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同时震源数据的加权结构增强最小二乘逆时偏移 被引量:8

Weighted structure-enhancing constrained least-squares reverse time migration of simultaneous-source data
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摘要 同时震源(Simultaneous-source,SS)地震采集技术能有效地提高地震数据采集效率,但直接对SS混合数据偏移成像会在最后的成像剖面中引入很强的串扰噪声.将SS数据偏移成像看作一个反演问题,利用最小二乘(Least-squares,LS)求解是压制SS直接成像中串扰噪声的一种有效尝试.构造增强滤波(Structure-enhancing filter,SE)约束的最小二乘逆时偏移(LSRTM)方法可以有效地压制SS数据成像中的串扰噪声,但SE实质为低通滤波,会将成像中的陡倾角等细节信息平滑涂抹,降低成像分辨率.本文在利用SE对LSRTM约束的基础上,提出了基于加权构造增强约束的LSRTM方法(WSE-LSRTM)并应用于SS数据的反演成像中.该方法不仅能够有效地压制串扰噪声(cross-talk)、保留结构信息,而且可以保护成像中的陡倾角结构不被过度平滑而破坏.在对简单模型和复杂Marmousi模型的数值测试中,该方法都取得了良好的效果. The simultaneous-source(SS)acquisition can significantly improve the efficiency of seismic survey.Direct imaging of SS blended data would introduce strong cross-talk noise in the final image.To gain a noise-free image,we consider the migration of blended data as a least-squares inversion problem.Structure-enhancing(SE)constrained least-squares reverse time migration(LSRTM)can efficiently suppress cross-talk in the direct image of SS data.The SE operator is actually a low-pass filter,which would smooth and smear the steep dip structure and reduce the resolution of image.In this study,we propose a weighted structure-enhancing operator to attenuate cross-talk artifacts during least-squares inversion.To preserve the steeping discontinuities in the seismic image,we apply a weight operator during each iteration.Tests using synthetic model and the Marmousi model showed the superior performance of the proposed method in preserving the steeping dip discontinuities of seismic images.
作者 张攀 毛伟建 ZHANG Pan;MAO WeiJian(Center for Computational and Exploration Geophysics,Institute of Geodesy and Geophysics,Chinese Academy of Sciences;State Key Laboratory of Geodesy and Earth′s Dynamics,Wuhan 430077,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处 《地球物理学报》 SCIE EI CAS CSCD 北大核心 2018年第10期4088-4099,共12页 Chinese Journal of Geophysics
基金 国家重点研发计划(2016YFC0601100) 国家自然科学基金重点项目(U1562216) 中国石油集团公司"深层与非常规物探新方法新技术"项目(2016A-33)联合资助
关键词 同时震源 加权构造增强 最小二乘逆时偏移 Simultaneous-source Weighted structure-enhancing LSRTM
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