摘要
不规则采样地震资料的插值重建是地震资料处理过程中一个非常重要的环节。文章主要从信号处理的角度介绍了近年来提出的地震数据重建方法,主要有基于压缩感知和基于深度学习的地震数据重建。基于压缩感知的方法主要介绍了基于低秩约束和稀疏约束的地震数据重建方法。依据时间先后梳理了地震数据重建方法的特点和发展的脉络,对各种方法的优缺点展开了总结说明,探讨了地震数据重建方法下一步的研究方向。
Interpolation reconstruction of irregular sampling seismic data is a very important part of seismic data processing.This paper mainly introduces the seismic data reconstruction methods proposed in recent years from the perspective of signal processing,mainly including the seismic data reconstruction based on compressive sensing and deep learning.The method based on compressive sensing mainly introduces the seismic data reconstruction method based on low rank constraint and sparse constraint.According to time,the characteristics and development of seismic data reconstruction methods are sorted out,the advantages and disadvantages of various methods are summarized,and the next research direction of seismic data reconstruction methods is discussed.
作者
秦思
田琳
QIN Si;TIAN Lin(School of Electronic Engineering,Yili Normal University,Yining,Xinjiang,835000,China)
出处
《新疆师范大学学报（自然科学版）》
2022年第3期45-54,共10页
Journal of Xinjiang Normal University(Natural Sciences Edition)
基金
国家自然科学基金(61761043)
伊犁师范大学“学实高层次人才岗位”项目(YSXSGG22006)
伊犁师范大学博士启动基金项目(2017YSBS07)。
关键词
地震数据重建
压缩感知
深度学习
低秩约束
稀疏约束
Seismic data reconstruction
Compressed sensing
Deep learning
Low-rank constraint
Sparse constraint
