石油物探2025，Vol.64Issue(5)：854-863,10.DOI:10.12431/issn.1000-1441.2024.0057
基于平滑流式预测误差滤波的多道反褶积方法
Multichannel deconvolution based on smooth streaming prediction error filter
摘要
Abstract
Deconvolution is an effective method for improving seismic resolution of imaging and reservoir prediction.Traditional deconvolution is usually implemented through trace-by-trace inversion under stationary conditions,and improved resolution cannot offset poor spatial continuity owing to the lack of spatial constraints.This paper proposes a multichannel deconvolution method based on a streaming prediction error filter,which uses temporal and spatial constraints to achieve multichannel adaptive deconvolution and improve the spatial continuity of non-stationary seismic data after deconvolution.A smoothing matrix is employed so as not to blur boundaries and geological structures,particularly complex structures.The new deconvolution method can effectively improve the vertical resolution of seismic data and reduce the workload through streaming computation,making it suitable for non-stationary big data.The processing results of synthetic data show that spatial constraints improve the spatial continuity of deconvolution,and a field data test verifies the effectiveness and practicality of this method.关键词
多道反褶积/流式预测误差滤波/空间连续性/平滑矩阵/非平稳地震数据Key words
multichannel deconvolution/streaming prediction error filter/spatial continuity/smoothing matrix/non-stationary seismic data分类
能源科技引用本文复制引用
秦宁,李凌云,田坤,李傲伟,孙小东,赵亮..基于平滑流式预测误差滤波的多道反褶积方法[J].石油物探,2025,64(5):854-863,10.基金项目
油气重大专项(2024ZD1400100)、山东省泰山产业领军人才课题(No.tscx202312059)、中央高校基本科研业务费专项资金(No.24CX02010A)、中国石油大学(华东)深层油气全国重点实验室和山东省自然科学基金面上项目(ZR2020MD048)共同资助. This research is financially supported by the Oil&Gas Major Project(Grant No.2024ZD1400100),Shandong Provincial Taishan Industry Leading Talent Program(Grant No.tscx202312059),Fundamental Research Funds for the Central Universities(Grant No.24CX02010A),the Fund of State Key Laboratory of Deep Oil and Gas China University of Petroleum(East China)and Shandong Natural Science Foundation of China(Grant No.ZR2020MD048). （2024ZD1400100）
