Contemporary automatic first break (FB) picking methods typically analyze 1D signals, 2D source gathers, or 3D source-receiver gathers. Utilizing higher-dimensional data, such as 2D or 3D, incorporates global features, improving the stability of local picking. Despite the benefits, high-dimensional data requires structured input and increases computational demands. Addressing this, we propose a novel approach using deep graph learning called DGL-FB, constructing a large graph to efficiently extract information. In this graph, each seismic trace is represented as a node, connected by edges that reflect similarities. To manage the size of the graph, we develop a subgraph sampling technique to streamline model training and inference. Our proposed framework, DGL-FB, leverages deep graph learning for FB picking. It encodes subgraphs into global features using a deep graph encoder. Subsequently, the encoded global features are combined with local node signals and fed into a ResUNet-based 1D segmentation network for FB detection. Field survey evaluations of DGL-FB show superior accuracy and stability compared to a 2D U-Net-based benchmark method.

翻译：当代自动初至拾取方法通常分析一维信号、二维炮集或三维炮检集。利用二维或三维等高维数据可融入全局特征，提升局部拾取的稳定性。尽管高维数据具有优势，但其要求结构化输入且计算量增大。针对此问题，我们提出一种基于深度图学习的新方法DGL-FB，通过构建大规模图结构高效提取信息。在该图中，每道地震记录被表示为节点，节点之间通过反映相似性的边连接。为控制图规模，我们开发了子图采样技术以简化模型训练与推理。所提出的DGL-FB框架利用深度图学习进行初至拾取：首先通过深度图编码器将子图编码为全局特征，随后将编码后的全局特征与局部节点信号融合，输入基于ResUNet的一维分割网络进行初至检测。野外实测数据评估表明，与基于二维U-Net的基准方法相比，DGL-FB在准确性和稳定性方面均表现更优。