Abstract:In seismic exploration,affected by the factors such as acquisition environment,technology and cost,some shots or traces can be missing in field data.The incompleteness of seismic data will have adverse effect on later seismic data processing and imaging,thus the reconstruction of these missing data is essential.In this paper,a sparse learning via iterative minimization (SLIM) method was proposed to reconstruct random missing 3D seismic data.It reconstructs 3D missing seismic data based on the 2D harmonic structure of frequency slice.Firstly,apply Fourier transform to 3D seismic data along time direction.Secondly,use cyclic minimization (CM) algorithm to solve the 2D harmonic spectrum of frequency slice iteratively.Finally,apply inverse Fourier transform to the estimated spectrum,and thus reconstruct the missing data.Besides,conjugate gradient least squares(CGLS) is applied to calculate the inverse in data reconstruction,in order to speed up the reconstruction.Test results indicate that the proposed SLIM method achieved good performance on both synthetic and real 3D seismic data,and it performed better than multi-channel singular spectrum analysis(MSSA) method using singular Hankel matrix based on frequency slice.
代志刚, 刘智慧, 王锦妍. 基于迭代最小化稀疏学习的三维地震数据重建[J]. 石油地球物理勘探, 2020, 55(1): 36-45.
DAI Zhigang, LIU Zhihui, WANG Jinyan. 3D seismic data reconstruction based on sparse lear-ning via iterative minimization. Oil Geophysical Prospecting, 2020, 55(1): 36-45.
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