Flexible Self-Supervised Surface-Related Multiple Elimination

Surface-related multiples contaminate seismic data and degrade imaging quality. Traditional suppression methods suffer from limited ability or high computational demand, while supervised neural networks require clean training labels which are unavailable for real data. To address these issues, we propose a self-supervised learning framework using a two-stage training strategy with warm-up and iterative data refinement phases. Our method requires only single multi-dimensional convolution to generate synthetic multiples, eliminating dependency on clean labels or velocity models. The network progressively learns to suppress multiples while preserving primary reflections through epoch-based refinement cycles. Validation on real marine seismic data demonstrates effective multiple attenuation. Migration results confirm removal of spurious artifacts and enhanced subsurface imaging. This approach provides a practical, flexible solution for multiple suppression in real-world seismic processing.