Marine controlled source electromagnetic data denoising while weak signal preserving based on jointly sparse model and dictionary learning

被引：1

作者：

Zhang, Pengfei ^{[1
,2
,3
,4
]}

Pan, Xinpeng ^{[3
,4
,5
]}

Guo, Zhenwei ^{[3
,4
,5
]}

Liu, Jianxin ^{[3
,4
,5
]}

Hou, Qiuyuan ^{[6
]}

机构：

[1] Harbin Engn Univ, Qingdao Innovat & Dev Ctr, Qingdao 266400, Peoples R China

[2] Harbin Engn Univ, Harbin 150001, Peoples R China

[3] Cent South Univ, Key Lab Metallogen Predict Nonferrous Met & Geol E, Minist Educ, Changsha, Peoples R China

[4] Key Lab Nonferrous Resources & Geol Hazard Detect, Changsha, Peoples R China

[5] Cent South Univ, Changsha 410083, Peoples R China

[6] China Natl Logging Corp, Beijing 10083, Peoples R China

来源：

JOURNAL OF APPLIED GEOPHYSICS | 2023年 / 215卷

基金：

中国国家自然科学基金;

关键词：

Marine CSEM; Denoising; Jointly sparse model; Weak signal preserving; Dictionary learning; K-SVD; MULTICOMPONENT;

D O I：

10.1016/j.jappgeo.2023.105122

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

The amplitude of the marine controlled source electromagnetic (CSEM) signal decays dramatically with the augment of offset. The signal is easily contaminated by surrounding noise when the transmitter-receiver offset is large. Denoising is crucial for marine CSEM data processing and interpretation. Nowadays, most denoising methods focus on suppressing the noise influences for a single component. The inherent relations among different electromagnetic (EM) components are neglected. Besides, some weak signals are removed mistakenly to get a better denoising effect. In this paper, a new denoising method is proposed based on a jointly sparse model and dictionary learning, which utilizes the correlations among multi-components of marine CSEM data. Synthetic experiments prove that it can not only effectively remove noise, but also successfully protect weak signals. Field data application further validates the effectiveness of the proposed method.

引用

页数：11