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.

中文翻译：

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使用生成对抗网络衰减地滚

地滚是陆地地震数据中一个长期存在的问题。这种类型的相干噪声通常会污染地震信号，并严重降低地震数据的信噪比。已经开发出多种解决地滚衰减的方法。但是，现有方法受到限制，尤其是在使用真实陆地地震数据时。例如，当地滚波和反射在时域或频域重叠时，传统方法无法将它们完全分开，并且它们通常会在抑制过程中使信号失真。我们已经开发了一个生成对抗网络（GAN），以减弱地震数据中的地面滚动。与依赖于各种滤波器的地滚衰减的传统方法不同，GAN方法基于大型训练数据集，该数据集包括具有和不具有地滚的数据对。用训练数据训练神经网络后，网络可以识别并过滤掉数据中的任何噪声。为了实现该目的，所提出的方法使用发生器和鉴别器。通过网络训练，生成器学习创建可以欺骗区分器的数据，然后区分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，生成器可产生更好的图像，而鉴别器可准确识别目标。对合成和真实陆地地震数据的测试表明，与其他三种方法相比，该方法有效地揭示了地滚波掩盖的反射，并且在衰减地滚波和保留信号方面获得了更好的结果。