基于水平集方法和U-Net网络的地震初至拾取技术

针对使用U-Net网络拾取地震初至存在的拾取结果不连续等问题，提出了一种融合水平集方法的U-Net网络结构模型。首先基于U-Net网络提取地震图像特征图，逐像素计算损失，然后应用水平集方法对特征图计算水平集损失，最后将两者的加权和作为最终的损失函数。改进后的网络模型采用水平集方法保证了地震道之间初至的连续性，同时保留了U-Net网络“端到端”精细化分类的能力；采用Focal Loss损失函数缓解地震数据样本类别不均衡问题；在训练过程采用混合模拟数据和实际数据的方法克服训练集数据不足问题。经正演模拟和实际地震资料测试结果表明，该网络模型不仅精确度较高，而且较好地适应在低信噪比环境下地震信号的初至拾取。

First-break picking technology based on level set method and U-Net

In order to solve the discontinuous problems in the seismic first-break breaking using the U-Net network, a variantone based on level set method is proposed. Firstly, the feature map of seismic image is extracted based on U-Net network, and the loss is calculated pixel by pixel. Then the level set loss is calculated by using the level set method. Finally, the weighted sum of the two is taken as the final loss function. The improved network uses the level set method to ensure the continuity of first breaks between the adjacent seismic channels, and retains its ability of "end-to-end" fine classification. The Focal Loss function is used to alleviate the imbalance of sample categories of the training set. The mixed training set combined with the synthetic data and the actual field data is used to overcome the training set insufficiency problem. The results tested by the theoretical data and actual seismic data show that the improved U-Net network can improve the picking accuracy and robustness in low signal-to-noise ratio seismic data.