It is well known that source deghosting can best be applied to common-receiver gathers, whereas receiver deghosting can best be applied to common-shot records. The source-ghost wavefield observed in the common-shot domain contains the imprint of the subsurface, which complicates source deghosting in the common-shot domain, in particular when the subsurface is complex. Unfortunately, the alternative, that is, the common-receiver domain, is often coarsely sampled, which complicates source deghosting in this domain as well. To solve the latter issue, we have trained a convolutional neural network to apply source deghosting in this domain. We subsample all shot records with and without the receiver-ghost wavefield to obtain the training data. Due to reciprocity, these training data are a representative data set for source deghosting in the coarse common-receiver domain. We validate the machine-learning approach on simulated data and on field data. The machine-learning approach gives a significant uplift to the simulated data compared to conventional source deghosting. The field-data results confirm that the proposed machine-learning approach can remove the source-ghost wavefield from the coarsely sampled common-receiver gathers.

中文翻译：

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使用卷积神经网络对粗采样的公共接收器数据进行源去虚幻

众所周知，源去幻影可以最好地应用于公共接收者集合，而接收者去幻影可以最好地应用于公共镜头记录。在共同散发域中观察到的源-幽灵波场包含地下的印记，这使共同散发域中的源去虚反射变得复杂，尤其是在地下复杂的情况下。不幸的是，通常是粗略地采样替代方案，即公共接收器域，这也使该域中的源去鬼影也变得复杂。为了解决后一个问题，我们训练了卷积神经网络以在该域中应用源去鬼影。我们对具有和不具有接收器-幽灵波场的所有射击记录进行二次采样，以获得训练数据。由于互惠，这些训练数据是在粗糙的公共接收器域中用于源去虚幻的代表性数据集。我们验证了对模拟数据和现场数据的机器学习方法。与传统的源反虚幻处理相比，机器学习方法极大地提高了模拟数据的准确性。现场数据结果证实，所提出的机器学习方法可以从粗糙采样的公共接收器波集中去除源-幽灵波场。