Lithologic Reservoirs ›› 2021, Vol. 33 ›› Issue (4): 93-100.doi: 10.12108/yxyqc.20210410

• EXPLORATION TECHNOLOGY • Previous Articles     Next Articles

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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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

CLC Number: 

  • P631.4
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