Diffraction imaging is the process of separating diffraction events from the seismic wavefield and imaging them independently, highlighting subsurface discontinuities. While there are many analytic‐based methods for diffraction imaging which use kinematic, dynamic or both, properties of the diffracted wavefield, they can be slow and require parameterization. Here, we propose an image‐to‐image generative adversarial network to automatically separate diffraction events on pre‐migrated seismic data in a fraction of the time of conventional methods. To train the generative adversarial network, plane‐wave destruction was applied to a range of synthetic and real images from field data to create training data. These training data were screened and any areas where the plane‐wave destruction did not perform well, such as synclines and areas of complex dip, were removed to prevent bias in the neural network. A total of 14,132 screened images were used to train the final generative adversarial network. The trained network has been applied across several geologically distinct field datasets, including a 3D example. Here, generative adversarial network separation is shown to be comparable to a benchmark separation created with plane‐wave destruction, and up to 12 times faster. This demonstrates the clear potential in generative adversarial networks for fast and accurate diffraction separation.

中文翻译：

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使用生成对抗网络进行迁移前衍射分离

衍射成像是将衍射事件从地震波场中分离出来并对其进行独立成像的过程，突出了地下不连续性。尽管有很多基于解析的衍射成像方法都使用了动态，动态或同时使用了衍射波场的特性，但它们可能很慢，需要进行参数化。在这里，我们提出了一个图像到图像的生成对抗网络，以自动分离预先迁移的地震数据上的衍射事件，而所需时间仅为传统方法的一小部分。为了训练生成的对抗网络，将平面波破坏应用于来自现场数据的一系列合成和真实图像，以创建训练数据。筛选了这些训练数据，以及平面波破坏效果不佳的任何区域，例如向斜线和复杂倾角的区域，删除以防止神经网络出现偏差。总共使用了14132张筛选的图像来训练最终的生成对抗网络。训练有素的网络已应用于多个地质上不同的现场数据集，包括3D示例。在这里，生成的对抗网络分离被证明可以与平面波破坏所产生的基准分离相媲美，并且可以快12倍。这证明了在生成对抗网络中快速和准确地进行衍射分离的巨大潜力。生成对抗网络的分离与通过平面波破坏创建的基准分离具有可比性，并且快了12倍。这证明了在生成对抗网络中快速和准确地进行衍射分离的巨大潜力。生成对抗网络的分离与通过平面波破坏创建的基准分离具有可比性，并且快了12倍。这证明了在生成对抗网络中快速和准确地进行衍射分离的巨大潜力。