  Velocity picking, a critical step in seismic data processing, has been
studied for decades. Although manual picking can produce accurate normal
moveout (NMO) velocities from the velocity spectra of prestack gathers, it is
time-consuming and becomes infeasible with the emergence of large amount of
seismic data. Numerous automatic velocity picking methods have thus been
developed. In recent years, deep learning (DL) methods have produced good
results on the seismic data with medium and high signal-to-noise ratios (SNR).
Unfortunately, it still lacks a picking method to automatically generate
accurate velocities in the situations of low SNR. In this paper, we propose a
multi-information fusion network (MIFN) to estimate stacking velocity from the
fusion information of velocity spectra and stack gather segments (SGS). In
particular, we transform the velocity picking problem into a semantic
segmentation problem based on the velocity spectrum images. Meanwhile, the
information provided by SGS is used as a prior in the network to assist
segmentation. The experimental results on two field datasets show that the
picking results of MIFN are stable and accurate for the scenarios with medium
and high SNR, and it also performs well in low SNR scenarios.
