Shearlet域尺度角度自适应深反射地震数据随机噪声压制方法

doi:10.11720/wtyht.2022.1187

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Abstract

Deep seismic reflection is one of the most effective means of studying the deep geological structure of the Earth.However,the energy of seismic waves exponentially decreases due to the filtering by the Earth,resulting in weak energy of effective deep seismic reflection signals.In this case,deep seismic reflection data are liable to be seriously disturbed by background noise,and thus it is difficult to obtain accurate images of deep geological structures.According to the study on the differences in the distribution of effective signals and random noise of deep seismic reflection data on different scales in the Shearlet domain,seismic signals on different scales are affected by random noise to different extents.Furthermore,with the signal-to-noise ratio,the L2 norm of Shearlet coefficients,and the residual errors of random noise in deep seismic reflection data as the parameters for threshold estimation,this study developed a random noise suppression method that is adaptive to different scales to minimize the effects of random noise.Theoretical model data and actual tests of deep seismic reflection data verified that this method can effectively eliminate the disturbance of random noise,improve the overall signal-to-noise ratio of seismic sections,and realize the accurate imaging of weak deep seismic reflection signals.

Key words： deep reflection seismic Shearlet transform adaptive threshold signal-to-noise ratio(SNR) seismic weak signal

Received: 17 April 2021 Published: 21 June 2022

ZTFLH:

P631.4

Corresponding Authors: WANG Xing-Yu E-mail: wangtong_igge@163.com;wxingyu@mail.cgs.gov.cn

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	Tong WANG
	Jian-Xun Liu
	Xing-Yu WANG
	Guang-Cai LI
	Mi TIAN

Cite this article:

Tong WANG,Jian-Xun Liu,Xing-Yu WANG, et al. Suppression of random noise in deep seismic reflection data using adaptive threshold-based Shearlet transform[J]. Geophysical and Geochemical Exploration, 2022, 46(3): 704-713.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2022.1187 OR https://www.wutanyuhuatan.com/EN/Y2022/V46/I3/704

Signal-to-noise ratio of seismic data at different scales and angles in Shearlet domain

L2 norm of seismic data at different scale angles in Shearlet domain

Sigsbee model(a) and single shot record containing random noise(b)

Shearlet domain threshold method for the removal of random noise
a—traditional Shearlet domain threshold method;b—Shearlet domain scale angle adaptive threshold method;c—random noise by traditional Shearlet domain threshold method;d—random noise by Shearlet domain scale angle adaptive threshold method;e—the 3^rd scale result of traditional Shearlet domain threshold method;f—the 4^th scale result of traditional Shearlet domain threshold method;g—the 3^rd scale result of Shearlet domain scale angle adaptive threshold method;h—the 4^th scale result of Shearlet domain scale angle adaptive threshold method

Zero-offset record containing random noise

Random noise removal effect of field seismic data in Songliao Basin
a—superimposed profile of deep reflection seismic data in Songliao Basin;b—Shearlet domain scale angle adaptive threshold denoised profiles;c—random noise

Fig.7
a—enlarge the yellow box area in figure 7a;b—enlarge the yellow box area in figure 7b;c—enlarge the blue box area in figure 7a;d—enlarge the blue box area in figure 7b
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Zoom in of Fig.7
a—enlarge the yellow box area in figure 7a;b—enlarge the yellow box area in figure 7b;c—enlarge the blue box area in figure 7a;d—enlarge the blue box area in figure 7b

Spectrum comparison of profile before and after denoising

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