Journal of Jilin University(Earth Science Edition) ›› 2020, Vol. 50 ›› Issue (3): 895-904.doi: 10.13278/j.cnki.jjuese.20190147

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Separation and Primary Estimation of Blended Data by 3D Sparse Inversion

Wang Tiexing, Wang Deli, Sun Jing, Hu bin, Liu Sixiu   

  1. College of GeoExploration Sicence and Technology, Jilin University, Changchun 130026, China
  • Received:2019-07-25 Published:2020-05-29
  • Supported by:
    Supported by National Nature Science Foundation of China(2016ZX05026-002-003)and National Natural Science Foundation of China(41374108)

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Abstract: The blended acquisition of seismic data is widely used in the industry area; however, the seismic data acquired by such a method contain overlapping shot records of multiple sources, which is not conducive to the subsequent seismic data processing. A modified separation and primary estimation method for blended data based on 3D sparse inversion is proposed in this paper. We introduce the L1 norm bi-convex optimization into the solution process of estimating primary impulse responses by conventional EPSI and SPGL1 algorithm to get the global minima, so that the inversion process is stable. Besides, 2D curvelet transform and 1D wavelet transform are combined into a 3D sparse constraint to improve the calculation speed while ensuring the inversion accuracy. Compared to the conventional EPSI for blended data in the standard industry workflow, the effectiveness and superiority of this proposed method is verified in the application in synthetic data and marine field data.

Key words: separation of blended data, primary estimation, 3D sparse inversion

CLC Number: 

  • P631.4
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