{
  "abstract": "Accurately characterizing subsurface lithological heterogeneity in tropical granitic terrains remains challenging because conventional electrical resistivity tomography (ERT) and induced polarization (IP) inversions often smooth abrupt boundaries, obscure weathered interfaces, and underrepresent clay–moisture contrasts. This study develops an integrated geophysical framework for approximating inversion-derived chargeability patterns using depth-referenced ERT–IP collocation, conservative outlier filtering, and stratified sampling to construct a robust dataset representative of residual soils, weathered and fractured granite, and moisture-bearing weak zones. Machine learning and deep learning (ML/DL) methods were applied to capture nonlinear resistivity–IP relationships governing lithological transitions. CatBoost achieved the strongest standalone performance (R 2 = 0.942 train; 0.945 test), marginally surpassing random forest, while the stacked ensemble achieved the highest predictive performance (test R 2 = 0.947). The one-dimensional convolutional neural network with global average pooling performed comparably to the artificial neural network while effectively representing depth-dependent resistivity–IP relationships. All ML/DL models yielded RMSE ≈ 0.76–1.09 msec (< 10%), confirming high predictive fidelity. ML/DL-derived and CatBoost-regressed 2D chargeability models reproduced inversion-defined structures and preserved the spatial expression of clay-rich horizons, fracture-controlled weak zones, and lithological interfaces. The results are relevant to geoengineering assessments, where relatively competent bedrock generally occurs below ~ 30–33 m and may provide favorable conditions for deep foundation development, subject to geotechnical verification. They also provide useful insights for hydrogeological evaluations by identifying weathered/fractured zones extending to ~ 41 m that may represent favorable groundwater targets. K-means clustering analysis (KMCA) further supported lithological discrimination by grouping materials into coherent residual-soil-to-weathered-granite domains. The integrated inversion–ML/DL–KMCA workflow provides a cost-effective and geologically consistent framework for approximating inversion-derived chargeability patterns and supporting subsurface characterization in complex crystalline terrains.",
  "abstract_shown": true,
  "affiliations": [
    "Earth System Processes and Hazard Modeling Center, Geophysics Programme, School of Physics, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia",
    "Faculty of Environmental & Urban Change, York University, 4700 Keele Street, Toronto, ON, M3J 1P3, Canada",
    "Geophysics Programme, School of Physics, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia",
    "Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences, 14473, TelegrafenbergPotsdam, Brandenburg, Germany",
    "Department of Computer Science, University of Fredericton, Fredericton, NB, E3B 6Z6, Canada",
    "Department of Chemical and Environmental Engineering, University of Cincinnati, Cincinnati, OH, USA",
    "Department of Geology, Federal University of Technology, Owerri, 1526, Imo, Nigeria",
    "Department of Mathematical and Physical Sciences, Miami University, Oxford, OH, USA",
    "Department of Physics, Federal University Gashua, Nguru Road, Gashua, Yobe, Nigeria",
    "Akwa Ibom State Polytechnic, 1200 Ikot Osurua, Ikot Ekpene, Akwa Ibom, Nigeria"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Adedibu Sunny Akingboye",
      "orcid": "0000-0003-2195-6098"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Andy Anderson Bery",
      "orcid": "0000-0001-8974-9808"
    },
    {
      "affs": [
        3
      ],
      "name": "Hui Tang",
      "orcid": "0000-0002-3384-8046"
    },
    {
      "affs": [
        4
      ],
      "name": "Ayokunle Olalekan Ige",
      "orcid": "0000-0001-5155-9304"
    },
    {
      "affs": [
        5,
        6,
        7
      ],
      "name": "Obinna Chigoziem Akakuru",
      "orcid": "0000-0002-2491-6481"
    },
    {
      "affs": [
        8,
        0,
        2
      ],
      "name": "G.A. Bala",
      "orcid": "0000-0002-6250-2044"
    },
    {
      "affs": [
        9,
        0,
        2
      ],
      "name": "Mbuotidem David Dick",
      "orcid": "0000-0003-2505-1747"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ej3n3b7zaggae.bib",
  "cited_by": 5,
  "doi": "10.1038/s41598-026-62674-1",
  "fulltext_url": null,
  "id": "ej3n3b7zaggae",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-62674-1",
  "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": [
    [
      "openalex",
      "1908|Geophysical and Geoelectrical Methods"
    ]
  ],
  "openalex": "W7203637613",
  "pdf_url": "https://www.nature.com/articles/s41598-026-62674-1_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-17",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/ej3n3b7zaggae.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T13:21:09Z",
      "native_id": "W7203637613",
      "source": "openalex",
      "url": "https://doi.org/10.1038/s41598-026-62674-1"
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  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1908|Geophysical and Geoelectrical Methods",
      "subfield": "geophysics"
    }
  ],
  "title": "Improving subsurface characterization in tropical granitic terrains using machine learning and deep learning-enhanced resistivity–chargeability modeling",
  "title_text": "Improving subsurface characterization in tropical granitic terrains using machine learning and deep learning-enhanced resistivity–chargeability modeling",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T13:20:41Z",
  "url": "https://research.draconitic.com/paper/ej3n3b7zaggae",
  "venue": "Scientific Reports",
  "versions": []
}