Research on Automatic Velocity Spectrum Picking Algorithm for Seabed Multiples Based on Deep Learning

Multiples often dominate semblance (velocity) spectra and bias-stacking velocity picks, degrading NMO correction and subsequent imaging. We propose FLD–PA, a practical two-stage workflow for automatic velocity-spectrum picking under strong multiple interference. First, the feature-level decoupling detector (FLD) uses an attention-enhanced, YOLOv4-style architecture to localize sparse key picks while suppressing multiple-related clutter. Second, the physics-informed Point Adjustment (PA) module refines coarse picks by enforcing lateral continuity across adjacent spectra and time consistency constraints derived from the stacked section. This refinement yields a geophysically plausible velocity trend. Experiments on two real datasets from a single offshore survey (with non-overlapping CMP/line subsets) show that FLD–PA improves PA@10px from 91.50% to 93.14% and reduces RMSE from 12.40 to 10.15 pixels compared with a YOLOv8–LSTM baseline. Under a matched-recall setting (≈81%), we tune confidence thresholds on a held-out validation subset and evaluate both methods at the same recall. FLD–PA achieves PA@10px = 93.14% with RMSE = 10.15 pixels, compared with 91.50% and 12.40 pixels for YOLOv8–LSTM. Overall, FLD–PA improves the accuracy and stability of velocity picking under strong multiple interference. However, our evaluation focuses on within-survey robustness; cross-survey generalization remains for future work.