{
  "abstract": "Denoising is a long-standing and widely-concerned topic in seismic data processing, for it can significantly increase the signal-to-noise ratio of seismic data. Numerous deep learning (DL) methods have shown promising denoising performance, but most of them are task-specific and focus on a certain type of seismic background noise. The real condition that seismic datasets are often contaminated by various types of noises motivates us to explore a well-generalized and versatile DL model for seismic data denoising. Recently, in the fields of computer vision and natural language processing, foundation models (FMs) pre-trained on vast datasets demonstrate outstanding adaptability and generality across diverse downstream tasks. This paradigm offers a promising path to address the challenges faced by task-specific DL denoising models, such as poor generalization, retraining from scratch for different noise, and the lack of labeled data. We first provide a brief review of traditional seismic denoising methods, followed by a comprehensive review of existing DL-based denoising methods categorized by noise type. Furthermore, we conduct a case study on a dedicated seismic denoising foundation model termed SeisDeFM. This is the first study in geophysical research to develop and validate a seismic denoising foundation model on pre-stack gathers with diverse noise conditions. Experimental results demonstrate that, compared with task-specific DL baselines, SeisDeFM achieves superior denoising performance and cross-noise generalization by the advantages of sufficient pre-training and appropriate downstream adaptation, and it effectively preserves weak reflection events while suppressing complex noise in pre-stack seismic data.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.34530",
  "author_total": 6,
  "authors": [
    {
      "name": "Xintong Dong"
    },
    {
      "name": "Zhengyi Yuan"
    },
    {
      "name": "Changxin Wei"
    },
    {
      "name": "Wenshuo Yu"
    },
    {
      "name": "Shiqi Dong"
    },
    {
      "name": "Jun Lin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1g62jccj3a9q5.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.34530",
  "fulltext_url": null,
  "id": "1g62jccj3a9q5",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.34530",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.geo-ph"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.34530",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1g62jccj3a9q5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:12Z",
      "native_id": "2609.34530",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.34530"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.geo-ph",
      "subfield": "geophysics"
    }
  ],
  "title": "A Review of Deep-learning-based Seismic Data Denoising and Its Promising Paradigm Shift to Foundation Models",
  "title_text": "A Review of Deep-learning-based Seismic Data Denoising and Its Promising Paradigm Shift to Foundation Models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:12Z",
  "url": "https://research.draconitic.com/paper/1g62jccj3a9q5",
  "venue": "arXiv",
  "versions": []
}