Regularized deep learning for unsupervised random noise attenuation in poststack seismic data

IF 1.6 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Chengyun Song, Shutao Guo, Chuanchao Xiong, Jiying Tuo
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引用次数: 0

Abstract

Deep learning methods achieve excellent noise reduction performances in seismic data processing compared with traditional methods. However, deep learning usually requires a large number of pairwise noisy-clean training data, which is an extremely challenging task. In this paper, an unsupervised approach without clean seismic data is proposed to suppress random noise. Seismic data is divided into odd and even traces, which serve as the input and output of the depth network. So that the proposed algorithm can be trained directly on the original data. What is more, the proposed method introduces two regularization terms to solve the over-smoothing problem caused by reconstruction of adjacent traces. The first term considers an ideal denoising network that does not cause oversmooth as a constraint, while the second term considers the structural information existing in seismic data. Experiments on synthetic post-stack data illustrate that the proposed method obtain the higher SNR than the comparison methods. In the application of field post-stack seismic data, the proposed method can effectively maintain the seismic amplitude and generate good spectral characteristics.
用于叠后地震数据无监督随机噪声衰减的正则化深度学习
与传统方法相比,深度学习方法在地震数据处理中具有出色的降噪性能。然而,深度学习通常需要大量成对的噪声-清洁训练数据,这是一项极具挑战性的任务。本文提出了一种无需干净地震数据的无监督方法来抑制随机噪声。地震数据被分为奇数道和偶数道,作为深度网络的输入和输出。这样,提出的算法就可以直接在原始数据上进行训练。此外,该方法还引入了两个正则化项,以解决因重建相邻地震道而产生的过平滑问题。第一个正则化项将不会导致过平滑的理想去噪网络作为约束条件,而第二个正则化项则考虑了地震数据中存在的结构信息。在合成叠后数据上的实验表明,所提出的方法比对比方法获得了更高的信噪比。在野外叠后地震数据的应用中,所提出的方法能有效地保持地震振幅并产生良好的频谱特征。
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来源期刊
Journal of Geophysics and Engineering
Journal of Geophysics and Engineering 工程技术-地球化学与地球物理
CiteScore
2.50
自引率
21.40%
发文量
87
审稿时长
4 months
期刊介绍: Journal of Geophysics and Engineering aims to promote research and developments in geophysics and related areas of engineering. It has a predominantly applied science and engineering focus, but solicits and accepts high-quality contributions in all earth-physics disciplines, including geodynamics, natural and controlled-source seismology, oil, gas and mineral exploration, petrophysics and reservoir geophysics. The journal covers those aspects of engineering that are closely related to geophysics, or on the targets and problems that geophysics addresses. Typically, this is engineering focused on the subsurface, particularly petroleum engineering, rock mechanics, geophysical software engineering, drilling technology, remote sensing, instrumentation and sensor design.
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