Seismic random noise attenuation using DnCNN with stratigraphic dip constraint

IF 1.6 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Wei Yang, Xuehua Chen, Ying Rao
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引用次数: 0

Abstract

Abstract In this paper, a method for seismic random noise detection and suppression using a denoising convolutional neural network (DnCNN) is presented. Thanks to the residual learning and batch normalization, deep learning networks can converge faster, the gradient descent and disappearance due to the increase of network layers are solved, and the residual results can be predicted more accurately. For seismic data, the variance estimation method is useful for obtaining an accurate noise distribution model and statistical parameters that provide a useful assessment of the noise level. With the variance estimation method based on weak texture blocks, a noise distribution model and statistical parameters can be derived with high accuracy, and this method effectively estimates seismic noise levels. The DnCNN networks are trained, and non-Gaussian noise reduction technology is used to achieve blind noise reduction at unknown levels, improving the noise reduction of seismic data. In addition, stratigraphic dip characteristics related to layer structure are used as DnCNN training network constraints to prevent effective signals in seismic data from being corrupted by conventional DnCNN noise reduction methods. Geological features such as faults and fracture-cavities can be effectively protected. Carbonate faults in the Tarim Basin in China are affected by the desert surface and the depth at which reservoirs are buried. The seismic data has a low signal-to-noise ratio, and the effective signals of the reservoir are low resolution. The seismic data can be effectively enhanced with this method for noise reduction in this area, the fracture-cavity is effectively displayed, and the fault features are also highlighted.
基于地层倾角约束的DnCNN地震随机噪声衰减方法
提出了一种基于去噪卷积神经网络(DnCNN)的地震随机噪声检测与抑制方法。由于残差学习和批归一化,深度学习网络收敛速度更快,解决了由于网络层数增加导致的梯度下降和消失问题,残差结果预测更加准确。对于地震资料,方差估计方法有助于获得准确的噪声分布模型和统计参数,从而提供有用的噪声水平评估。利用基于弱纹理块的方差估计方法,可以较准确地导出噪声分布模型和统计参数,有效地估计了地震噪声水平。对DnCNN网络进行训练,利用非高斯降噪技术实现未知层次的盲降噪,提高地震数据的降噪效果。此外,利用与层结构相关的地层倾角特征作为DnCNN训练网络约束,防止地震数据中的有效信号被常规DnCNN降噪方法破坏。可有效保护断层、缝洞等地质特征。塔里木盆地碳酸盐岩断裂受沙漠地表和储层埋深的影响。地震资料信噪比低,储层有效信号分辨率低。该方法能有效增强该区域的地震资料降噪,有效显示缝洞,突出断层特征。
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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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