空间结构正则化多道稀疏脉冲反褶积  

Spatially structured regularization multichannel sparse pulse deconvolution

作　　者：汤国松[1] 李皓 梁兵[3] 夏连军[1] 鲍伟[1] 李红彩[1] 罗军梅 TANG Guosong;LI Hao;LIANG Bing;XIA Lianjun;BAO Wei;LI Hongcai;LUO Junmei(Geophysical Prospecting Research lnstitute of Jiangsu Oilfield Company,Sinopec,Nanjing 210046,China;Yangtze Delta Region Institute,University of Electronic Science and Technology of China,Huzhou 313001,China;Jiangsu Oilfield Company,Sinopec,Yangzhou 225009,China)

机构地区：[1]中国石油化工股份有限公司江苏油田分公司物探研究院,南京210046 [2]电子科技大学长三角研究院(湖州),湖州313001 [3]中国石油化工股份有限公司江苏油田分公司,扬州225009

出　　处：《石油科学通报》2024年第6期911-920,共10页Petroleum Science Bulletin

基　　金：国家重点研发计划“多元信息深度融合的高分辨率处理方法研究”(2018YFS0702504);中石化研究项目“苏北盆地岩性油藏地震资料保真处理与储层关键技术”(P22162)联合资助。

摘　　要：稀疏脉冲反褶积,有时也称为稀疏脉冲反演,是一种非线性高分辨率处理方法。常规脉冲反褶积方法假设反射系数序列满足高斯分布,其反褶积处理属于线性滤波过程。不同于常规脉冲反褶积方法,稀疏脉冲反褶积假设射系数序列满足稀疏分布,在稀疏函数正则化条件下对反射系数序列进行反演,其反褶积处理属于非线性反演过程。稀疏脉冲反褶积较常规脉冲反褶积能够更大幅度地提高地震数据分辨率,但其高频分量具有更强的多解性和不稳定性。为此,本文提出了一种空间结构正则化多道稀疏脉冲反褶积方法。首先,该方法基于地震信号在空间上的连续性和可预测性,采用结构张量对地震信号的空间结构进行估算和表征。然后,沿倾角方向设计预测误差滤波器,该滤波器保证地震信号具有最小预测误差。在此基础上,将预测误差滤波器作为空间结构约束引入到稀疏脉冲反褶积的正则化条件,建立稀疏结构和空间结构联合约束的多道稀疏脉冲反褶积目标函数。最后,采用迭代重加权算法对目标函数进行数值求解,得到反射系数序列。我们分别采用模型数据和实际数据就本文方法与常规方法进行了对比分析,并利用测井合成地震记录验证了本文方法的可靠性。模型数据和实际数据的测试结果表明,本文方法较好地抑制了随机噪声对反褶积结果的影响,增强了高频地震信号恢复精度。Sparse spike deconvolution,sometimes referred to as sparse spike inversion,is a nonlinear high-resolution processing method.Conventional pulse deconvolution assumes that the reflection coefficient series follows a Gaussian distribution,making its deconvolution process linear.In contrast,sparse spike deconvolution assumes that the reflection coefficient series follows a sparse distribution and performs inversion under the sparse function regularization,making the deconvolution process nonlinear.Sparse spike deconvolution can significantly improve the resolution of seismic data compared to conventional methods;however,its high-frequency components exhibit stronger multiple solutions and instability.To address this,this paper proposes a spatial structure regularized multichannel sparse spike deconvolution method.First,based on the spatial continuity and predictability of seismic signals,the method estimates and characterizes the spatial structure of the seismic signal using structure tensors.Then,a prediction error filter is designed along the dip direction,ensuring that the seismic signal has minimal prediction error.Building on this,the prediction error filter is introduced as a spatial structure constraint into the regularization conditions of sparse spike deconvolution,establishing a multichannel sparse spike deconvolution objective function with the sparse and spatial structure constraints.Finally,an iterative reweighting algorithm is employed to numerically solve the objective function and obtain the reflection coefficient series.We compare and analyze the proposed method against conventional methods using both model data and actual data,and we validate the reliability of this method through synthetic seismic records based on well logs.The results based on model data and actual data indicate that the proposed method effectively suppresses the influence of random noise on the deconvolution results and enhances the accuracy of high-frequency seismic signal recovery.

关 键 词：稀疏结构 空间结构 反褶积 分辨率 正则化 

分 类 号：P631.4[天文地球—地质矿产勘探] P618.13[天文地球—地质学]