{
  "abstract": "Characterizing subsurface fracture networks is challenging because geometry and connectivity are sparsely observed and uncertain away from measurements. We develop a latent diffusion framework for unconditional and observation-conditioned generation of two-dimensional (2D) and three-dimensional (3D) fracture networks. Fractures and the surrounding matrix are represented as binary fields and compressed by a variational autoencoder into compact spatial latent representations, where an Elucidated Diffusion Model learns their distribution. The 3D model operates on native volumetric fields, with a reconstruction objective designed to preserve sparse, thin fracture structures. Sparse borehole observations are incorporated through a conditioning encoder, classifier-free guidance, and diffusion posterior sampling. Generated ensembles reproduce key fracture-network statistics, including fracture area/volume fraction, two-point correlation, and chord-length distributions. Reconstructed fracture counts and gas-production responses are comparable to those of the reference networks under the considered multiphase flow scenarios. Conditional generation satisfies 99.6% of prescribed well observations in 2D. In 3D, where vertical boreholes constrain full voxel columns, conditional realizations satisfy 84.2% of observed well voxels while maintaining fracture volume and geometry comparable to the reference ensemble. These results demonstrate the potential of latent diffusion models as compact probabilistic representations for fracture-network characterization, ensemble generation, and sparse-observation conditioning.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.14412",
  "author_total": 3,
  "authors": [
    {
      "name": "Jiawei Shen"
    },
    {
      "name": "Hongkyu Yoon"
    },
    {
      "name": "Jonghyun Lee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/efjxz3hphgbfm.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.14412",
  "fulltext_url": null,
  "id": "efjxz3hphgbfm",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.14412",
  "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",
      "2212|Reservoir Engineering and Simulation Methods"
    ]
  ],
  "openalex": "W7221778714",
  "pdf_url": "https://arxiv.org/pdf/2610.14412",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/efjxz3hphgbfm.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:06Z",
      "native_id": "2610.14412",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.14412"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.geo-ph",
      "subfield": "geophysics"
    }
  ],
  "title": "Latent Diffusion Modeling of Two- and Three-dimensional Fracture Networks with Sparse Observation Conditioning",
  "title_text": "Latent Diffusion Modeling of Two- and Three-dimensional Fracture Networks with Sparse Observation Conditioning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:06Z",
  "url": "https://research.draconitic.com/paper/efjxz3hphgbfm",
  "venue": "arXiv",
  "versions": []
}