We propose an algorithm for seismic trace interpolation using generative adversarial networks, a type of deep neural network. The method extracts feature vectors from the training data using self‐learning and does not require any pre‐processing to create the training labels. The algorithm also does not make any prior explicit assumptions about linearity of seismic events or sparsity of the data, which are often required in the traditional interpolation methods. We create the training labels by removing traces from different receiver indices of the original datasets to simulate the effect of missing traces. We adopt the framework of the generative adversarial networks to train the network and add additional loss functions to regularize the model. Numerical examples using land and marine field datasets demonstrate the validity and effectiveness of the proposed approach. With minimal computational burden and proper training, the proposed method can be applied to three‐dimensional seismic datasets to achieve accurate interpolation results.

中文翻译：

---

使用深度生成和对抗网络进行地震数据插值

我们提出了一种使用生成对抗网络（一种深度神经网络）的地震道插值算法。该方法使用自学习从训练数据中提取特征向量，不需要任何预处理即可创建训练标签。该算法也没有对地震事件的线性或数据稀疏性做出任何事先的明确假设，而传统的插值方法通常需要这些假设。我们通过从原始数据集的不同接收者索引中删除迹线来创建训练标签，以模拟丢失迹线的效果。我们采用生成对抗网络的框架来训练网络并添加其他损失函数以使模型正规化。使用陆地和海洋领域数据集的数值示例证明了该方法的有效性和有效性。通过最小的计算负担和适当的训练，该方法可应用于三维地震数据集，以实现准确的插值结果。