Customizing Large Vision Model-Guided Low-Rank Approximation for Ground-Roll Denoise

• URL: http://arxiv.org/abs/2604.00998v1
• Date: Wed, 01 Apr 2026 14:59:48 GMT
• Title: Customizing Large Vision Model-Guided Low-Rank Approximation for Ground-Roll Denoise
• Abstract summary: Ground-roll is a dominant source of coherent noise in land and vertical seismic profiling (VSP) data.<n>We propose a training-free framework that reformulates ground-roll attenuation as a semantic-guided signal separation problem.
• Score: 4.536244262111516
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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