基于高阶TV正则化的叠前动校正域随机噪声压制方法

doi:10.13810/j.cnki.issn.1000-7210.2024.01.006

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Abstract The conventional total variation （TV） regularization model only considers the first-order derivative information in the horizontal and vertical directions. When dealing with prestack seismic data with curved reflection events，it can severely damage the amplitude information and cause “staircase effects” by suppressing the lateral gradient characteristics of the amplitude. The local dip information of seismic data is often applied to improve the amplitude-preserving ability of the TV model. However，the calculation of local dip information itself will be impacted by noise. To address this issue，this paper proposes a high-order TV regularization model to suppress random noise in prestack seismic data in the domain of normal moveout（NMO）. This method first transforms the prestack seismic data into the NMO domain，NMO is robust to noise and avoids the calculation of the local dip angle. In the NMO domain，the curved event is flattened，and then high-order TV denoising is performed. Finally，the prestack seismic data are restored through inverse NMO. Taking the second-order derivative as an example，a high-order TV regularization inversion denoising objective function is constructed，and a fast optimization method is derived under the split Bregman optimization framework. The processing results of synthetic seismic data and actual seismic data show that this method can not only effectively suppress random noise but also eliminate amplitude distortion caused by curved reflection events and “staircase effects”， improving the amplitude preservation performance of the TV denoising method.

Key words： high order TV regularization normal moveout （NMO） domain random noise suppression amplitude-preserving denoising split Bregman optimization framework

Received: 25 May 2023

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P631

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	Articles by authors
	ZHANG Peng
	HAO Yaju
	ZHU Yunfeng
	ZHANG Hongjing
	YIN Duowen
	TIAN Xiao

Cite this article:

ZHANG Peng,HAO Yaju,ZHU Yunfeng等. Random noise suppression method in prestack NMO domain based on high-order TV regularization[J]. OGP, 2024, 59(1): 70-79.

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http://www.ogp-cn.com/EN/10.13810/j.cnki.issn.1000-7210.2024.01.006 OR http://www.ogp-cn.com/EN/Y2024/V59/I1/70

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