石油地球物理勘探2024，Vol.59Issue(3)：424-432,9.DOI:10.13810/j.cnki.issn.1000-7210.2024.03.005
应用非线性KNN数据搜索的三维叠前自由表面多次波预测
Three dimension pre-stack surface-related multiple prediction based on nonlinear KNN algorithm for data search
摘要
Abstract
Surface-related multiple prediction(SRMP)is an important part of surface-related multiple elimina-tion(SRME)and imaging.Although SRME technology is effective,it requires a regular and dense seismic data acquisition in theory.However,the spatial distribution of actual shot points and receiver points is sparse,and the seismic data fails to meet the requirements of SRME.The conventional method is to regularize seismic data before SRME.To avoiding the pre-SRME data interpolation,3D pre-stack data has been organized by a K dimensional index data tree,then a nonlinear K-nearest neighbor(KNN)algorithm comprehensively utilizes spa-tial location coordinates of sources and receivers,offset,azimuth to search from field data an approximate trace in real time,which is closest to an ideal trace.After that,a partial normal move-out correction is used to correct travel time difference because of the offset difference between the approximate trace and the ideal trace.Through the above two steps,the two seismic traces related with any downward reflection point(DRP)in the aperture of a single trace can be obtained and be used in SRMP.By convolution the two traces and stacking the results of all DRPs in the aperture of a single trace,a stable multiple model for that trace can be obtained.The method has been proven effective by testing on synthetic data of a modified 3D Pluto model and field seismic data in Northwest China.关键词
自由表面多次波/预测/消除/索引数据树/非线性K近邻(KNN)算法Key words
surface-related multiple/prediction/elimination/index data tree/nonlinear K-nearest neighbor分类
天文与地球科学引用本文复制引用
谢飞,朱成宏,高鸿,徐蔚亚..应用非线性KNN数据搜索的三维叠前自由表面多次波预测[J].石油地球物理勘探,2024,59(3):424-432,9.基金项目
本项研究受国家重点研发计划项目"高分辨率地震实时成像理论与技术"之课题"高分辨率地震成像软件系统开发及应用"(2018YFA0702505)、中国石油化工股份公司科技攻关项目"π平台层间多次波压制与成像技术研发集成"(P24124)联合资助. （2018YFA0702505）
