Seismic First Break Picking in a Higher Dimension Using Deep Graph
  Learning
Hongtao Wang Li Long Jiangshe Zhang Xiaoli Wei Chunxia Zhang Zhenbo Guo
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.