ABSTRACT Seismically derived amplitude‐versus‐angle attributes along with well constraints are the base inputs into inverting seismic into subsurface properties. Conditioning the common image gathers is a common workflow in quantitative inversion and leads to a more accurate inversion product due to the removal of post‐migration artefacts. Here, we apply a neural network to condition the post‐migration gathers. The network is a cycle generative adversarial network, CycleGAN, which was designed for image‐to‐image translation. This can be considered the same problem as translating an artefact rich seismic gather to an artefact free seismic gather. To assess the feasibility of applying the network to pre‐stack conditioning, synthetic data sets were generated to train different networks for different tasks. The networks were trained to remove white noise, residual de‐multiples, gather flattening and a combination of the above for conditioning. The results show that a trained network was able remove white noise providing a more robust amplitude‐versus‐offset calculation. Another network trained using synthetic gathers with and without multiples assisted in multiple removal. However, instability around primary preservation has been observed so the network works better as a residual de‐multiple method. For gather conditioning, a network was trained with the unpaired artefact‐rich and artefact‐free training data where the artefacts included complex moveout, noise and multiples. When applied to the test data sets, the networks cleaned the artefact‐rich test data and translated complex moveout into flat gathers whilst preserving the amplitude response. Finally, two networks are applied to real data where a gather based on the well logs is used to quantify the match between the conditioned gathers and the raw gathers. The first network used synthetic data to train the network and, when applied to real data, provided a better tie with the well. The second network was trained with synthetic gathers whose properties were constrained by real seismic gathers from near the well. As anticipated, the network trained on the representative training data outperforms the network trained using the unconstrained data. However, the ability of the first network to condition the gather indicates that a sweep of networks can be trained without the need for real data and applied in a manner analogous to the way parameters are adjusted in traditional geophysical methods. The results show that the different neural networks can offer an alternative or augmentation to the existing geophysical workflow for conditioning pre‐stack seismic gathers.

中文翻译：

---

使用循环生成对抗网络的常见图像收集调节

摘要 地震导出的振幅-角度属性以及井约束是将地震反演为地下属性的基本输入。调节公共图像集是定量反演中的一个常见工作流程，由于去除了迁移后的伪影，可以得到更准确的反演产品。在这里，我们应用神经网络来调节迁移后的集合。该网络是一个循环生成对抗网络 CycleGAN，专为图像到图像的翻译而设计。这可以被认为是与将富含人工制品的地震道集转换为无人工制品的地震道集相同的问题。为了评估将该网络应用于叠前调节的可行性，生成了合成数据集以针对不同的任务训练不同的网络。网络经过训练以去除白噪声、残差去乘法、聚集平坦化以及上述条件的组合。结果表明，经过训练的网络能够去除白噪声，提供更稳健的幅度对偏移计算。另一个使用合成聚集训练的网络，有和没有倍数辅助多重去除。然而，已经观察到初级保存的不稳定性，因此网络作为残差去乘法效果更好。对于聚集调节，使用未配对的富含人工制品和无人工制品的训练数据训练网络，其中人工制品包括复杂的时差、噪声和倍数。当应用于测试数据集时，网络清理了大量人工制品的测试数据，并将复杂的时差转换为平坦的道集，同时保留幅度响应。最后，将两个网络应用于实际数据，其中基于测井记录的集合用于量化条件集合和原始集合之间的匹配。第一个网络使用合成数据来训练网络，当应用于真实数据时，提供了更好的与井的联系。第二个网络使用合成道集进行训练，其属性受井附近真实地震道集的限制。正如预期的那样，在代表性训练数据上训练的网络优于使用无约束数据训练的网络。然而，第一个网络调节道集的能力表明，可以在不需要真实数据的情况下训练网络扫描，并以类似于传统地球物理方法中调整参数的方式应用。