Exploring the Potential of Artificial Intelligence and Machine Learning in Mineral Exploration: A Review Article

Abstract

Mineral exploration is a complex and challenging process, requiring vast amounts of data to be analyzed to make informed decisions. Traditional methods of mineral exploration are time-consuming, expensive, and often yield low success rates. However, the emergence of Artificial Intelligence (AI) and Machine Learning (ML) presents an opportunity to revolutionize the mining industry. This review article investigates the current stateof-the-art applications of AI and ML in mineral exploration, evaluates their effectiveness and limitations, and identifies the potential benefits and challenges of their adoption. The study highlights that AI and ML techniques can significantly increase the efficiency and success rate of mining projects. Various AI and ML algorithms such as Neural Networks, Decision Trees, and Random Forests are being used for mineral exploration. These techniques help in identifying patterns and correlations in the vast amount of data, reducing the time and cost involved in mineral exploration. The study also identifies potential limitations such as the need for high-quality data, the lack of interpretability of the results, and the ethical considerations that need to be addressed when using AI and ML in mining. The findings of this study have significant implications for the mining industry. The adoption of AI and ML techniques in mineral exploration can lead to increased profitability, reduced costs, and improved environmental and social impacts. The study provides recommendations for future research and development of AI and ML techniques in mineral exploration. In conclusion, the potential of AI and ML in mineral exploration is immense, and their adoption could lead to a paradigm shift in the mining industry.