{
  "abstract": "Full-waveform inversion (FWI) requires accurate initial velocity models to avoid cycle-skipping, but constructing such models remains challenging in practice. Building on the depth-progressive diffusion framework introduced in Part~I, which relied on idealized reflectivity constraints, this work adapts the methodology to realistic exploration scenarios. We replace perfect structural information with migration-derived attributes extracted from seismic images, and introduce smooth background velocity models from tomography as additional conditioning inputs. The framework jointly leverages background/migration velocity, migrated structural information, and sparse well measurements to synthesize high-resolution velocity models through depth-progressive generation. Validation on synthetic examples demonstrates superior accuracy compared to conventional interpolation and alternative deep learning methods, with generated models successfully initializing FWI and mitigating cycle-skipping even in complex geological structures. Field data confirms practical applicability: despite training on synthetic data, the method generalizes effectively to field conditions, producing velocity models with synthetic data response that nearly match observed seismic data. As a result, this framework establishes a practical pathway to deploy generative diffusion models for velocity model building under realistic constraints.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.26482",
  "author_total": 3,
  "authors": [
    {
      "name": "Shijun Cheng"
    },
    {
      "name": "Randy Harsuko"
    },
    {
      "name": "Tariq Alkhalifah"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2e8d80e1108ae.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.26482",
  "fulltext_url": null,
  "id": "2e8d80e1108ae",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.26482",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.geo-ph"
    ],
    [
      "openalex",
      "1908|Seismic Imaging and Inversion Techniques"
    ]
  ],
  "openalex": "W7214118956",
  "pdf_url": "https://arxiv.org/pdf/2609.26482",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2e8d80e1108ae.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:39Z",
      "native_id": "2609.26482",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.26482"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.geo-ph",
      "subfield": "geophysics"
    }
  ],
  "title": "Shallow-to-deep velocity model building via diffusion models-Part II: Realistic scenarios",
  "title_text": "Shallow-to-deep velocity model building via diffusion models-Part II: Realistic scenarios",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:39Z",
  "url": "https://research.draconitic.com/paper/2e8d80e1108ae",
  "venue": "arXiv",
  "versions": []
}