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Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning
April 15, 2024, 4:42 a.m. | Hongtao Wang, Li Long, Jiangshe Zhang, Xiaoli Wei, Chunxia Zhang, Zhenbo Guo
cs.LG updates on arXiv.org arxiv.org
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 …
abstract analyze arxiv benefits cs.ai cs.lg data eess.sp features global graph graph learning improving physics.geo-ph seismic stability type
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