Abstract:

Machine learning (ML) and artificial intelligence (AI) are transforming the mining industry, particularly in exploration. Traditional methods often rely on manual data interpretation, which can be time-consuming and prone to errors. ML and AI can process large datasets, identify patterns, and make predictions, enhancing exploration efficiency and accuracy. This review examines the applications and potential of ML and AI in mining exploration. This review explores the applications and potential of machine learning and artificial intelligence in mining exploration. Findings from this study revealed the successful application of ML and AI in mineral prospectivity mapping, ore reserve estimation, and geochemical anomaly detection. Techniques like convolutional neural networks and random forests improve mineral exploration targeting and reduce uncertainty. ML algorithms can integrate multiple datasets, including geological, geophysical, and geochemical data, to identify potential mineral deposits. AI-powered systems can also automate data processing, freeing up geoscientists to focus on high-value tasks. Case studies demonstrate the effectiveness of ML and AI in identifying new exploration targets and improving mining operations. ML and AI are revolutionizing mining exploration, offering improved accuracy, efficiency, and decision-making. As these technologies continue to evolve, they are likely to play a critical role in discovering new mineral deposits and optimizing mining operations.