🤖 AI Summary
This study addresses the challenge of strong surface-wave noise in land and vertical seismic profile (VSP) data, which often overlaps significantly with useful reflection signals, limiting the adaptability of conventional denoising methods or rendering them dependent on labeled training data. To overcome this, the authors propose a semantic-guided signal separation approach that, for the first time, leverages a large vision model (LVM)—requiring no task-specific training—to extract surface-wave semantic priors from visual representations of seismic gathers via text or image prompts. These priors are used to generate continuous soft masks, which are then integrated into a mask-constrained low-rank inversion framework solved via the ADMM algorithm for adaptive denoising. The method requires neither annotated data nor fine-tuning and consistently outperforms traditional filtering and implicit neural representation techniques on both synthetic and real VSP datasets, effectively attenuating surface waves while preserving reflection continuity and waveform fidelity.
📝 Abstract
Ground-roll is a dominant source of coherent noise in land and vertical seismic profiling (VSP) data, severely masking reflection events and degrading subsequent imaging and interpretation. Conventional attenuation methods, including transform-domain filtering, sparse representation, and deep learning, often suffer from limited adaptability, signal leakage, or dependence on labeled training data, especially under strong signal-noise overlap. To address these challenges, we propose a training-free framework that reformulates ground-roll attenuation as a semantic-guided signal separation problem. Specifically, a promptable large vision model is employed to extract high-level semantic priors by converting seismic gathers into visual representations and localizing ground-roll-dominant regions via text or image prompts. The resulting semantic response is transformed into a continuous soft mask, which is embedded into a mask-conditioned low-rank inverse formulation to enable spatially adaptive suppression and reflection-preserving reconstruction. An efficient alternating direction method of multipliers (ADMM)-based solver is further developed to solve the proposed inverse problem, enabling stable and physically consistent signal recovery without requiring task-specific training or manual annotation. Extensive experiments on both synthetic and field VSP datasets demonstrate that the proposed method achieves superior ground-roll attenuation while preserving reflection continuity and waveform fidelity, consistently outperforming representative transform-domain filtering and implicit neural representation methods.