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Velocity picking, a critical step in seismic data processing, has beenstudied for decades. Although manual picking can produce accurate normalmoveout (NMO) velocities from the velocity spectra of prestack gathers, it istime-consuming and becomes infeasible with the emergence of large amount ofseismic data. Numerous automatic velocity picking methods have thus beendeveloped. In recent years, deep learning (DL) methods have produced goodresults on the seismic data with medium and high signal-to-noise ratios (SNR).Unfortunately, it still lacks a picking method to automatically generateaccurate velocities in the situations of low SNR. In this paper, we propose amulti-information fusion network (MIFN) to estimate stacking velocity from thefusion information of velocity spectra and stack gather segments (SGS). Inparticular, we transform the velocity picking problem into a semanticsegmentation problem based on the velocity spectrum images. Meanwhile, theinformation provided by SGS is used as a prior in the network to assistsegmentation. The experimental results on two field datasets show that thepicking results of MIFN are stable and accurate for the scenarios with mediumand high SNR, and it also performs well in low SNR scenarios.

Automatic Velocity Picking Using a Multi-Information Fusion Deep Semantic Segmentation Network