石油物探2018，Vol.57Issue(1)：24-27,4.DOI:10.3969/j.issn.1000-1441.2018.01.002
压缩感知走进地球物理勘探
Compressive sensing in geophysical exploration
马坚伟1
作者信息
- 1. 哈尔滨工业大学地球物理中心/数学系,黑龙江哈尔滨 150001
- 折叠
摘要
Abstract
Compressive sensing (CS)is based on random sampling and sparsity,which bypasses a limitation of the Nyquist-Shan-non sampling theorem.CS enables the reconstruction of signals from incomplete measurements significantly below the Shannon sampling rate.In this paper,we review the theory of CS and its applications in seismic data acquisition,processing,imaging,and in-version.Three key components for the application of CS are random acquisition (including random distribution of shot and detector points),sparse representation of signals,and fast algorithm for optimal reconstruction with sparse constraints.The percentage of data required for reconstructing targets decreases with increasing dimensions involved.The paper also highlights the potential of combining compressed sensing with deep learning.关键词
压缩感知/地球物理勘探/稀疏变换/随机采样Key words
compressive sensing/geophysics exploration/sparse transform/random sampling分类
天文与地球科学引用本文复制引用
马坚伟..压缩感知走进地球物理勘探[J].石油物探,2018,57(1):24-27,4.
