基于去噪卷积神经网络的异常振幅压制方法

doi:10.13810/j.cnki.issn.1000-7210.2023.04.003

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Abstract Abnormal amplitude of seismic data often leads to uneven spatial energy in seismic data, resulting in arc phenomenon during the prestack migration and interfering with seismic data interpretation. Therefore, suppressing abnormal amplitudes has become an important step in seismic data processing. Due to the limitation of application conditions, traditional methods fail to completely suppress abnormal amplitude while protecting effective signals. Therefore, a method for suppressing abnormal amplitude based on a denoising convolutional neural network (DnCNN) is proposed. Firstly, according to the distribution characteristics of seismic abnormal amplitude, this method builds a DnCNN structure suitable for suppressing abnormal amplitude through network improvement and optimization. Secondly, training sets with and without abnormal amplitude are produced by artificial synthesis and real data extraction. The network is trained with the training set, and a network training model that can suppress abnormal amplitude is obtained. Finally, tests on model data and real seismic data show that the proposed method can effectively suppress abnormal amplitude in seismic data, protect effective signals, and obtain better processing results than commonly used traditional methods.

Key words： deep learning convolutional neural network training sets abnormal amplitude denoising

Received: 22 August 2022

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P631

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	Articles by authors
	FAN Chengxiang
	GUO Hongwei
	YUAN Yijun

Cite this article:

FAN Chengxiang,GUO Hongwei,YUAN Yijun. Abnormal amplitude suppression method based on denoising convolutional neural network[J]. OGP, 2023, 58(4): 780-788.

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http://www.ogp-cn.com/EN/10.13810/j.cnki.issn.1000-7210.2023.04.003 OR http://www.ogp-cn.com/EN/Y2023/V58/I4/780

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