A Review of Machine Learning-Based Geochemical Signature Analysis for Mineral Prospectivity Mapping

Aug 4, 2026

Geochemical signature analysis remains foundational to mineral geology, yet capturing multielement signals with traditional statistical tools becomes increasingly difficult under modern exploration demands. The review synthesizes how machine learning algorithms shift geochemical prospectivity mapping through advanced pattern recognition and predictive modeling. It covers theoretical foundations, supervised and unsupervised learning methods, and application across geological environments, highlighting challenges such as data preprocessing, model interpretability, and generalization.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages24

Reading Time1 h 0 min

AccessFree to download

Introduction

Machine learning and AI in geoscientific research

Geochemical surveys and traditional interpretation

Why machine learning transforms geochemical analysis

A Review of Machine Learning-Based Geochemical Signature Analysis for Mineral Prospectivity Mapping Akintunde Stephen Samakinde & Vincent Bailey Arohunmolase. Received: 16 October 2025/Accepted: 25 January 2026 /Published: 30 January 2026 [https://dx.doi.org/10.4314/cps.v13i1.4](https://dx.doi.org/10.4314/cps.v13i1.4) Abstract: Geochemical signature analysis to exploration in both developed and frontier has been a basic technique of mineral geology and responding to the pressing exploration over the years, but the nonlinear demand of new mineral resources in an era of and complicated nature of multi-element energy transition. geochemical data has proven hard to capture using traditional tools of statistical analysis. Keywords: Metallogenic province, ML This is because the incorporation of machine algorithms, exploration, geochemistry and learning algorithms into geochemical prospectivity map. analysis is a paradigm shift that will allow Akintunde Stephen Samakinde more sophisticated pattern recognition and Michigan Technological University, predictive modeling of mineral prospectivity Michigan, USA maps. This review summarizes the existing Email: [assamaki@mtu.edu](assamaki@mtu.edu) information on machine learning as applied [https://orcid.org/0009-0009-8144-997X](https://orcid.org/0009-0009-8144-997X)[ ](https://orcid.org/0009-0009-8144-997X)to the geochemical signature analysis, including the theoretical basis of the method, Vincent Bailey Arohunmolase algorithms, and application in different Michigan Technological University, geological environments. We delve into how Michigan, USA supervised approaches to learning, [including](including Email: vbarohun@mtu.edu)[ Email: ](including Email: vbarohun@mtu.edu)[vbarohun@mtu.edu](including Email: vbarohun@mtu.edu) Random Forest, Support Vector Machines, [https://orcid.org/0009-0001-6210-1523](https://orcid.org/0009-0001-6210-1523)[ ](https://orcid.org/0009-0001-6210-1523)and neural networks, have revolutionized the 1.0 Introduction field of anomaly detection and target Machine learning (ML) and artificial generation and unsupervised approaches to intelligence (AI) are increasingly learning, including clustering algorithms and transforming geoscientific research by dimensionality reduction procedures, are enabling the analysis of complex, highused to discover the unknown geochemical dimensional datasets that exceed the worlds. A review is done of the successful capabilities of conventional statistical tools. case studies using various types of deposits In mineral exploration, these approaches and in geological environments with a focus support the identification of subtle, on uses in underexplored areas such as multivariate geochemical patterns linked to African metallogenic provinces. The concealed mineralization, improving the problematic issues, such as the complexity of predictive accuracy of prospectivity mapping data preprocessing, the interpretability ofthe (Ademilua, 2021) . Their integration models, and the ability to generalize and facilitates innovative methods for real-time apply the models to various geological analysis and automated decision-making settings are addressed. New directions in across sectors (Ufomba & Ndibe, 2023) . AI architecture, like deep learning and and ML reshape research by processing large explainable artificial intelligence, as well as datasets and enhancing autonomous multi-source data integration, are also performance (Ndibe, 2024) . The widespread indicative of more advanced exploration adoption of these tools supports intelligent processes. This detailed discussion shows frameworks that strengthen analytical that geochemical analysis based on machine precision and operational efficiency (Sanni, learning does not only increases the level of 2024) . By enabling intelligent automation target identification but also redefines the and data-driven reasoning, they offer principles of exploration, providing avenues t

FAQs

为什么传统统计分析在地球化学找矿中难以满足需求？

地球化学多元素数据往往噪声大、具有组成性且存在空间相关性；传统统计方法通常依赖单变量阈值与人工解释，难以反映多变量复杂性与元素关系。

机器学习如何用于地球化学特征分析与成矿远景预测？

机器学习可在不预先假设数据分布的情况下，挖掘高维复杂模式，从而提升成矿远景制图的预测能力与目标识别的精度，并支持更自动化、数据驱动的推理。

综述重点讨论哪些机器学习方法与应用场景？

综述讨论监督学习与无监督学习，包括随机森林、支持向量机和神经网络，并结合不同地质环境与案例研究，尤其关注欠探明区域如非洲成矿省的应用。

Aug 4, 2026

Upload by Valentina