Ground-roll attenuation using generative adversarial networks
Ground-roll attenuation using generative adversarial networks
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DOI：
10.1190/geo2019-0414.1
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发表时间：
2020-07
期刊：
影响因子：
3.3
通讯作者：
Yijun Yuan;Xu Si;Y. Zheng
中科院分区：
文献类型：
--
作者：
Yijun Yuan;Xu Si;Y. Zheng
地滚是陆地地震资料中一个长期存在的问题。这种相干噪声经常污染地震信号，严重降低地震资料的信噪比。各种解决地滚衰减的方法已经被开发出来。然而，现有的方法是有限的，特别是在使用实际的陆地地震数据时。例如，当地滚和反射在时域或频域重叠时，传统的方法不能完全分离它们，并且在抑制过程中往往会使信号失真。我们开发了一种生成对抗网络（GAN）来衰减地震数据中的地滚。与依赖于各种滤波器的地滚衰减的传统方法不同，GAN方法基于一个大型训练数据集，其中包括有地滚和没有地滚的数据对。用训练数据训练神经网络后，神经网络可以识别并滤除数据中的任何噪声。为了实现这一目的，该方法使用了一个生成器和一个鉴别器。通过网络训练，生成器学习创建可以欺骗鉴别器的数据，鉴别器可以区分生成器生成的数据和训练数据。由于生成器和鉴别器之间的竞争，生成器产生的图像质量更好，而鉴别器对目标的识别精度更高。在合成地震数据和实际地面地震数据上的试验表明，该方法能有效地揭示被地滚掩盖的反射，在地滚衰减和信号保存方面取得了较好的效果。
Ground roll is a persistent problem in land seismic data. This type of coherent noise often contaminates seismic signals and severely reduces the signal-to-noise ratio of seismic data. A variety of methods for addressing ground-roll attenuation have been developed. However, existing methods are limited, especially when using real land seismic data. For example, when ground roll and reflections overlap in the time or frequency domains, traditional methods cannot completely separate them and they often distort the signals during the suppression process. We have developed a generative adversarial network (GAN) to attenuate ground roll in seismic data. Unlike traditional methods for ground-roll attenuation dependent on various filters, the GAN method is based on a large training data set that includes pairs of data with and without ground roll. After training the neural network with the training data, the network can identify and filter out any noise in the data. To fulfill this purpose, the proposed method uses a generator and a discriminator. Through network training, the generator learns to create the data that can fool the discriminator, and the discriminator can then distinguish between the data produced by the generator and the training data. As a result of the competition between generators and discriminators, generators produce better images whereas discriminators accurately recognize targets. Tests on synthetic and real land seismic data show that the proposed method effectively reveals reflections masked by the ground roll and obtains better results in the attenuation of ground roll and in the preservation of signals compared to the three other methods.