标题： SeisDiff-deno：一种基于扩散的去噪框架用于VSP数据中的管波衰减 显示英文标题

标题： SeisDiff-deno: A Diffusion-Based Denoising Framework for Tube Wave Attenuation in VSP Data

摘要： 管波在垂直地震剖面数据中是一个重大挑战，常常掩盖来自地震采集的关键地震信号。 在本研究中，我们引入了地震扩散去噪模型，这是一种快速扩散模型，专门设计用于有效去除地震共炮点数据中的噪声。 我们的方法在计算效率和高质量图像去噪之间取得平衡，确保该方法在实际应用中具有实用性和鲁棒性。 我们通过在合成数据和现场数据上的严格测试验证了所提出方法的有效性，证明了其在保留重要地震信号的同时消除不需要的相干噪声的能力。 结果表明，所提出的方法提高了数据质量，并在地震采集过程中支持连续生产，为改善地下监测和分析铺平了道路。 显示英文摘要

摘要： Tube waves present a significant challenge in vertical seismic profiling data, often obscuring critical seismic signals from seismic acquisition. In this study, we introduce the Seismic Diffusion Model for Denoising, a fast diffusion model specifically designed to remove the noise from seismic shotgather effectively. Our approach balances computational efficiency with high-quality image denoising, ensuring that the method is practical and robust for real-world applications. We validate the effectiveness of the proposed method through rigorous testing on both synthetic and field data, demonstrating its capability to preserve essential seismic signals while eliminating unwanted coherent noise. The results suggest that the proposed method enhances data quality and supports continuous production during seismic acquisition, paving the way for improved subsurface monitoring and analysis.