Integrating multi-proxy geochemistry and machine learning to decode cretaceous palaeoenvironments and tectonic evolution in the Nigerian Middle Benue Trough, West Africa
5 affiliations
- 1Department of Earth Sciences, Kogi State University
- 2Department of Geology, University of Maiduguri
- 3Department of Geosciences, Confluence University of Science and Technology Osara
- 4Department of Geosciences, University of Lagos
- 5Mining Institute, National University of Science and Technology MISiS
Abstract
The Middle Benue Trough, a key segment of the West African Rift, preserves a complex Cretaceous stratigraphy important for understanding basin evolution and hydrocarbon potential. We integrate sedimentological (foraminifera, palynofacies, clay minerals) and geochemical (major oxides, trace elements, REEs including weathering indices such as the chemical index of alteration) proxies in a machine-learning framework. To mitigate overfitting on our modest sample set, we preselected ~ 15 key proxies for analysis. Unsupervised learning (PCA, hierarchical clustering, t-SNE) clearly partitioned the Albian Asu River and Coniacian Awgu shale samples. Geologically, the Asu facies is interpreted as a restricted hypersaline lagoon (high Sr/Cu; abundant agglutinated foraminifera), whereas the Awgu facies records an open-shelf transgression (lower Sr/Cu; higher V/Cr, U/Th; organic-rich shales). These contrasts reflect a warm, humid depositional climate. A Random Forest classifier, evaluated by repeated stratified five-fold cross-validation, achieved ≈ 96% mean accuracy (AUC ≈ 0.98). Feature ranking in RF highlighted the Eu/Eu* anomaly and LREE/HREE ratio among the top predictors, indicating subtle REE signals can be as diagnostic as classical proxies. Other classifiers (SVM, XGBoost, kNN) gave consistent results. We emphasize that these ML analyses provide independent quantitative supports for the interpretation of the results of conventional proxies. Importantly, the models largely supported the conventional Asu/Awgu facies grouping rather than unveiling new depositional types. Bulk geochemistry (CIA ≈ 80–90) and tectonic indicators imply deposition on a rifted passive margin under a humid-to-semiarid climate. This integrated ML approach refines the Benue Trough stratigraphic framework and provides a reproducible, data-driven basis for paleoenvironmental reconstruction and exploration-risk assessment in similar rift basins.
Related
- Sedimentology and geochemistry of the Pleistocene-Holocene fluvial-lacustrine colluvial sedimentary sequence in the Pegalan River section, NW Borneo: Implications for regional tectono-climatic conditions and landscape evolution
- Middle Jurassic–Lower Cretaceous Depositional Facies Evolution and a Fan-Delta–Beach-Bar Model Based on a Case Study from the Anjihai Section in the Southern Junggar Basin
- Late-stage sulfide saturation in Kaikata Seamount magmas (Izu-Bonin arc) constrained by platinum group element geochemistry
- Ultramafic weathering in tropical volcanic arcs: Geochemistry and inverse modeling of rivers draining the Zambales Ophiolite Complex (ZOC), Philippines
- Surface Geochemistry of Natural Hydrogen: Modelling of Soil Artefacts Versus Active Hydrogen Seeping
BibTeX
@article{adamu2026integrating,
title = {{Integrating multi-proxy geochemistry and machine learning to decode cretaceous palaeoenvironments and tectonic evolution in the Nigerian Middle Benue Trough, West Africa}},
author = {L. M. Adamu and A. O. Umaru and B. F. Fatoye and K. G. Ugbena and F. Abubakar and D. O. Obasaju and L. J. Yunusa and A. F. Ohiemi and A. Ebeh and O. B. Isaac},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-63162-2},
url = {https://doi.org/10.1038/s41598-026-63162-2},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrating multi-proxy geochemistry and machine learning to decode cretaceous palaeoenvironments and tectonic evolution in the Nigerian Middle Benue Trough, West Africa
AU - L. M. Adamu
AU - A. O. Umaru
AU - B. F. Fatoye
AU - K. G. Ugbena
AU - F. Abubakar
AU - D. O. Obasaju
AU - L. J. Yunusa
AU - A. F. Ohiemi
AU - A. Ebeh
AU - O. B. Isaac
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-63162-2
UR - https://doi.org/10.1038/s41598-026-63162-2
AB - The Middle Benue Trough, a key segment of the West African Rift, preserves a complex Cretaceous stratigraphy important for understanding basin evolution and hydrocarbon potential. We integrate sedimentological (foraminifera, palynofacies, clay minerals) and geochemical (major oxides, trace elements, REEs including weathering indices such as the chemical index of alteration) proxies in a machine-learning framework. To mitigate overfitting on our modest sample set, we preselected ~ 15 key proxies for analysis. Unsupervised learning (PCA, hierarchical clustering, t-SNE) clearly partitioned the Albian Asu River and Coniacian Awgu shale samples. Geologically, the Asu facies is interpreted as a restricted hypersaline lagoon (high Sr/Cu; abundant agglutinated foraminifera), whereas the Awgu facies records an open-shelf transgression (lower Sr/Cu; higher V/Cr, U/Th; organic-rich shales). These contrasts reflect a warm, humid depositional climate. A Random Forest classifier, evaluated by repeated stratified five-fold cross-validation, achieved ≈ 96% mean accuracy (AUC ≈ 0.98). Feature ranking in RF highlighted the Eu/Eu* anomaly and LREE/HREE ratio among the top predictors, indicating subtle REE signals can be as diagnostic as classical proxies. Other classifiers (SVM, XGBoost, kNN) gave consistent results. We emphasize that these ML analyses provide independent quantitative supports for the interpretation of the results of conventional proxies. Importantly, the models largely supported the conventional Asu/Awgu facies grouping rather than unveiling new depositional types. Bulk geochemistry (CIA ≈ 80–90) and tectonic indicators imply deposition on a rifted passive margin under a humid-to-semiarid climate. This integrated ML approach refines the Benue Trough stratigraphic framework and provides a reproducible, data-driven basis for paleoenvironmental reconstruction and exploration-risk assessment in similar rift basins.
SN - 2045-2322
ER -