3D Block matching seismic data denoising based on Curvelet noise estimation

被引：0
|
作者：
Sun C. [1 ,2 ]
Diao J. [1 ,2 ]
Li W. [3 ]
机构：
[1] School of Geosciences, China University of Petroleum (East China), Qingdao, 266580, Shandong
[2] Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266071, Shandong
[3] Research & Development Center, BGP Inc., CNPC, Zhuozhou, 072751, Hebei
来源：
Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2019年 / 54卷 / 06期
关键词：
3D block matching (BM3D); Curvelet transform; Noise prior estimation; Seismic data denoising; Signal-noise ratio (SNR);
D O I：
10.13810/j.cnki.issn.1000-7210.2019.06.002
中图分类号：
学科分类号：
摘要：
Conventional 3D block matching (BM3D) algorithms are used for seismic data denoising. However, some parameters such as filtering threshold are difficult to be determined because of the lack of prior noise information in the practical processing. In this paper, an improved BM3D denoising method based on the Curvelet noise estimation is developed for seismic data. First the noise variance of seismic data is estimated by the Curvelet transform method. Then appropriate threshold parameters are adaptively determined. Finally the noise elimination is accurately achieved by this improved BM3D algorithm. Based on model and real data tests, the proposed algorithm can better eliminate random noise and protect signals than the conventional BM3D algorithm and Curvelet transform algorithm. Furthermore the proposed algorithm maintains most detailed information of boundary reflection and its computational efficiency is relatively high. © 2019, Editorial Department OIL GEOPHYSICAL PROSPECTING. All right reserved.
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页码：1188 / 1194
页数：6
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