Technologies in Underground Mining: A Bibliometric Review

Authors

Keywords:

Underground mining, Automation, Internet of Things, Machine learning, Digital twin

Abstract

This study presents a global bibliometric review of technologies applied  to underground mining based on Scopus records published between 2016  and 2025. A total of 739 documents were analyzed using Bibliometrix/BiblioShiny through performance analysis and science mapping. The review examined publication trends, leading sources, authors, countries, collaboration patterns, keyword dynamics, and thematic structure. The results show sustained growth in the field, with a marked rise after 2019 and a high-output phase in recent years. Scientific production is led by China and India, followed by the United States and Australia, within a geographically concentrated but internationally connected landscape. Publication channels are broad and non-hegemonic, while authorship remains decentralized. The thematic structure reveals a consolidated core around underground mining, mining, the Internet of Things, and automation, together with the rapid expansion of artificial intelligence-related topics, especially machine learning and deep learning. Overall, the evidence suggests a transition from isolated technological applications toward more integrated digital systems for monitoring, prediction, control, logistics, and safety. This study provides a structured baseline for understanding the evolution of the field and outlines a future agenda centered on data standardization, multi-mine validation, cybersecurity in 5G/edge environments, and digital twins for safer and more efficient underground operations

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Published

2026-06-09

How to Cite

Vásquez-Maldonado, P. A., & Vicente-Ramos, W. E. (2026). Technologies in Underground Mining: A Bibliometric Review. Minería & Geología, 42, e004. Retrieved from https://revista.ismm.edu.cu/index.php/revistamg/article/view/e004

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Revisiones