吉林大学学报(地球科学版)

• 地球探测与信息技术 • 上一篇    下一篇

α稳定分布地震信号特征指数估计方法

岳碧波1,彭真明1, 张启衡2   

  1. 1.电子科技大学光电信息学院,成都610054;
    2.中国科学院光电技术研究所,成都610209
  • 收稿日期:2012-12-06 出版日期:2013-11-26 发布日期:2013-11-26
  • 通讯作者: 彭真明(1966-),男,教授,博士生导师,主要从事油气预测及信号处理应用研究 E-mail:zmpeng@uestc.edu.cn
  • 作者简介:岳碧波(1984-),男,博士研究生,主要从事信号与信息处理、地震反演与储层预测研究,E-mail:yue_bibo@163.com
  • 基金资助:

    国家自然科学基金项目(41274127,40874066,40839905);中央高校基本科研业务费专项资金(ZYGX2011YB021)

α-Stable Distribution Seismic Signal Characteristic Exponent Estimation

Yue Bibo1, Peng Zhenming1, Zhang Qiheng2   

  1. 1.School of Opto-Electronic Information, University of Electronic Science and Technology of China, Chengdu610054, China;
    2.Institute of Optics and Electronics, China Academy of Sciences, Chengdu610209,China
  • Received:2012-12-06 Online:2013-11-26 Published:2013-11-26

摘要:

地震信号通常都被认为是服从高斯分布的。研究了实际地震信号的动态样本方差特征,假定地震信号是服从非高斯α稳定分布的。对于非高斯α稳定分布地震信号,其特征指数的取值大小表征了信号的脉冲强度,对地震信号去噪、阻抗反演等处理算法范数的选取起到了重要作用;当算法选取的范数大于特征指数时,将有可能得不到预期的结果。采用基于分数低阶统计量的α稳定分布特征指数计算方法,并结合粒子群优化算法,估计出了实际地震数据的特征指数最大为1.930 1。研究表明,实际地震数据的脉冲性强于高斯分布,其特征指数小于高斯分布的特征指数2。因此,将实际地震数据的统计分布假设为非高斯α稳定分布比假设为高斯分布更合理。

关键词: 非高斯, &alpha, 稳定分布, 特征指数, 信号处理

Abstract:

It was assumed that seismic signals follow Gaussian distribution. After analyzing the dynamic sample variance of real seismic data, the authors proposed that seismic data obeys nonGaussian α-stable distribution. In the applications of non-Gaussian α-stable distribution seismic signal processing, such as noise suppressing and seismic impedance inversion, characteristic exponent is a key parameter; And if the norm greater than characteristic exponent,  misleading results will be produced. Combining with particle swarm optimization algorithm,  α-stable distribution characteristic exponent estimation method based on fractional lower order moments was applied to real seismic data and the characteristic exponent of real seismic data was obtained. The work of this paper shows that the pulse characteristic in real seismic data are stronger than that in Gaussian distribution; The proposition that real seismic data follow non-Gaussian α-stable distribution is reasonable; The calculation of real seismic data character exponent is also helpful to the selection of seismic signal processing algorithm.

Key words: non-Gaussian, α-stable distribution, characteristic exponent, signal processing

中图分类号: 

  • P631.4
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