Reconstruction of non-uniformly sampled seismic data based on fast POCS algorithm

被引：0
|
作者：
Dong L. [1 ]
Zhang M. [1 ]
Wang C. [1 ]
An X. [1 ]
Guo Z. [1 ]
Fan H. [1 ]
机构：
[1] BGP Inc, CNPC, Hebei, Zhuozhou
来源：
Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2023年 / 58卷 / 02期
关键词：
curvelet transform; fast iterative shrinkage thresholding; fast projection on convex set; seismic data reconstruction;
D O I：
10.13810/j.cnki.issn.1000-7210.2023.02.009
中图分类号：
学科分类号：
摘要：
The projection on convex set (POCS) algorithm has been successfully applied to seismic data reconstruction, and it is flexible and simple. However, this algorithm must reconstruct data on a regular grid，and the actual collected data deviate from preset grid points due to obstacles and other factors，which results in poor reconstruction effects. Moreover，the convergence speed of the algorithm is only OC 1/k). In order to solve these challenges, this paper builds a forward model of seismic data with non-uniform grids and derives a fast POCS (FPOCS) algorithm based on curvelet transform from the fast iterative shrinkage thresholding algorithm (FIS-TA). The FPOCS is a fast seismic data reconstruction method，which retains the computational simplicity of iterative shrinkage thresholding algorithm (ISTA) and has a global convergence speed of Oil/k2). Finally, the paper demonstrates the effectiveness of the proposed method through simulation and actual data. © 2023 Science Press. All rights reserved.
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页码：334 / 339
页数：5
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