{
  "abstract": "Natural fracture networks govern subsurface fluid flow, rock-mass stability, and strain accommodation in the brittle crust, yet their automated delineation from outcrop imagery remains challenging due to multi-scale size variability, low contrast between fracture boundaries and host-rock texture, and scene clutter from vegetation, shadows, and blast artifacts. Standard encoder-decoder networks apply fixed-size receptive fields that inadequately span the fractal-like scale range of natural discontinuity networks, while gradient-based classical detectors lack the semantic context required to suppress non-structural scene edges. We introduce GeoFractNet: a dilated U-Net coupling ConvNeXt encoder blocks with multi-scale dilated convolutions ( $$d \\in \\{1,\\!2,\\!4,\\!8\\}$$ ), gated edge-aware skip connections fusing Scharr and Gabor filter responses, and a Sobel Edge-Aware (SEA) Loss that directly penalizes gradient misalignment at fracture boundaries. The model output is a binary semantic edge map (per-pixel classification of fracture-boundary pixels versus background) rather than a vectorized fracture trace network; conversion to discrete trace objects requires subsequent post-processing. Trained on GeoCrack, an open-access fracture edge dataset of 12,158 annotated patches from 49 structurally and lithologically diverse outcrops across five countries, GeoFractNet achieves mIoU $$= 0.91$$ , Dice $$= 0.92$$ , and Boundary F $$_1 = 0.90$$ , outperforming all evaluated classical and deep-learning baselines. Compared to the best performing network in literature, YOLACT++, GeoFractNet reduces false positives by 38% and recovers 94% of long-range fracture edges missed by classical (Canny) edge detection on a curated hard-negative evaluation subset (420 patches containing vegetation, blast holes, and strong illumination gradients). Ablation studies confirm that the gated edge-aware skip connections and SEA Loss provide the largest incremental gains in boundary-sensitive metrics, with no architectural component degrading performance at any ablation step. Code, model weights, and the GeoCrack dataset are openly released, providing a reproducible benchmark for automated fracture characterization in structural geology, geomechanics, and reservoir analogue studies.",
  "abstract_shown": true,
  "affiliations": [
    "Electrical and Computer Engineering, Texas A&M University",
    "Petroleum Engineering, Texas A&M University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mohammed Yaqoob"
    },
    {
      "affs": [
        0
      ],
      "name": "Mohammed Ishaq"
    },
    {
      "affs": [
        0
      ],
      "name": "Mohammed Yusuf Ansari"
    },
    {
      "affs": [
        1
      ],
      "name": "Thomas Daniel Seers"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0p7mhp460xpnn.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-58908-x",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-58908-x",
  "id": "0p7mhp460xpnn",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-58908-x",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1707|Advanced Neural Network Applications"
    ]
  ],
  "openalex": "W7169883296",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/0p7mhp460xpnn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:58Z",
      "native_id": "a3b4e39704d34af9b13b19a00d2dbcee",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-58908-x"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Semantic edge detection of fractures in geological outcrops using edge aware dilated convolutional networks",
  "title_text": "Semantic edge detection of fractures in geological outcrops using edge aware dilated convolutional networks",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:58Z",
  "url": "https://research.draconitic.com/paper/0p7mhp460xpnn",
  "venue": "Scientific Reports",
  "versions": []
}