学习迁移
稳健性(进化)
合并(版本控制)
计算机科学
深度学习
切片
人工神经网络
数据挖掘
人工智能
网(多面体)
地质学
化学
情报检索
万维网
基因
生物化学
几何学
数学
作者
Yun Zhu,Jingjie Cao,Hang Yin,Jingtao Zhao,Kehan Gao
标识
DOI:10.1016/j.jappgeo.2023.105241
摘要
In field seismic exploration, missing seismic traces is inevitably encountered due to the constraints of the exploration environment and equipment. Thus, seismic data reconstruction is essential for seismic exploration analysis. In this paper, an attention-based U-net (AU-net) architecture is proposed by incorporating the attention mechanism into the U-net structure to address seismic data reconstruction challenges. The network incorporates both channel and spatial attention modules. Experiments comparing the reconstruction results of U-net, DnCNN, curvelet, and AU-net demonstrate the robustness of deep learning methods in handing increased missing trace percentages. The AU-net achieves superior reconstruction effect on contiguous missing regions, and exhibits better generalization through transfer learning experiments. To facilitate network training, this paper introduces a novel data-slicing technique to split and merge rectangular data for reconstructing shot records. This approach can be applied to any network and has high practical value.
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