Abstract

Least-squares migration (LSM) is a data-fitting imaging approach seeking the seismic reflectivity image of the most accurate amplitude and optimal resolution. However, the high computational cost of LSM has hindered its broad application. In this study, we combine a convolutional neural network (CNN) with LSM to significantly improve the computational efficiency while retaining the imaging quality. Taking CNN as a “projector,” we treat LSM as the “projection” from the ordinarily migrated images to the least-squares updated images. We conduct this CNN-assisted LSM in the shot gather domain using a Gaussian beam migration and the corresponding LSM. The training data for CNN consist of 10–15% of all shot gathers, with the Gaussian beam migrated shot gathers as the input and the LSM shot gathers as the target. After the training, the processing time for the remaining shot gathers took several minutes for 2D cases. The results from testing with the Sigsbee 2B synthetic dataset and a field marine dataset indicate the CNN-assisted LSM saved 80–90% of the computation time of the full LSM and achieved significantly higher image fidelity than that of the ordinary migration.

中文翻译：

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卷积神经网络辅助最小二乘迁移

摘要

最小二乘偏移（LSM）是一种数据拟合成像方法，用于寻找振幅最准确、分辨率最优的地震反射率图像。然而，LSM 的高计算成本阻碍了其广泛应用。在这项研究中，我们将卷积神经网络 (CNN) 与 LSM 相结合，以在保持成像质量的同时显着提高计算效率。将 CNN 视为“投影仪”，我们将 LSM 视为从普通迁移图像到最小二乘更新图像的“投影”。我们使用高斯光束偏移和相应的 LSM 在镜头收集域中执行此 CNN 辅助 LSM。CNN 的训练数据占所有炮集的 10-15%，以高斯波束偏移炮集作为输入，LSM 炮集作为目标。培训结束后，D例。使用 Sigsbee 2B 合成数据集和野外海洋数据集进行测试的结果表明，CNN 辅助 LSM 节省了完整 LSM 的 80-90% 的计算时间，并实现了比普通偏移显着更高的图像保真度。