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Geology Applied to Mineral Deposits

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 495

Special Issue Editors


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Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: W-Sn deposit; Li-Be deposit; Nb-Ta deposit; granite; pegmatite

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Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: mafic–ultramafic intrusion; Ni-Cu-(PGE) sulfide deposit; cobalt mineralization

E-Mail Website
Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: metallogenic prediction; Cu-Au deposit; Mn deposit; Pb-Zn deposit

Special Issue Information

Dear Colleagues,

In this Special Issue, we delve into the critical processes of mineralization in relation to key metals such as lithium, beryllium, niobium, tantalum, tungsten, tin, cobalt, nickel, and platinum-group elements (PGEs). These metals are essential for various technological applications and are increasingly sought after in the context of sustainable development and green technologies. Our focus encompasses the intricate relationships between these metals and their associated geological environments, particularly highly fractionated granites and mafic–ultramafic rocks.

Understanding the formation mechanisms of these critical metals is vital, especially as global demand continues to rise. This Special Issue aims to present cutting-edge research, innovative methodologies, and interdisciplinary approaches that enhance our understanding of mineralization processes. By fostering collaboration among geologists, mineralogists, and industry stakeholders, we aspire to create a comprehensive resource that not only informs but also inspires further research and exploration in this dynamic field. Together, we can address the challenges posed by the increasing demand for these vital resources while promoting sustainable practices in mineral exploration and extraction.

Dr. Yiqu Xiong
Dr. Yonghua Cao
Prof. Dr. Yongjun Shao
Guest Editors

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Keywords

  • W-Sn deposit
  • Li-Be deposit
  • Nb-Ta deposit
  • granite
  • pegmatite
  • magmatic Ni-Cu-(PGE) deposit
  • mafic–ultramafic intrusion
  • cobalt mineralization

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Published Papers (1 paper)

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Research

18 pages, 6327 KiB  
Article
Machine Learning Reveals Magmatic Fertility of Skarn-Type Tungsten Deposits
by Rui-Chang Tan, Yong-Jun Shao, Yi-Qu Xiong, Zhi-Wei Fan, Hong-Fei Di, Zhao-Jun Wang and Kang-Qi Xu
Appl. Sci. 2025, 15(10), 5237; https://doi.org/10.3390/app15105237 - 8 May 2025
Viewed by 363
Abstract
The chemical composition of apatite has been utilized as an indicator of magmatic fertility related to tungsten mineralization in skarn systems. In this study, we compiled 5776 apatite trace element data from 374 intrusions, along with records indicating magmatic fertility. Then we trained [...] Read more.
The chemical composition of apatite has been utilized as an indicator of magmatic fertility related to tungsten mineralization in skarn systems. In this study, we compiled 5776 apatite trace element data from 374 intrusions, along with records indicating magmatic fertility. Then we trained and validated machine learning (ML) models, specifically support vector machine (SVM) and random forests (RF), to classify magmatic fertility based on apatite chemistry in igneous rocks. RF model achieved high classification accuracies (~93%) on the test dataset, demonstrating that employing ML approaches to distinguish apatite derived from fertile versus barren magmas is feasible and effective. Furthermore, we optimized classification thresholds to maximize the model’s predictive accuracy for identifying potentially fertile magmas. Feature-importance analysis of the machine learning classifier shows that elevated La, Yb, and Mn, together with depleted Sr, Y, Gd, and Tb, constitute the most diagnostic elemental signatures of magmatic fertility. As a case study, we applied our trained ML model to predict the magmatic fertility of apatite samples from the Nanling Range (southern China’s largest skarn-type tungsten mineralization province). Benefiting from the application of GAN-based techniques to address sample imbalance, our ML models can effectively identify tungsten-mineralized favorable skarn areas. Additionally, the visualization technique t-distributed stochastic neighbor embedding (t-SNE) was employed to validate and assess classification outcomes. Results showed clear separation between fertile and barren categories within the reduced 3D space. Our findings emphasize apatite as a sensitive indicator mineral for granite-related magmatic fertility and metallogenesis, underscoring its significant potential in mineral exploration. Finally, we provide a convenient prediction software for magmatic fertility based on a machine learning model utilizing apatite trace element compositions. Full article
(This article belongs to the Special Issue Geology Applied to Mineral Deposits)
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