Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning

• URL: http://arxiv.org/abs/2404.08408v1
• Date: Fri, 12 Apr 2024 11:36:24 GMT
• Title: Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning
• Authors: Hongtao Wang, Li Long, Jiangshe Zhang, Xiaoli Wei, Chunxia Zhang, Zhenbo Guo,
• Abstract summary: First break (FB) picking methods typically analyze 1D signals, 2D source gathers, or 3D source-receiver gathers. We propose a novel approach using deep graph learning called DGL-FB, constructing a large graph to efficiently extract information. DGL-FB 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.
• Score: 6.021171669112303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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