  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.
