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Global Geology 2020, 23(4) 241-246 DOI: 10.3969/j.issn.1673-9736.2020.04.05 ISSN: 1673-9736 CN: 22-1371/P

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	ZHENG Jialiang
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Seismic data denoising based on data-driven tight frame dictionary learning method

ZHENG Jialiang, WANG Deli, ZHANG Liang

College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China

摘要： Because of various complicated factors in seismic data collection, the random noise of seismic data is too difficult to avoid. This random noise reduces the quality of seismic data and increases the difficulty of seismic data processing and interpretation. Improving the denoising technology is significant. In order to improve seismic data denoising result, a novel method named data-driven tight frame (DDTF) is introduced in this paper. First, we get the sparse coefficients of seismic data with noise by DDTF. Then we remove the smaller sparse coefficient by using the hard threshold function. Finally, we get the denoised seismic data by inverse transform. Furthermore, the DDTF is compared with curvelet transform in the stimulation and practical seismic data experiments to validate its performance. DDTF can raise the signal-to-noise ratio of seismic data denoising and protect the effective signal well.

关键词： DDTF dictionary hard threshold curvelet transform random noise

Seismic data denoising based on data-driven tight frame dictionary learning method

ZHENG Jialiang, WANG Deli, ZHANG Liang

College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China

Abstract: Because of various complicated factors in seismic data collection, the random noise of seismic data is too difficult to avoid. This random noise reduces the quality of seismic data and increases the difficulty of seismic data processing and interpretation. Improving the denoising technology is significant. In order to improve seismic data denoising result, a novel method named data-driven tight frame (DDTF) is introduced in this paper. First, we get the sparse coefficients of seismic data with noise by DDTF. Then we remove the smaller sparse coefficient by using the hard threshold function. Finally, we get the denoised seismic data by inverse transform. Furthermore, the DDTF is compared with curvelet transform in the stimulation and practical seismic data experiments to validate its performance. DDTF can raise the signal-to-noise ratio of seismic data denoising and protect the effective signal well.

Keywords: DDTF dictionary hard threshold curvelet transform random noise

收稿日期 2020-02-19 修回日期 2020-03-29 网络版发布日期

DOI: 10.3969/j.issn.1673-9736.2020.04.05

基金项目:

通讯作者: WANG Deli

作者简介:

作者Email: 1070021872@qq.com

参考文献：

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