Provenance studies of Au-bearing stream sediments and performance assessment of machine learning-based models: insight from whole-rock geochemistry central Tanzania, East Africa

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dc.contributor.authorAbu, Mahamuda
dc.contributor.authorMvile, Benatus Norbert
dc.contributor.authorKalimenze, John Desderius
dc.date.accessioned2024-08-16T12:32:04Z
dc.date.available2024-08-16T12:32:04Z
dc.date.issued2024
dc.descriptionAbstract. Full-text available at https://link.springer.com/article/10.1007/s12665-024-11419-2
dc.description.abstractThe source of clastic sediments generally, can be traced to their source through provenance studies using the whole rock geochemistry of clastic sediments. However, the provenance of the Au-bearing stream sediments within the central parts of Tanzania is yet to be deciphered. Hence, in this study, to enhance exploration targeting, the source of the Au-bearing stream sediments was characterized using whole-rock geochemistry. The performance of linear regression (LR), decision tree (DT), and polynomial regression (PR) models as prediction models for the Au mineralization in the area, were also compared as additional Au exploration techniques worth exploring in the area. The weathering condition proxies, CIA, ICV, CIW, and PIA as well as discriminant diagrams suggest weakly to intensely weathered sediments. The values of SiO2/Al2O3 and K2O/Al2O3 are indicative of felsic source rocks rather than compositional maturity due to sediments reworking. From Th/Cr, Cr/Th, Th/U, La/Sc, and Th/Sc proxies, the Au-bearing stream sediments are sourced from felsic igneous rocks. These indications are corroborated by the correlation matrix assessment. However, Au is not sourced from the same source rocks as the host sediments due probably, to a prior depositional mixing of the sediments before subsequent transportation to their current depositional environment. With R2 (0.62), MAE (0.6035), MSE (0.6546), and RMSE (0.8091) for LR, R2 (1.0), MAE (0.7500), MSE (1.6273), and RMSE (1.2752) for DT, and R2 (1.0), MAE (2.6608), MSE (12.7840), and RMSE (3.5755), for PR. The LR model performs better in predicting the Au occurrence in the area.
dc.identifier.citationAbu, M., Mvile, B. N., & Kalimenze, J. D. (2024). Provenance studies of Au-bearing stream sediments and performance assessment of machine learning-based models: insight from whole-rock geochemistry central Tanzania, East Africa. Environmental Earth Sciences, 83(3), 105.
dc.identifier.doi10.1007/s12665-024-11419-2
dc.identifier.otherDOI: 10.1007/s12665-024-11419-2
dc.identifier.urihttps://repository.udom.ac.tz/handle/20.500.12661/4628
dc.language.isoen
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofEnvironmental Earth Sciences
dc.subjectProvenance studies
dc.subjectAu-bearing stream sediments
dc.subjectWhole-rock geochemistry
dc.subjectFelsic source rocks
dc.subjectWeathering condition proxies
dc.titleProvenance studies of Au-bearing stream sediments and performance assessment of machine learning-based models: insight from whole-rock geochemistry central Tanzania, East Africa
dc.typeArticle
oaire.citation.issue3
oaire.citation.volume83
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