{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-anti-noise-seismic-inversion-method-based","title":"An anti-noise seismic inversion method based on diffusion model","arxiv_id":"2405.05026","date":"2024-05-08","proceeding":null,"authors":["Yingtian Liu","Yong Li","Xingan Hao","Huating Li","Zhangquan Liao","Junheng Peng"],"abstract":"Seismic impedance inversion is one of the most important part of geophysical exploration. However, due to random noise, the traditional semi-supervised learning (SSL) methods lack generalization and stability. To solve this problem, some authors have proposed SSL methods with anti-noise function to improve noise robustness and inversion accuracy. However, such methods are often not ideal when faced with strong noise. In addition, Low-frequency impedance models can mitigate this problem, but creating accurate low-frequency models is difficult and error-prone when well-log data is sparse and subsurface structures are complex. To address those issues, we propose a novel deep learning inversion method called DSIM-USSL (Unsupervised and Semi-supervised joint Learning for Seismic Inversion based on diffusion model). Specifically, we are the first to introduce a diffusion model with strong noise tendency and construct a diffusion seismic inversion model (DSIM). In the reverse diffusion of DSIM, we design the encoder-decoder which combines CNN for capturing local features and GRU for global sequence modeling; and we choose U-net to learn the distribution of random noise, enhancing the generalization and stability of proposed method. Furthermore, to further improve generalization of the proposed method, a two-step training approach (USSL) is utilized. First, an unsupervised trained encoder-decoder is used as the initial network model in place of the traditional low-frequency wave impedance model that is difficult to accurately acquire. Then, the SSL is employed to further optimize the encoder-decoder model. Experimental results on the Marmousi2 model and field data demonstrate that the DSIM-USSL method achieves higher accuracy in the presence of seismic data with random noise, and maintains high stability even under strong noise conditions.","url_abs":"https://arxiv.org/abs/2405.05026v1","url_pdf":"https://arxiv.org/pdf/2405.05026v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"an-anti-noise-seismic-inversion-method-based","repo_url":"https://github.com/goodlikewater/DSIM-USSL","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}