A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry
| Autor: | Mohammad Jooshaki, Simon P. Michaux, Alona Nad |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science Science and engineering Geology mineralogical analysis mining 010501 environmental sciences artificial intelligence 010502 geochemistry & geophysics Geotechnical Engineering and Engineering Geology Machine learning computer.software_genre 01 natural sciences Geological structure machine learning Artificial intelligence mineralogy business computer Complex problems QE351-399.2 0105 earth and related environmental sciences |
| Zdroj: | Minerals, Vol 11, Iss 816, p 816 (2021) |
| ISSN: | 2075-163X |
| DOI: | 10.3390/min11080816 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |