Large Vision Model-Guided Masked Low-Rank Approximation for Ground-Roll Attenuation
Ground roll is a common type of coherent noise in seismic records, and its attenuation remains challenging due to its substantial overlap with useful reflections in localized regions. Existing attenuation methods can be broadly classified into global and local categories according to whether ground-roll-contaminated regions are explicitly identified. Global methods, however, typically impose uniform attenuation on both contaminated and uncontaminated regions, which may result in signal leakage or distortion of reflections. By contrast, local methods restrict attenuation to contaminated regions and are therefore less prone to unnecessary modification of clean areas. However, their performance is often limited by manually designed or simplistic model-based mask estimation strategies. To address these limitations, we propose a large vision model-guided masked low-rank approximation (LVM-LRA) framework for ground-roll attenuation. Within this framework, a promptable LVM is first employed to identify ground-roll-dominant regions in seismic records through multimodal prompting and to generate accurate, fine-grained masks. The estimated masks are then incorporated into an LRA model for ground-roll attenuation. A global low-rank constraint is imposed on the reflection component to preserve event continuity, whereas a mask-guided local low-rank constraint is imposed on the ground-roll component so that its separation is confined to the masked regions. An iterative optimization algorithm based on the alternating direction method of multipliers (ADMM) is further developed to solve the resulting model efficiently. Experiments on synthetic and field datasets demonstrate that the proposed method achieves more effective ground-roll attenuation and better suppresses signal leakage than the baseline methods.