Compressed sensing seismic data reconstruction based on maximum correntropy criterion

被引：0

作者：

Jiao X. ^{[1
]}

Du Q. ^{[2
,3
,4
]}

Zhao Q. ^{[2
,3
,4
]}

机构：

[1] Geophysical Research and Development Institute, Geophysical-COSL, Tianjin

[2] Key Laboratory of Deep Oil and Gas in China University of Petroleum (East China), Qingdao

[3] Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao

[4] Key Laboratory of Geophysical Prospecting, CNPC, China University of Petroleum (East China), Qingdao

来源：

Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science) | 2020年 / 44卷 / 03期

关键词：

Erratic noise attenuation; Maximum correntropy; Robust misfit metric; Seismic data reconstruction;

D O I：

10.3969/j.issn.1673-5005.2020.03.004

中图分类号：

学科分类号：

摘要：

For robust reconstruction of under-sampled and noisy data, the maximum correntropy criterion (MCC), which is widely used in the field of information theoretic learning, is introduced to measure the distance between the observed data and the recovered signal, reducing the influence of erratic noise on misfit estimation. With the help of MCC-based reconstruction model, an iterative shrinkage algorithm is applied to solve the nonlinear problem and robust reconstruction in the presence of erratic noise is achieved. Several seismic examples show that erratic noise present in the under-sampled data indeed influences the reconstruction accuracy with conventional compressive sensing methods, whereas the proposed MCC-based method can effectively attenuate the influence of erratic noise and provide superior accuracy and stability. © 2020, Editorial Office of Journal of China University of Petroleum(Edition of Natural Science). All right reserved.

引用

页码：38 / 46

页数：8

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