, Yuchun Zhou, Xu Ouyang
{"title":"Smooth model of blasting seismic wave signal denoising based on two-stage denoising algorithm","authors":"Miao Sun, Li Wu, Chunjun Li, Qing Yuan, Yuchun Zhou, Xu Ouyang","doi":"10.1080/12269328.2020.1778543","DOIUrl":null,"url":null,"abstract":"ABSTRACT In this paper, a two-stage denoising algorithm is proposed. Complementary ensemble empirical mode decomposition based on permutation entropy (CEEMD-PE) is carried out for the noisy monitoring signal in the first stage. Several denoising models are established according to the intrinsic mode function obtained by CEEMD-PE. An objective function considers both the smoothness of the denoising model and the similarity between the denoising model and the noisy monitoring signal is established, and the second stage denoising is realized by solving the objective function. The denoising model corresponding to the optimal solution of the objective function is the smooth denoising model. In order to verify the correctness of the two-stage denoising algorithm, the mixed simulation signal with noise is denoised, and based on the definition of signal-to-noise ratio, the effect of two-stage denoising is calculated. Finally, the algorithm is applied to the actual blasting seismic signal denoising processing. It is found that the proposed algorithm can not only reduce the noise interference but also retain the real part of the original signal while filtering the noise.","PeriodicalId":12714,"journal":{"name":"Geosystem Engineering","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2020-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/12269328.2020.1778543","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geosystem Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/12269328.2020.1778543","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 3

Abstract

ABSTRACT In this paper, a two-stage denoising algorithm is proposed. Complementary ensemble empirical mode decomposition based on permutation entropy (CEEMD-PE) is carried out for the noisy monitoring signal in the first stage. Several denoising models are established according to the intrinsic mode function obtained by CEEMD-PE. An objective function considers both the smoothness of the denoising model and the similarity between the denoising model and the noisy monitoring signal is established, and the second stage denoising is realized by solving the objective function. The denoising model corresponding to the optimal solution of the objective function is the smooth denoising model. In order to verify the correctness of the two-stage denoising algorithm, the mixed simulation signal with noise is denoised, and based on the definition of signal-to-noise ratio, the effect of two-stage denoising is calculated. Finally, the algorithm is applied to the actual blasting seismic signal denoising processing. It is found that the proposed algorithm can not only reduce the noise interference but also retain the real part of the original signal while filtering the noise.
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
基于两阶段去噪算法的爆破地震波信号平滑去噪模型
本文提出了一种两阶段去噪算法。对第一阶段的噪声监测信号进行基于置换熵的互补系综经验模态分解(CEEMD-PE)。根据CEEMD-PE得到的内禀模态函数,建立了几种去噪模型。建立兼顾去噪模型平滑性和去噪模型与噪声监测信号相似度的目标函数,通过求解目标函数实现第二阶段去噪。目标函数最优解对应的去噪模型为平滑去噪模型。为了验证两阶段去噪算法的正确性,对带有噪声的混合仿真信号进行去噪,并根据信噪比的定义,计算两阶段去噪的效果。最后,将该算法应用于实际爆破地震信号的去噪处理。实验结果表明,该算法在对噪声进行滤波的同时,能够有效地降低噪声干扰,并保留了原始信号的实部。
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
Geosystem Engineering
Geosystem Engineering GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
2.70
自引率
0.00%
发文量
11
期刊最新文献
Novel approaches in geomechanical parameter estimation using machine learning methods and conventional well logs Correlating shale geochemistry with metal sorption: influence of kaolinite content Effect of activator type and Pozzocrete waste on the mechanical and microstructural properties of eco-friendly geopolymer incorporating electric arc furnace slag Study on tubing string safety during perforation detonation in ultra-deep wells The prediction of recovery percent of water-free stage and its application in the correction of theoretical water cut
×
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