Elastic reverse-time migration (ERTM) is becoming increasingly feasible with the development of high-performance computing. It can provide more physical information on subsurface structures. However, the crosstalk artifacts degrade the imaging resolution of ERTM. To obtain high-resolution ERTM imaging, we have developed additional constraints through a convolutional neural network (CNN) in the dip-angle domain. This procedure can significantly improve the image quality of ERTM by recognizing the dominant reflection events and rejecting the crosstalk artifacts in the dip-angle domain. This method can be divided into the following three steps. First, we generate the dip-angle gathers of ERTM using Poynting vectors shot by shot. Then, we stack all the dip-angle gathers over all the shots. Finally, we adopt the CNN to predict the dip-angle constraint, which can suppress the crosstalk artifacts and enhance the ERTM image quality. The picking method using CNN is an end-to-end procedure that can perform automatic picking without additional human intervention once the network is well-trained. The numerical examples have verified the potential of our method.

中文翻译：

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使用深度学习改善倾角域中弹性逆时偏移的图像质量

随着高性能计算的发展，弹性逆时迁移（ERTM）变得越来越可行。它可以提供有关地下结构的更多物理信息。但是，串扰伪像会降低ERTM的成像分辨率。为了获得高分辨率的ERTM成像，我们通过倾角域中的卷积神经网络（CNN）开发了其他约束。通过识别主要反射事件并拒绝倾角域中的串扰伪像，此过程可以显着提高ERTM的图像质量。此方法可以分为以下三个步骤。首先，我们使用逐次拍摄的坡印廷矢量生成ERTM的倾角聚集。然后，我们将所有俯角收集在所有镜头上。最后，我们采用CNN来预测倾角约束，可以抑制串扰伪像并提高ERTM图像质量。使用CNN的挑选方法是一种端到端的过程，一旦网络训练有素，就可以执行自动挑选，而无需额外的人工干预。数值例子验证了我们方法的潜力。