Seismic channel interpretation involves detecting channel structures, which often appear as meandering shapes in 3D seismic images. Many conventional methods are proposed for delineating channel structures using different seismic attributes. However, these methods are often sensitive to seismic discontinuities (e.g., noise and faults) that are not related to channels. We have adopted a convolutional neural network (CNN) method to improve automatic channel interpretation. The key problem in applying the CNN method into channel interpretation is the absence of labeled field seismic images for training the CNNs. To solve this problem, we adopt a workflow to automatically generate numerous synthetic training data sets with realistic channel structures. In this workflow, we first randomly simulate various meandering channel models based on geologic numerical simulation. We further simulate structural deformation in the form of stratigraphic folding referred to as “folding structures” and combine them with the previously generated channel models to create reflectivity models and the corresponding channel labels. Convolved with a wavelet, the reflectivity models can be transformed into learnable synthetic seismic volumes. By training the designed CNN with synthetic seismic data, we obtain a CNN that learns the characterization of channel structures. Although trained on only synthetic seismic volumes, this CNN shows outstanding performance on field seismic volumes. This indicates that the synthetic seismic images created in this workflow are realistic enough to train the CNN for channel interpretation in field seismic images.

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ChannelSeg3D：用于 3D 地震图像中通道解释的通道模拟和深度学习

地震通道解释涉及检测通道结构，这些结构在 3D 地震图像中通常表现为蜿蜒的形状。提出了许多传统方法来使用不同的地震属性来描绘通道结构。然而，这些方法通常对与通道无关的地震不连续性（例如，噪声和断层）敏感。我们采用了卷积神经网络 (CNN) 方法来改进自动通道解释。将 CNN 方法应用于通道解释的关键问题是缺乏用于训练 CNN 的标记现场地震图像。为了解决这个问题，我们采用了一种工作流程来自动生成大量具有真实通道结构的合成训练数据集。在这个工作流程中，我们首先在地质数值模拟的基础上随机模拟各种曲折通道模型。我们以称为“折叠结构”的地层折叠形式进一步模拟结构变形，并将它们与先前生成的通道模型相结合，以创建反射率模型和相应的通道标签。与小波卷积，反射率模型可以转换为可学习的合成地震体。通过用合成地震数据训练设计的 CNN，我们获得了一个学习通道结构特征的 CNN。尽管仅对合成地震体进行训练，但该 CNN 在现场地震体上表现出出色的性能。