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基于数据规则化和稀疏反演的三维表面多次波压制方法
引用本文:方云峰,聂红梅,张丽梅,唐博文,王增波.基于数据规则化和稀疏反演的三维表面多次波压制方法[J].地球物理学报,2016,59(2):673-681.
作者姓名:方云峰  聂红梅  张丽梅  唐博文  王增波
作者单位:1. 中国海洋大学, 青岛 266100;2. 中国石油东方地球物理公司物探技术研究中心, 河北涿州 072751
基金项目:国家科技重大专项项目(2011ZX05019-003)资助.
摘    要:表面多次波是海洋地震勘探中的主要问题.目前,二维数据驱动的表面多次波压制技术(SRME)已经比较成熟,并且已经成为工业界压制海洋表面多次波的主流方法.但是由于二维SRME算法没有考虑横测线方向上多次波的贡献,导致在处理实际三维海洋资料时存在比较大的误差.将二维SRME算法扩展到三维空间后可以得到三维SRME算法,但是由于目前实际采集的三维海洋资料的观测系统存在拖缆漂移,而且横测线方向采样过于稀疏,直接应用三维SRME算法无法准确预测表面多次波.本文提出的通过数据规则化配合稀疏反演的三维表面多次波压制方法能够解决这种实际资料和三维SRME算法之间的矛盾.本文通过研究数据规则化与反规则化技术,使得数据分布满足三维SRME的要求;通过研究稀疏反演技术,有效解决了横测线方向采样稀疏对于多次波预测的影响,三维实际海洋资料的应用结果验证了方法的有效性和可行性.

关 键 词:三维  表面多次波  数据规则化  稀疏反演  
收稿时间:2014-09-17

3D SRME based on joint regularization and sparse inversion
FANG Yun-Feng,NIE Hong-Mei,ZHANG Li-Mei,TANG Bo-Wen,WANG Zeng-Bo.3D SRME based on joint regularization and sparse inversion[J].Chinese Journal of Geophysics,2016,59(2):673-681.
Authors:FANG Yun-Feng  NIE Hong-Mei  ZHANG Li-Mei  TANG Bo-Wen  WANG Zeng-Bo
Institution:1. Ocean University of China, Qingdao 266100, China;2. R & D Center of BGP, CNPC, Heibei Zhuozhou 072751, China
Abstract:Surface related multiple is an important problem in marine seismic data processing. Now the 2D wave-theory-based surface related multiple elimination (SRME) is a primary tool to solve this issue. But there are great errors when processing 3D data by 2D SRME because it does not consider the multiple contribution of cross-line direction. The 3D SRME algorithm can be obtained by extending the 2D SRME to 3D space. However, the surface related multiple cannot be predicted by 3D SRME beacause of the streamer feathering and the large cable interval for current 3D marine acquisition. This paper presents a new 3D surface related multiple prediction method that combines regularization with sparse inversion to solve this contradiction between the field data and the 3D SRME algorithm. Using data regularization and de-regularization, this method makes the data distribution satisfy the prerequisites of 3D SRME, and using sparse inversion to eliminate the adverse effect of sparse sampling at cross-line for multiple prediction. The 3D field marine data example proves that this 3D SRME method is effective and feasible.
Keywords:3D  Surface related multiple  Data regularization  Sparse inversion
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