Seismic random noise suppression based on scale-adaptive 3D-Shearlet transform

被引：0
|
作者：
Cheng H. [1 ]
Wang D. [2 ]
Wang E. [1 ]
Fu J. [1 ]
Hou Z. [1 ]
机构：
[1] Key Laboratory of Safe Mining of Deep Metal Mines, Ministry of Education, Northeastern University, Shenyang, 110819, Liaoning
[2] College of Geo-Exploration Sciences and Technology, Jilin University, Changchun, 130026, Jilin
来源：
Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2019年 / 54卷 / 05期
关键词：
3D-Shearlet transform; Random noise; Scale-adaptive factor; Signal-to-noise ratio (SNR); Sparsity;
D O I：
10.13810/j.cnki.issn.1000-7210.2019.05.004
中图分类号：
学科分类号：
摘要：
We use a scale-adaptive 3D-Shearlet transform to suppress random noise of multi-source seismic data.First multi-source seismic data is transformed into the 3D-Shearlet domain.In this domain, seismic data can be represented more sparsely.Since seismic signals distribute in a low scale and random noise distributes in the all scales, scale-adaptive factors suppress random noise based on hard thresholds.With the inverse 3D-Shearlet transform, the de-noising seismic data can be obtained.According to numerical and seismic data tests, the proposed approach can better suppress random noise than the 2D-Shearelet transform and the 3D-Shearlet transform; however it needs a larger computer memory, and may damage weak signals. © 2019, Editorial Department OIL GEOPHYSICAL PROSPECTING. All right reserved.
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页码：970 / 978
页数：8
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