A Data-Physics Dual-Constraint Framework for Intelligent Surface Wave Suppression

High-energy surface waves (ground roll) are a major source of coherent noise in land seismic data, often overlapping with reflections and degrading subsurface imaging quality. We propose an intelligent surface-wave suppression method based on a dual-constraint framework that integrates data-driven supervision with a physics-guided prior. A composite loss is constructed with (1) a data constraint in the time–space (t–x) domain, implemented as a supervised loss that compares the network output with the labeled targets, and (2) a physics constraint in the frequency–velocity (f–v) domain, where the surface-wave dispersion curve is exploited to delineate the physically plausible ground-roll region and to penalize residual energy inconsistent with the dispersion curve. We train UNet and TransUNet architectures on field datasets using this composite objective. Compared with purely data-driven training, the proposed dual-constraint loss reduces the dependence on potentially imperfect labels by enforcing dispersion-consistent behavior in the f–v domain, leading to lower residual surface-wave energy while maintaining reflection continuity. These results demonstrate that incorporating physically meaningful constraints into modern network architectures can improve robustness under imperfect supervision and enhance intelligent seismic surface-wave suppression.