利用经验模态分解及小波变换压制微震信号中的随机噪声
TD311%TN911.7; 随机噪声的压制是微震信号分析过程中的重要环节,目前大多数降噪技术都不同程度的存在去噪效果差、易损伤有效信号等问题.针对微震信号的随机非平稳特征,提出一种联合经验模态分解(EMD)及小波阈值的降噪方法,压制微震信号中的随机噪声.该法首先使用EMD对微震信号进行自适应分解,得到有限个本征模态函数(IMF).考虑到随机噪声主要集中在高频IMF分量中,基于噪声能量突变原则找出低频IMF分量与高频IMF分量的分界后,利用小波阈值方法对高频IMF进行降噪处理,最后将降噪后的高频IMF分量与剩余的低频IMF分量重构即可实现微震信号降噪.仿真分析及实验结果表明,该方法能充分保留微...
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| Published in | 煤炭学报 Vol. 43; no. 11; pp. 3247 - 3256 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
山东科技大学山东省智慧矿山信息技术省级重点实验室,山东青岛266590
01.11.2018
山东科技大学计算机科学与工程学院,山东青岛266590 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0253-9993 |
| DOI | 10.13225/j.cnki.jccs.2017.1667 |
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| Abstract | TD311%TN911.7; 随机噪声的压制是微震信号分析过程中的重要环节,目前大多数降噪技术都不同程度的存在去噪效果差、易损伤有效信号等问题.针对微震信号的随机非平稳特征,提出一种联合经验模态分解(EMD)及小波阈值的降噪方法,压制微震信号中的随机噪声.该法首先使用EMD对微震信号进行自适应分解,得到有限个本征模态函数(IMF).考虑到随机噪声主要集中在高频IMF分量中,基于噪声能量突变原则找出低频IMF分量与高频IMF分量的分界后,利用小波阈值方法对高频IMF进行降噪处理,最后将降噪后的高频IMF分量与剩余的低频IMF分量重构即可实现微震信号降噪.仿真分析及实验结果表明,该方法能充分保留微震信号的随机非平稳特征,较对比方法具有更好的降噪效果. |
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| AbstractList | TD311%TN911.7; 随机噪声的压制是微震信号分析过程中的重要环节,目前大多数降噪技术都不同程度的存在去噪效果差、易损伤有效信号等问题.针对微震信号的随机非平稳特征,提出一种联合经验模态分解(EMD)及小波阈值的降噪方法,压制微震信号中的随机噪声.该法首先使用EMD对微震信号进行自适应分解,得到有限个本征模态函数(IMF).考虑到随机噪声主要集中在高频IMF分量中,基于噪声能量突变原则找出低频IMF分量与高频IMF分量的分界后,利用小波阈值方法对高频IMF进行降噪处理,最后将降噪后的高频IMF分量与剩余的低频IMF分量重构即可实现微震信号降噪.仿真分析及实验结果表明,该方法能充分保留微震信号的随机非平稳特征,较对比方法具有更好的降噪效果. |
| Author | 彭延军 张杏莉 赵卫东 卢新明 宫月 贾瑞生 |
| AuthorAffiliation | 山东科技大学计算机科学与工程学院,山东青岛266590;山东科技大学山东省智慧矿山信息技术省级重点实验室,山东青岛266590 |
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| Author_FL | GONG Yue JIA Ruisheng LU Xinming ZHAO Weidong ZHANG Xingli PENG Yanjun |
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| DocumentTitle_FL | To suppress the random noise in microseismic signal by using empirical mode decomposition and wavelet transform |
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| Keywords | 微震信号 经验模态分解 小波阈值 降噪技术 |
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| Publisher | 山东科技大学山东省智慧矿山信息技术省级重点实验室,山东青岛266590 山东科技大学计算机科学与工程学院,山东青岛266590 |
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| Title | 利用经验模态分解及小波变换压制微震信号中的随机噪声 |
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