Journal of Jilin University(Earth Science Edition) ›› 2017, Vol. 47 ›› Issue (6): 1865-1874.doi: 10.13278/j.cnki.jjuese.201706303

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Multiple Prediction with 3D Sparse Inversion and Curvelet Match

Wang Tong, Wang Deli, Feng Fei, Cheng Hao, Wei Jingxuan, Tian Mi   

  1. College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China
  • Received:2017-03-31 Online:2017-11-26 Published:2017-11-26
  • Supported by:
    Supported by Major Projects of the National Science and Technology of China (2016ZX05026-002-003) and National Natural Science Foundation of China (41374108)

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Abstract: The traditional study of surface-related multiple elimination (SRME) is based on two-dimensional algorithm to confine the contribution of multiple waves, which is too constrained to deal with a complex geological structure. The 3D SRME algorithm can hardly be promoted and applied because of its clumsy algorithm and harsh demand of data. The multiple prediction with 3D sparse inversion takes advantage of Cauchy probability density function totransform the multiple waves' energy contribution gathers to model spacetoreconstruct the event energy vertex-Fresnel zone so as to calculate and predict multiple waves' energy effectively and accurately. The Curvelet match method is used to remove multiples from seismic data withretainingmany more directions and scales more effectively compared to the traditional least square matching phase subtraction. In comparison withthe result of 2D SRME, the effectiveness of this method is verified through establishing a 3D inclined layer velocity model and testingit on simulated 3d seismic data.

Key words: surface related multiple, surface-related multiple elimination (SRME), multiple contribution, sparse inversion, curvelet match

CLC Number: 

  • P631.4
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