期刊文献+

基于奇异值分解的强单频干扰自动识别与消除 被引量:1

Automatic Identification and Elimination of Strong Single-frequency Interference Based on SVD
下载PDF
导出
摘要 单频干扰常常会将有效信号掩盖,严重影响了地震勘探数据的处理、解释。为了提高单频干扰压制的效果,研究了一种自动识别及消除单频干扰的新技术。该技术可在频率域快速识别出强单频干扰,利用奇异值分解(SVD)的特性,并用含有单频干扰的地震道构建Hankel矩阵,利用Hankel矩阵的奇异值分布与信号振幅谱之间的对应关系,通过奇异值分解和重构,可有效压制地震道中的单频干扰。模型数据和实际资料测试表明:该方法对强单频干扰具有较好的识别和压制效果,并且对有效信号伤害较小。 Single-frequency interference often covers desired signals,influencing seriously the processing and interpretation of seismic exploration data.The research has been made on a new technology which can automatically identify and eliminate single-frequency interference to improve the effect of such interference suppression.This technology can identify quickly strong single-frequency interference in the frequency domain and can build Hankel matrix based on SVD(singular value decomposition)character and seismic trace with single-frequency interference which can be suppressed effectively by using correspondent relationship between singular value distribution in Hankel matrix and signal amplitude spectrum and through singular value decomposition and reconstruction.Model data and actual data testing show that this technology has a better effect in identification and suppression of strong single-frequency interference and has a less harm on desired signals.
作者 郭利荣 GUO Lirong(Geophysical Prospecting Research Institute of Jianghan Oilfield Company,SINOPEC,Wuhan,Hubei,430045China)
出处 《江汉石油职工大学学报》 2019年第4期11-13,30,共4页 Journal of Jianghan Petroleum University of Staff and Workers
关键词 单频干扰 HANKEL矩阵 奇异值分解 Single-frequency Interference Hankel Matrix SVD
  • 相关文献

参考文献5

二级参考文献30

  • 1刘洋.强工频干扰波的提取与消除方法[J].石油物探,2003,42(2):154-159. 被引量:31
  • 2高少武,周兴元,蔡加铭.时间域单频干扰波的压制[J].石油地球物理勘探,2001,36(1):51-55. 被引量:18
  • 3陈亚光,杨仲乐,陈心浩.诱发脑电信号中工频噪声及其谐波成分的去除[J].中南民族学院学报(自然科学版),1997,16(1):14-17. 被引量:2
  • 4陈宝林.最优化理论与算法[M].北京:清华大学出版社,1994.95. 被引量:2
  • 5Kehoe M W. A Historical overview of flight flutter testing. NASA TM-4720, October 1995. 被引量:1
  • 6Sanliturk K Y, Caker O. Noise elimination from measured frequeney response function [J]. IEEE Mechanical System and signal Processing, 2005,19 : 615 - 631. 被引量:1
  • 7Gialamas T P, Tsahalis D T. Otte D, et al. Substructuring Technique: Improvement bv means of Singular Value Decomposition (SVD) [J]. Applied Acoustics, 2001,62, 1211 -1219. 被引量:1
  • 8Maia Nuno M M. Fundamentals of Singular Value Decomposition[J]. 1991 Proceedings of the 9th International Modal Analysis Conference, 1515 - 1521. 被引量:1
  • 9Moor B D. The singular value decomposition and long and short spaces of noisy matrices [ J ]. IEEE Trans. Signal Processing, 1993,41 (9) :2826 - 2838. 被引量:1
  • 10Ephraim Y, Van Trees H L. A signal subspace approach for speech enhancement[ J]. IEEE Trans. Speech Audio Processing, 1995,3(4) :251 -266. 被引量:1

共引文献80

同被引文献9

引证文献1

二级引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部