Knowing a land’s facies type before drilling is an essential step in oil exploration. In seismic surveying, subsurface images are analyzed to segment and classify the facies in that volume. With the recent developments in deep learning, multiple works have utilized deep neural networks to classify facies from subsurface images. Unlike natural images, seismic data have different patterns and structures, which means that although general deep learning architectures can work with seismic data, it would be more effective if these architectures were optimized and refined specifically for such types of data. Most of the works in the seismic domain focus on convolution neural networks as the main backbone for the architectures, and more recently transformers started becoming more common in seismic data processing. Proposing a different approach that can capture unique correlations in the data, we introduce the use of dynamic graph convolutional networks as a method for capturing long-term dependencies for seismic facies classification. The proposed architecture combines the use of convolution neural networks and graph convolution networks to capture both global and local structures of the data. The performance of the model was evaluated on a facies classification dataset, and the proposed method provided state-of-the-art results while significantly reducing the number of parameters in the model compared to other architectures. Code is available at https://github.com/swaidan/Geology-Restricted-DGCNN.

钻探前了解陆地相类型是石油勘探的重要步骤。在地震勘探中，分析地下图像以对该体积中的相进行分割和分类。随着深度学习的最新发展，多项工作利用深度神经网络对地下图像中的相进行分类。与自然图像不同，地震数据具有不同的模式和结构，这意味着虽然一般的深度学习架构可以处理地震数据，但如果这些架构专门针对此类数据进行优化和细化，则会更有效。地震领域的大多数工作都集中在卷积神经网络作为架构的主要骨干上，最近变压器开始在地震数据处理中变得越来越普遍。我们提出了一种可以捕获数据中独特相关性的不同方法，引入了使用动态图卷积网络作为捕获地震相分类的长期依赖性的方法。所提出的架构结合了卷积神经网络和图卷积网络的使用来捕获数据的全局和局部结构。该模型的性能在相分类数据集上进行了评估，所提出的方法提供了最先进的结果，同时与其他架构相比显着减少了模型中的参数数量。代码可在https://github.com/swaidan/Geology-Restricted-DGCNN获取。