  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.
