Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area.

中文翻译：

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机器学习在采矿和矿产工业矿物学数据开发中的应用系统综述

机器学习是人工智能的一个子类别，旨在使计算机无需明确编程即可解决复杂问题。近年来，大型数据集的可用性、有效算法的开发以及对强大计算机的访问导致机器学习取得了前所未有的成功。这种强大的工具已被用于包括采矿和矿物工业在内的众多科学和工程领域。考虑到全球对原材料的需求不断增加，矿床地质结构的复杂性以及矿石品位的下降，需要高质量和广泛的矿物学信息。对这些宝贵信息的综合分析需要先进而强大的技术，包括机器学习。本文系统回顾了致力于开发基于机器学习的解决方案以更好地在采矿和矿物研究中利用矿物学数据的努力。为此，我们调查了相关文献中机器学习优势背后的主要原因、已部署的机器学习算法、输入数据、相关输出，以及该学科领域的总体趋势。