Noise is All You Need: rethinking the value of noise on seismic denoising via diffusion models
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866918331655651328 |
|---|---|
| author | Zhu, Donglin Li, Peiyao Jin, Ge |
| author_facet | Zhu, Donglin Li, Peiyao Jin, Ge |
| contents | We introduce SeisDiff-denoNIA, a diffusion-based seismic denoising framework that trains directly on noise extracted from field data, eliminating the dependence on synthetic datasets. Unlike conventional denoising methods that require clean signal labels, our approach uses field noise recorded as training targets, enabling the diffusion model to explicitly learn the true noise distribution. We further demonstrate the framework on a field DAS-VSP survey, where the model effectively suppresses multiple types of noise, including production, instrument, and environment related noise, while preserving key seismic events. By denoising shot gathers with SeisDiff-denoNIA prior to migration, the resulting images exhibit improved event continuity and enhanced reflection visibility without artificial artifacts. In addition, synthetic experiments show that the method substantially outperforms traditional signal-based diffusion models under low-SNR conditions. These results suggest that explicitly modeling noise is not only viable but advantageous for a broad class of seismic denoising tasks, particularly in challenging field environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03629 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Noise is All You Need: rethinking the value of noise on seismic denoising via diffusion models Zhu, Donglin Li, Peiyao Jin, Ge Geophysics We introduce SeisDiff-denoNIA, a diffusion-based seismic denoising framework that trains directly on noise extracted from field data, eliminating the dependence on synthetic datasets. Unlike conventional denoising methods that require clean signal labels, our approach uses field noise recorded as training targets, enabling the diffusion model to explicitly learn the true noise distribution. We further demonstrate the framework on a field DAS-VSP survey, where the model effectively suppresses multiple types of noise, including production, instrument, and environment related noise, while preserving key seismic events. By denoising shot gathers with SeisDiff-denoNIA prior to migration, the resulting images exhibit improved event continuity and enhanced reflection visibility without artificial artifacts. In addition, synthetic experiments show that the method substantially outperforms traditional signal-based diffusion models under low-SNR conditions. These results suggest that explicitly modeling noise is not only viable but advantageous for a broad class of seismic denoising tasks, particularly in challenging field environments. |
| title | Noise is All You Need: rethinking the value of noise on seismic denoising via diffusion models |
| topic | Geophysics |
| url | https://arxiv.org/abs/2509.03629 |