Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning
This work addresses the challenge of efficient and stable seismic data processing for geophysical applications, representing an incremental improvement over existing methods.
The paper tackles the problem of automatic first break picking in seismic data by proposing DGL-FB, a deep graph learning method that constructs graphs from seismic traces to incorporate global features, resulting in superior accuracy and stability compared to a 2D U-Net benchmark in field survey evaluations.
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.