When sampling at offset is too coarse during seismic acquisition, spatial aliasing will appear, affecting the accuracy of subsequent processing. The receiver spacing can be reduced by interpolating one or more traces between every two traces to remove the spatial aliasing. And the seismic data with spatial aliasing can be seen as regular missing data. Deep learning is an efficient method for seismic data interpolation. We propose to interpolate the regular missing seismic data to remove the spatial aliasing by using conditional generative adversarial networks (cGAN). Wasserstein distance, which can avoid gradient vanishing and mode collapse, is used in training cGAN (cWGAN) to improve the quality of the interpolated data. One velocity model is designed to simulate the training dataset. Test results on different seismic datasets show that the cWGAN with Wasserstein distance is an accurate way for de-aliased seismic data interpolation. Unlike the traditional interpolation methods, cWGAN can avoid the assumptions of low-rank, sparsity, or linearity of seismic data. Besides, once the neural network is trained, we do not need to test different parameters for the best interpolation result, which will improve efficiency.

在地震采集过程中，如果偏移处的采样过于粗糙，则会出现空间混叠，从而影响后续处理的准确性。可以通过在每两个迹线之间插入一个或多个迹线以消除空间混叠来减小接收器间距。具有空间混叠的地震数据可以看作是常规缺失数据。深度学习是用于地震数据插值的有效方法。我们建议对常规缺失地震数据进行插值，以使用条件生成对抗网络（cGAN）消除空间混叠。Wasserstein距离可以避免梯度消失和模式崩溃，它用于训练cGAN（cWGAN）以提高插值数据的质量。设计了一种速度模型来模拟训练数据集。在不同地震数据集上的测试结果表明，具有Wasserstein距离的cWGAN是消除混叠地震数据插值的准确方法。与传统的插值方法不同，cWGAN可以避免地震数据的低秩，稀疏性或线性的假设。此外，一旦训练了神经网络，我们就不需要测试不同的参数以获得最佳插值结果，这将提高效率。