论文标题
水平层限制了对伴有速度拾取的注意力神经网络
Horizontal Layer Constrained Attention Neural Network for Semblance Velocity Picking
论文作者
论文摘要
外观速度分析是地震数据处理的关键步骤。为了避免手动执行巨大的时间成本，提出了一些深度学习方法，以进行自动伴随速度拾取。但是，现有深度学习方法的应用仍受到实践中标签短缺的限制。在这封信中，我们提出了一个注意神经网络，并结合点对点回归速度选择策略来减轻此问题。在我们的方法中，外观贴片和速度值分别用作网络输入和输出。这样，关注神经网络可以有效地提取隐藏在外观贴片中的全球和本地功能。基于水平层提取的下采样策略还旨在提高预测过程中的拾取效率。对合成和现场数据集的测试表明，所提出的方法可以产生合理的结果并保持与标签一致的全局速度趋势。此外，还测试了针对随机噪声的鲁棒性。
Semblance velocity analysis is a crucial step in seismic data processing. To avoid the huge time-cost when performed manually, some deep learning methods are proposed for automatic semblance velocity picking. However, the application of existing deep learning methods is still restricted by the shortage of labels in practice. In this letter, we propose an attention neural network combined with a point-to-point regression velocity picking strategy to mitigate this problem. In our method, semblance patch and velocity value are served as network input and output, respectively. In this way, global and local features hidden in semblance patch can be effectively extracted by attention neural network. A down-sampling strategy based on horizontal layer extraction is also designed to improve the picking efficiency in prediction process. Tests on synthetic and field datasets demonstrate that the proposed method can produce reasonable results and maintain global velocity trend consistent with labels. Besides, robustness against random noise is also tested on the field data.
