三维地震数据频域无监督随机噪声压制方法

doi:10.13810/j.cnki.issn.1000-7210.2023.06.003

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (9480 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Improving the signal-to-noise ratio is a key step in seismic data processing. The current deep learning-based noise reduction methods have achieved better results. However，these methods are carried out in the temporal-spatial domain based on the local similarity of the seismic data and the processing efficiency is low. In view of the lateral continuity of geological structure，the shot gathers are very similar. Thus， an unsupervised rank-reduction denoise method in frequency domain is proposed based on the low-rank feature of the same frequency component of 3D data. The low-rank principle in frequency domain of 3D data is expounded and the singular value decomposition theory is used to guide the establishment of autoencoding network； Considering the characteristics of random noise distribution in frequency domain，K-L（Kullback-Leibler） divergence is used to constrain the loss function to improve the denoising effect. The experiments on synthetic and field data verified the advantages of the proposed method in denoising performance and computational efficiency compared with the multichannel singular spectrum analysis （MSSA） and K-SVD （K-Singular Value Decomposition） methods.

Key words： unsupervised network denoising in frequency domain singular value decomposition K-L divergence autoencoding network

Received: 01 December 2022

PACS:

P631

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	XUE Yaru
	SU Junli
	FENG Luyu
	ZHANG Cheng
	LIANG Qi

Cite this article:

XUE Yaru,SU Junli,FENG Luyu等. An unsupervised random noise suppression method in frequency domain for 3D seismic data[J]. OGP, 2023, 58(6): 1322-1331.

URL:

http://www.ogp-cn.com/EN/10.13810/j.cnki.issn.1000-7210.2023.06.003 OR http://www.ogp-cn.com/EN/Y2023/V58/I6/1322

[1]	ABMA R,CLAERBOUT J. Lateral prediction for noise attenuation by t-x and f-x techniques[J]. Geophysics,1995,60(6):1887-1896.
[2]	HARRIS P E,WHITE R E. Improving the performance of f-x prediction filtering at low signal-to-noise ratios[J]. Geophysical Prospecting,1997,45(2):269-302.
[3]	LIU G,CHEN X. Noncausal f-x-y regularized nonstationary prediction filtering for random noise attenuation on 3D seismic data[J]. Journal of Applied Geophysics,2013,93:60-66.
[4]	SACCHI M D,ULRYCH T J,WALKER C J. Interpolation and extrapolation using a high-resolution discrete Fourier transform[J]. IEEE Transactions on Signal Processing,1998,46(1):31-38.
[5]	GUO D F,ZHU W H,GAO Z M,et al. A study of wavelet thresholding denoising[C]. 5th International Conference on Signal Processing Proceedings,2000,329-332.
[6]	HAGHSHENAS LARI H,GHOLAMI A. Curvelet-TV regularized Bregman iteration for seismic random noise attenuation[J]. Journal of Applied Geophysics,2014,109:233-241.
[7]	CADZOW J A. Signal enhancement:a composite property mapping algorithm[J]. IEEE Transactions on Acoustics,Speech,and Signal Processing,1988,36(1):49-62.
[8]	ANVARI R,MOHAMMADI M,KAHOO A R. Enhancing 3-D seismic data using the t-SVD and optimal shrinkage of singular value[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2019,12(1):382-388.
[9]	HUANG W,WANG R,CHEN Y,et al. Damped multichannel singular spectrum analysis for 3D random noise attenuation[J]. Geophysics,2016,81(4):V261-V270.
[10]	FAOUZI ZIZI M O,TURQUAIS P. A dictionary learning method for seismic data compression[J]. Geophysics,2022,87(2):V101-V116.
[11]	CHEN Y K,FOMEL S. EMD-seislet transform[J]. Geophysics,2018,83(1):A27-A32.
[12]	ZHANG X,CHEN Y,JIA R,et al. Two-dimensional variational mode decomposition for seismic record denoising[J]. Journal of Geophysics and Engineering,2022,19(3):433-444.
[13]	YU S,MA J,WANG W. Deep learning for denoising[J]. Geophysics,2019,84(6):V333-V350.
[14]	LIU D,WANG W,WANG X,et al. Poststack seismic data denoising based on 3-D convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing,2019,58(3):1598-1629.
[15]	WANG F,CHEN S. Residual learning of deep convolutional neural network for seismic random noise attenuation[J]. IEEE Geoscience and Remote Sensing Letters,2019,16(8):1314-1318.
[16]	ZHU W, MOUSAVI S M, BEROZA G C. Seismic signal denoising and decomposition using deep neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing,2019,57(11):9476-9488.
[17]	ZHAO Y,LI Y,DONG X,et al. Low-frequency noise suppression method based on improved DnCNN in desert seismic data[J]. IEEE Geoscience and Remote Sensing Letters,2019,16(5):811-815.
[18]	董新桐,钟铁,王洪洲,等. 基于卷积对抗降噪网络的塔里木盆地沙漠地震资料消噪方法研究[J]. 地球物理学报,2022,65(7):2661-2672.DONG Xintong,ZHONG Tie,WANG Hongzhou,et al. The denoising of desert seismic data acquired from Tarim Basin based on convolutional adversarial denoising network[J]. Chinese Journal of Geophysics,2022,65(7):2661-2672.
[19]	YAO X,HAN J,CHENG G,et al. Semantic annotation of high-resolution satellite images via weakly supervised learning[J]. IEEE Transactions on Geoscience and Remote Sensing,2016,54(6):3660-3671.
[20]	宋辉,高洋,陈伟,等. 基于卷积降噪自编码器的地震数据去噪[J]. 石油地球物理探,2020,55(6):1210-1219.SONG Hui,GAO Yang,CHEN Wei,et al. Seismic noise suppression based on convolutional denoising autoencoders[J]. Oil Geophysical Prospecting,2020,55(6):1210-1219.
[21]	ZHANG M,LIU Y,BAI M,et al. Seismic noise attenuation using unsupervised sparse feature learning[J]. IEEE Transactions on Geoscience and Remote Sensing,2019,57(12):9709-9723.
[22]	YANG L,WANG S,CHEN X,et al. Unsupervised 3D random noise attenuation using deep skip autoencoder[J]. IEEE Transactions on Geoscience and Remote Sensing,2022,60:1-16.
[23]	SAAD O M,BAI M,CHEN Y. Uncovering the microseismic signals from noisy data for high-fidelity 3D source-location imaging using deep learning[J]. Geophysics,2021,86(6):KS161-KS173.
[24]	SAAD O M,CHEN Y. Deep denoising autoencoder for seismic random noise attenuation[J]. Geophysics,2020,85(4):V367-V376.
[25]	LIU D,DENG Z,WANG C,et al. An unsupervised deep learning method for denoising prestack random noise[J]. IEEE Geoscience and Remote Sensing Letters,2022,19:1-5.
[26]	LECUN Y. Modeles Connexionnistes de L'apprentissage (Connectionist Learning Models)[D]. Universite Pierre et Marie Curie (Paris 6),1987.
[27]	CHEN Y,ZHANG M,BAI M,et al. Improving the signal-to-noise ratio of seismological datasets by unsupervised machine learning[J]. Seismological Research Letters,2019,90(4):1552-1564.