Delimiting salt inclusions from migrated images during the velocity model building flow is a time‐consuming activity that depends on highly human‐curated analysis and is subject to interpretation errors or limitations of the images and methods available. We propose a supervised deep learning based method to include three‐dimensional salt geometries in the velocity models. We compare two convolutional networks – based on the U‐Net architecture – which can process three‐dimensional seismic data. One architecture uses three‐dimensional convolutional kernels, and the other has convolutional long short‐term memory units. Each architecture requires specific preprocessing steps which affects their training and predictive performance. Both proposed architectures use subsurface offset gathers obtained from reverse time migration with an extended imaging condition as input and are trained to predict the salt inclusions. The velocity model used in migration is a reasonable approximation of sediment velocity but without salt inclusions. Thus, the migration model and, consequently, the migrated images are inaccurate due to the absence of the salt inclusion in the model using just the sediment velocity information for the segmentation. A similar salt inclusion methodology was previously validated for two‐dimensional approaches; we extend it to the three‐dimensional case. Our approach relies on subsurface common image gathers to focus the sediments' reflections around the zero offset and spread salt reflections' energy over large subsurface offsets. The results show that both proposed network models can accurately delineate the salt bodies in the test models, but when evaluating the trained networks for the three‐dimensional SEG/EAGE salt model, the architecture with convolutional long short‐term memory units has proven to generalize better.

中文翻译：

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深层盐：使用深度学习从不准确的迁移的地下偏移集合中完成三维盐分割

在速度模型构建流程中从迁移图像中界定盐夹杂物是一项耗时的活动，它依赖于高度人工策划的分析，并且容易受到解释错误或可用图像和方法的限制。我们提出了一种基于监督深度学习的方法，将三维盐几何形状包含在速度模型中。我们比较了两个基于 U-Net 架构的卷积网络，它们可以处理三维地震数据。一种架构使用三维卷积核，另一种架构具有卷积长短期记忆单元。每种架构都需要特定的预处理步骤，这会影响其训练和预测性能。两种提出的架构都使用从逆时偏移获得的地下偏移道集，并以扩展成像条件作为输入，并经过训练来预测盐包裹体。迁移中使用的速度模型是沉积物速度的合理近似，但不包含盐包裹体。因此，由于仅使用沉积物速度信息进行分割的模型中不存在盐夹杂物，因此迁移模型以及迁移图像是不准确的。先前已针对二维方法验证了类似的盐包含方法；我们将其扩展到三维情况。我们的方法依赖于地下共同图像收集，将沉积物的反射集中在零偏移附近，并将盐反射的能量传播到大的地下偏移上。结果表明，两种提出的网络模型都可以准确地描绘测试模型中的盐体，但是在评估三维 SEG/EAGE 盐模型的训练网络时，具有卷积长短期记忆单元的架构已被证明可以泛化更好的。