Abstract:The vibroseis blending acquisition improves greatly acquisition efficiency.However,this acquisition brings serious noise in massive blended seismic data due to the continuous shooting.Therefore accurate,stable,and fast noise-separation algorithms are needed.We propose in this paper an inversion-based de-blending method applied on 3D common receiver gathers.Firstly,temporal-spatial seismic data is transformed into the frequency-wavenumber-wavenumber(FKK) domain data by 3D fast Fourier transform.Then,the hard thresholding algorithm extracts coherent signals,and noise is predicted with the extracted coherent signals.Finally,driven by data residuals,signals are iteratively updated with shrinking thresholds until full noise separation from data is achieved.Tests on both synthetic and field data show that the proposed method can separate high-productivity blended acquired data in an accurate,stable and fast way.
宋家文, 李培明, 王文闯, 王成祥, 李合群, 王宝彬. 基于稀疏反演的高效混采数据分离方法[J]. 石油地球物理勘探, 2019, 54(2): 268-273.
SONG Jiawen, LI Peiming, WANG Wenchuang, WANG Chengxiang, LI Hequn, and WANG Baobin. High-productivity blended acquired data separation by sparse inversion. Oil Geophysical Prospecting, 2019, 54(2): 268-273.
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