  In seismic exploration, identifying the first break (FB) is a critical
component in establishing subsurface velocity models. Various automatic picking
techniques based on deep neural networks have been developed to expedite this
procedure. The most popular class is using semantic segmentation networks to
pick on a shot gather called 2-dimensional (2-D) picking. Generally, 2-D
segmentation-based picking methods input an image of a shot gather, and output
a binary segmentation map, in which the maximum of each column is the location
of FB. However, current designed segmentation networks is difficult to ensure
the horizontal continuity of the segmentation. Additionally, FB jumps also
exist in some areas, and it is not easy for current networks to detect such
jumps. Therefore, it is important to pick as much as possible and ensure
horizontal continuity. To alleviate this problem, we propose a novel semantic
segmentation network for the 2-D seismic FB picking task, where we introduce
the dynamic snake convolution into U-Net and call the new segmentation network
dynamic-snake U-Net (DSU-Net). Specifically, we develop original dynamic-snake
convolution (DSConv) in CV and propose a novel DSConv module, which can extract
the horizontal continuous feature in the shallow feature of the shot gather.
Many experiments have shown that DSU-Net demonstrates higher accuracy and
robustness than the other 2-D segmentation-based models, achieving
state-of-the-art (SOTA) performance in 2-D seismic field surveys. Particularly,
it can effectively detect FB jumps and better ensure the horizontal continuity
of FB. In addition, the ablation experiment and the anti-noise experiment,
respectively, verify the optimal structure of the DSConv module and the
robustness of the picking.
