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<p>Geochemical exploration has been an indispensable approach to mineral exploration and has been responsible for significant mineralization discoveries across the globe. With the increasingly difficult exploration targeting, there is a need for an add-on in the geochemical data processing techniques that has the qualities of enhancing exploration targeting even with the slightest of geochemical signatures. In recent years, machine learning methods coupled with multivariate statistical methods have gradually proven to be a good add-on to the geochemical data processing techniques, which are yielding positive exploration targeting results. Although similar studies aimed at enhancing exploration targeting have been conducted in some parts of the Tanzania Craton (TC) using the same approach, these earlier works did not include the sensitivity analysis of the mineralization characteristics within the TC. Hence, this study seeks to trace the potential source(s) of Au-Pt-Pd in stream sediments, their pathfinder elements, and the most sensitive elements to their occurrence in the study area. The study adopted multivariate statistics together with regression modeling, artificial neural network (ANN), and sensitivity analysis methods. The elemental association of Sr, Ba, Nb, and Rb suggests felsic igneous rocks, and Co, Ni, V, and Cr indicate mafic rock sources of the Au-Pd-Pt. The most sensitive elements to the Au occurrence in the area are Zn, Bi, Sr, Ca, K, Sn, Y, and Nb, while those for Pd are Au and Pt. The prediction model for Au performed better with RE values of 0.787 and 0.576 in training and testing stages, respectively. The geological complexity of the TC might have accounted for the variable pathfinder elements of Au within the different Au-bearing catchments.</p> Graphical Abstract <p></p> <p>From the graphical illustration of the workflow, the study was conducted with the aim of elucidating the source of Au-Pd-Pt in stream sediments in the central parts of Tanzania. A total of 126 stream sediment samples were collected. This sample size includes 9 samples for QC and QA purposes. The samples were analyzed using the ICP-MS analytical method. The samples were processed using SPSS and ArcGIS to reveal trends, elemental associations and clusters, patterns, and spatial distribution. Multivariate statistics, multivariate linear regression modeling, and artificial neural networks were combined to bring out insight and hidden patterns and associations, as well as the sensitivity of the trace elements to the occurrences of the Au-Pd-Pt mineralization in the area.</p>

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Multivariate Statistics and Machine Learning Techniques as Tools for Exploration Targeting in Parts of the Tanzania Craton: Insights from Stream Sediments Geochemistry

  • 期刊论文

  • 发表日期：

    2026年5月12日

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  • Samuel Nunoo,
  • Benatus Norbert Mvile,
  • John Desderius Kalimenze,
  • Mahamuda Abu

摘要

Geochemical exploration has been an indispensable approach to mineral exploration and has been responsible for significant mineralization discoveries across the globe. With the increasingly difficult exploration targeting, there is a need for an add-on in the geochemical data processing techniques that has the qualities of enhancing exploration targeting even with the slightest of geochemical signatures. In recent years, machine learning methods coupled with multivariate statistical methods have gradually proven to be a good add-on to the geochemical data processing techniques, which are yielding positive exploration targeting results. Although similar studies aimed at enhancing exploration targeting have been conducted in some parts of the Tanzania Craton (TC) using the same approach, these earlier works did not include the sensitivity analysis of the mineralization characteristics within the TC. Hence, this study seeks to trace the potential source(s) of Au-Pt-Pd in stream sediments, their pathfinder elements, and the most sensitive elements to their occurrence in the study area. The study adopted multivariate statistics together with regression modeling, artificial neural network (ANN), and sensitivity analysis methods. The elemental association of Sr, Ba, Nb, and Rb suggests felsic igneous rocks, and Co, Ni, V, and Cr indicate mafic rock sources of the Au-Pd-Pt. The most sensitive elements to the Au occurrence in the area are Zn, Bi, Sr, Ca, K, Sn, Y, and Nb, while those for Pd are Au and Pt. The prediction model for Au performed better with RE values of 0.787 and 0.576 in training and testing stages, respectively. The geological complexity of the TC might have accounted for the variable pathfinder elements of Au within the different Au-bearing catchments.

Graphical Abstract

From the graphical illustration of the workflow, the study was conducted with the aim of elucidating the source of Au-Pd-Pt in stream sediments in the central parts of Tanzania. A total of 126 stream sediment samples were collected. This sample size includes 9 samples for QC and QA purposes. The samples were analyzed using the ICP-MS analytical method. The samples were processed using SPSS and ArcGIS to reveal trends, elemental associations and clusters, patterns, and spatial distribution. Multivariate statistics, multivariate linear regression modeling, and artificial neural networks were combined to bring out insight and hidden patterns and associations, as well as the sensitivity of the trace elements to the occurrences of the Au-Pd-Pt mineralization in the area.