{
  "abstract": "Automated delineation of salt structures from three-dimensional seismic data remains a fundamental challenge in structural interpretation, constrained by severe scarcity of expert-labeled training data and the persistent tendency of segmentation networks to misclassify acquisition noise as salt boundaries. We present the first application of unfactorized three-dimensional global self-attention to seismic salt dome segmentation, within a hybrid UNet combining volumetric convolution with complete spatial context modeling. The model is trained with self-supervised denoising pretext training across three unlabeled surveys, followed by discriminative transfer learning on binary Zechstein salt annotations from the Netherlands F3 block using layer-wise learning rate decay, gradual unfreezing, and Unified Focal Loss. This pretext task develops explicit noise-discrimination representations before salt-specific supervised adaptation; in a controlled comparison under the identical architecture, it yielded a larger gain over random initialization than contrastive (Barlow Twins-style) or masked-reconstruction pretext tasks. Validation on the F3 dataset achieved Dice = 0.843, IoU = 0.729, Precision = 0.831, and MCC = 0.829, representing statistically significant improvements of 5.0–9.2% over the strongest comparison model, the 3D CNN + Swin architecture, and exceeding a Salt3DNet-style self-supervised reference model on all six metrics. On the spatially distinct blind test partition containing a large Zechstein diapir, performance gaps over Swin widened to 10.6–18.1%, with the proposed model exhibiting substantially lower degradation (3.7% Dice reduction) versus Swin (8.6%) and the baseline CNN (11.7%). The proposed model remains compact and memory-efficient, with 3.43M parameters (3.18M for the baseline) and only 0.9 GB of GPU memory for training thanks to fused attention, while exact global attention over the full 64 3 sub-volume requires 6.8 TFLOPs per forward pass. Zero-shot cross-basin evaluation on the recently released Gulf of America OCS volume (NAMSS survey B-51-94-LA), assessed qualitatively on a survey with no prior salt interpretation, provides the first automated structural delineation of Louann Salt diapir geometries in this survey; the results are qualitatively consistent with generalization to geologically distinct salt morphologies and constitute an original geoscientific contribution to one of the world's most tectonically significant salt provinces.",
  "abstract_shown": true,
  "affiliations": [
    "Corresponding author."
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Matin Mahzad"
    },
    {
      "affs": [
        0
      ],
      "name": "Majid Bagheri"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cz5pf39tb2n2t.bib",
  "cited_by": 0,
  "doi": "10.1016/j.acags.2026.100424",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S2590197426001084",
  "id": "cz5pf39tb2n2t",
  "issns": [
    "2590-1974"
  ],
  "landing_url": "https://doi.org/10.1016/j.acags.2026.100424",
  "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",
      "G"
    ],
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      "lcc",
      "QA75.5-76.95"
    ],
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      "lcc",
      "QE1-996.5"
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    [
      "openalex",
      "1908|Seismic Imaging and Inversion Techniques"
    ]
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  "openalex": "W7167260641",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-08",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/cz5pf39tb2n2t.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T14:20:47Z",
      "native_id": "0c9763ab36fa4944880d6b129330c740",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.acags.2026.100424"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1908|Seismic Imaging and Inversion Techniques",
      "subfield": "geophysics"
    }
  ],
  "title": "3D seismic salt dome segmentation using a hybrid UNet with global attention, denoising pretraining, and discriminative transfer learning",
  "title_text": "3D seismic salt dome segmentation using a hybrid UNet with global attention, denoising pretraining, and discriminative transfer learning",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T14:20:47Z",
  "url": "https://research.draconitic.com/paper/cz5pf39tb2n2t",
  "venue": "Applied Computing and Geosciences",
  "versions": []
}