Artificial Intelligence-Driven Predictive Modeling for Mineral Exploration: Enhancing Resource Estimation through Big Data and Machine Learning

Abstract

The increasing global demand for critical minerals, coupled with the depletion of easily accessible ore deposits and rising exploration costs, has intensified the need for advanced technologies capable of improving the efficiency, accuracy, and reliability of mineral prospecting activities. Traditional mineral exploration methods, which primarily rely on geological interpretation, geochemical surveys, geophysical investigations, and expert-driven decision-making processes, often encounter limitations associated with uncertainty, sparse sampling, high operational expenditures, and prolonged exploration timelines. In recent years, the convergence of artificial intelligence, big data analytics, and machine learning techniques has emerged as a transformative approach for addressing these challenges by enabling the systematic analysis of large, heterogeneous, and multidimensional geological datasets. This study investigates the application of artificial intelligence-driven predictive modeling frameworks for mineral exploration with the objective of enhancing resource estimation, identifying prospective mineralized zones, and supporting informed exploration decision-making. The proposed approach integrates diverse datasets comprising geological maps, remote sensing imagery, geochemical assays, geophysical measurements, drill core information, topographic characteristics, and historical exploration records to develop predictive models capable of recognizing complex spatial relationships and hidden mineralization patterns. Various machine learning algorithms, including random forests, support vector machines, gradient boosting methods, artificial neural networks, and deep learning architectures, are evaluated for their ability to classify mineral potential zones, estimate ore grades, and reduce exploration uncertainty. Feature engineering and dimensionality reduction techniques are employed to improve model interpretability and computational efficiency, while cross-validation procedures ensure robustness and generalizability of predictive outcomes. The findings suggest that artificial intelligence-based models significantly outperform conventional statistical methods in identifying subtle geological signatures associated with mineral deposits and provide more accurate estimations of resource distribution across unexplored terrains. Furthermore, the integration of big data technologies facilitates real-time processing of extensive exploration datasets, enhances scalability, and supports adaptive learning mechanisms capable of continuously refining predictions as new information becomes available. Despite these advantages, challenges related to data quality, model transparency, computational complexity, and transferability across different geological environments remain important considerations requiring further investigation. The study highlights the potential of intelligent predictive systems to revolutionize mineral exploration practices by reducing exploration risks, optimizing resource allocation, minimizing environmental disturbances associated with unnecessary drilling activities, and contributing to sustainable resource management strategies essential for meeting future industrial, technological, and energy transition demands.