岩性油气藏 ›› 2021, Vol. 33 ›› Issue (4): 93–100.doi: 10.12108/yxyqc.20210410

• 勘探技术 • 上一篇    下一篇

基于独立分量分析的地震信号盲源分离及应用

孟会杰, 苏勤, 曾华会, 徐兴荣, 刘桓, 张小美   

  1. 中国石油勘探开发研究院 西北分院, 兰州 730020
  • 收稿日期:2020-12-25 修回日期:2021-03-05 出版日期:2021-08-01 发布日期:2021-08-06
  • 第一作者:孟会杰(1991-),男,硕士,工程师,主要从事地震资料处理及地震资料处理方法研究方面的工作。地址:(730020)甘肃省兰州市城关区雁儿湾路535号。Email:menghj@petrochina.com.cn。
  • 基金资助:
    中国石油天然气集团公司勘探与生产分公司项目“利用地震面波特征估算近地表速度及Q值方法研究”（编号：2020-53007071-000004）资助

Blind source separation of seismic signals based on ICA algorithm and its application

MENG Huijie, SU Qin, ZENG Huahui, XU Xingrong, LIU Huan, ZHANG Xiaomei   

  1. PetroChina Research Institute of Petroleum Exploration & Development-Northwest, Lanzhou 730020, China
  • Received:2020-12-25 Revised:2021-03-05 Online:2021-08-01 Published:2021-08-06

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摘要： 受采集条件及野外环境影响，实际地震资料通常包含严重的噪声，严重影响成像质量。因此，寻找合适的去噪方法来提高资料信噪比至关重要。随盲源信号分离理论发展而来的独立分量分析（ICA）算法以高阶统计理论分析为基础，根据地震有效信号和随机噪声统计独立的特征，可实现信噪分离，但该方法通常要求观测信号数大于源信号数。基于此，提出一种相空间重构与独立分量分析相结合的地震信号单通道盲源分离算法，对地震资料进行去噪处理。该方法利用相空间重构技术，将一维信号重构成多维信号，根据重构相空间中有效信号和随机噪声的几何特征差异，并利用ICA算法结合数据本身的高阶统计特性，可以有效分离噪声和有效信号，提高地震资料信噪比。

关键词: 相空间, 重构, 独立分量分析, 去噪, 盲源分离

Abstract: Affected by the acquisition conditions and field environment,the actual seismic data usually contains severe noise,which seriously affects the imaging quality. Therefore,it is significant to find a suitable denoising method to improve the S/N ratio of the data and thus improve the accuracy of imaging. Independent component analysis（ICA）algorithm developed with blind source separation theory is based on higher-order statistical theoretical analysis,which can achieve the purpose of separating the signal and noise by combining the independent statistics features of effective signal and noise. However,this method requires the number of the observation signals is more than source signals. Based on this,a blind sources separation algorithm of seismic signal was proposed to process the seismic data. By phase space reconstruction（PSR）,the single-channel signal was reconstructed into a multi-dimensional phase space,and the dynamic characteristics of the reconstructed phase space is consistent with the original signal. Then,in the reconstructed phase space,based on the difference of the geometric characteristics and combining the high-order statistical characteristics of the data,the noise and effective signal can be effectively separated by ICA algorithm,and achieve the purpose of improving the signal-to-noise ratio of seismic data.

Key words: phase space, reconstruction, independent component analysis, denoising, blind sources separation

中图分类号: 

  • P631.4
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