Attention Neural Network Semblance Velocity Auto Picking with Reference Velocity Curve Data Augmentation

Semblance velocity analysis plays an indispensable role in seismic data processing. In order to avoid the huge time-cost when performed manually, some deep learning methods are proposed for automatic velocity picking from semblance. However, the application of existing deep learning methods is still restricted by the shortage of labels in practice. To solve this problem, we take semblance velocity analysis as a point-to-point regression problem at each time sample. A time window on semblance which can extract the block corresponding to a time-velocity (t-v) pair and the reference velocity curve (RVC) which can transform semblance randomly are employed together to augment the labeled data. We divide the development of data augmentation strategy into three progressive modes. The datasets from three modes are prepared for training designed attention neural network. The field experiments show that the attention neural network can produce reasonable results and the data augmentation strategy can effectively improve the velocity picking accuracy.