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.

在地震勘探中，我们提出了一种新的语义分割网络（DSU-Net），通过引入动态蛇卷积到 U-Net 中，在 2D 地震首波捕捉任务中实现了更高的准确性和鲁棒性，实现了首波跳跃的有效检测和更好的水平连续性。

DSU-Net: 2D 地震初始震相采集的动态蛇 U-Net

DSU-Net: Dynamic Snake U-Net for 2-D Seismic First Break Picking

In seismic exploration, the selection of first break times is a crucial
aspect in the determination of subsurface velocity models, which in turn
significantly influences the placement of wells. Many deep neural network
(DNN)-based automatic first break picking methods have been proposed to speed
up this picking processing. However, there has been no work on the uncertainty
of the first picking results of the output of DNN. In this paper, we propose a
new framework for first break picking based on a Bayesian neural network to
further explain the uncertainty of the output. In a large number of
experiments, we evaluate that the proposed method has better accuracy and
robustness than the deterministic DNN-based model. In addition, we also verify
that the uncertainty of measurement is meaningful, which can provide a
reference for human decision-making.

本文介绍了基于贝叶斯神经网络的地震勘探首波拾取新框架。新框架在大量实验中得到了验证，具有更好的准确性和稳健性，并且此拾取结果的不确定性对人类决策提供了参考。