Data-driven Approaches to Mineral Resource Management Using AI: A Brief Review

Year : 2025 | Volume : 02 | Issue : 01 | Page : 25 29
    By

    Arya,

  1. Student, Department of Biotechnology, Amity University, Rajasthan, India

Abstract

The role of Artificial Intelligence (AI) in the mineral resource sector has become increasingly significant over the past few years, as industries seek to optimize and modernize their operations. AI encompasses a variety of technologies and techniques, such as machine learning, deep learning, and expert systems, that are now widely used in mineral exploration, resource estimation, and mine management. These AI-driven approaches have brought about a transformative shift, enhancing efficiency, accuracy, and cost-effectiveness in mineral resource operations. In the context of mineral exploration, AI algorithms enable better prediction models by analyzing complex geological data, offering new insights into the distribution of mineral resources and the potential for discovery. Artificial Intelligence (AI) improves modeling accuracy in resource estimation by identifying trends that conventional approaches can miss. This results in more reliable and sustainable resource management practices, which are crucial for minimizing environmental impact while maximizing extraction efficiency. Additionally, AI technologies play an important role in mine management by optimizing operations, improving safety, and reducing operational costs. AI can forecast equipment breakdowns, suggest maintenance plans, and improve resource allocation by utilizing real-time data from sensors and monitoring systems. But there are several difficulties in incorporating AI into the mining sector. Careful consideration must be given to problems including poor data quality, intricate geological models, and expensive implementation costs. Notwithstanding these obstacles, mining has tremendous prospects for the future thanks to the continuous developments in AI. This article reviews the current state of AI applications in mineral resource estimation and management, discussing its methods, benefits, and challenges while exploring the long-term potential of AI to revolutionize the mining industry.

Keywords: Artificial intelligence in mining, mineral resource estimation, machine learning in geology, AI-driven mine management, predictive analytics in mining, geostatistics and AI integration

[This article belongs to International Journal of Minerals ]

How to cite this article:
Arya. Data-driven Approaches to Mineral Resource Management Using AI: A Brief Review. International Journal of Minerals. 2025; 02(01):25-29.
How to cite this URL:
Arya. Data-driven Approaches to Mineral Resource Management Using AI: A Brief Review. International Journal of Minerals. 2025; 02(01):25-29. Available from: https://journals.stmjournals.com/ijmi/article=2025/view=200553


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Regular Issue Subscription Review Article
Volume 02
Issue 01
Received 06/02/2025
Accepted 11/02/2025
Published 19/02/2025
Publication Time 13 Days
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