ABSTRACT We propose to adopt a deep learning based framework using generative adversarial networks for ground‐roll attenuation in land seismic data. Accounting for the non‐stationary properties of seismic data and the associated ground‐roll noise, we create training labels using local time–frequency transform and regularized non‐stationary regression. The basic idea is to train the network using a few shot gathers such that the network can learn the weights associated with noise attenuation for the training shot gathers. We then apply the learned weights to test ground‐roll attenuation on shot gathers, that are not a part of training input to obtain the desired signal. This approach gives results similar to local time–frequency transform and regularized non‐stationary regression but at a significantly reduced computational cost. The proposed approach automates the ground‐roll attenuation process without requiring any manual input in picking the parameters for each shot gather other than in the training data. Tests on field‐data examples verify the effectiveness of the proposed approach.

中文翻译：

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使用深度学习的地震地滚噪声衰减

摘要我们建议采用基于深度学习的框架，使用生成对抗网络对陆地地震数据中的地滚衰减进行处理。考虑到地震数据的非平稳特性和相关的地滚噪声，我们使用本地时频变换和正则化非平稳回归创建训练标签。基本思想是使用几个镜头集训练网络，以便网络可以学习与训练镜头集的噪声衰减相关的权重。然后，我们应用学习到的权重来测试炮集上的地滚衰减，这不是获得所需信号的训练输入的一部分。这种方法给出的结果类似于本地时频变换和正则化非平稳回归，但计算成本显着降低。所提出的方法使地滚衰减过程自动化，而无需任何手动输入来为除训练数据之外的每个炮集选择参数。对现场数据示例的测试验证了所提出方法的有效性。