SeisDiff-deno: A Diffusion-Based Denoising Framework for Tube Wave Attenuation in VSP Data
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910853703401472 |
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| author | Zhu, Donglin Li, Peiyao Jin, Ge |
| author_facet | Zhu, Donglin Li, Peiyao Jin, Ge |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00637 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SeisDiff-deno: A Diffusion-Based Denoising Framework for Tube Wave Attenuation in VSP Data Zhu, Donglin Li, Peiyao Jin, Ge Geophysics 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. |
| title | SeisDiff-deno: A Diffusion-Based Denoising Framework for Tube Wave Attenuation in VSP Data |
| topic | Geophysics |
| url | https://arxiv.org/abs/2503.00637 |