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Minerals
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Advances in 3D Geological Modeling and Metallogenic Prediction
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Advances in 3D Geological Modeling and Metallogenic Prediction

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".

Deadline for manuscript submissions: closed (11 January 2024) | Viewed by 3743

Special Issue Editor

Prof. Dr. Keyan Xiao

E-Mail Website
Guest Editor
MIR Key Laboratory of Metallogeny and Mineral Resource Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
Interests: metallogenic prediction; mineral prospectivity mapping; 3D modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the increasing difficulty of mineral exploration and exploration risks, the use of computer-based 3D geological modeling, visualization techniques, and geological big data analysis methods has gradually become a highlight in the field of metallogenic prediction; 3D geological modeling and visualization can vividly depict the spatial distribution relationship of strata, rock masses, and structures within a certain depth range underground. Based on this, visualization and quantitative prediction methods incorporating multi-disciplinary geological information such as geology, geochemistry, and geophysics play an important role in the development of three-dimensional mineralization prediction theory and the search for hidden ore bodies. Therefore, the Special Issue titled "3D Geological Modeling and Metallogenic Prediction" focuses on the advancements and applications of these two interconnected fields in geology and mineral exploration. The collection of articles within this Issue explores the latest research, methodologies, and case studies related to 3D geological modeling and metallogenic prediction.

Prof. Dr. Keyan Xiao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ore-prospecting information from geology, geochemistry, geophysics, and remote sensing survey
  • 3D geological modeling
  • metallogenic prediction
  • machine learning

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Published Papers (2 papers)

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Research

21 pages, 8367 KiB  
Article
Machine Learning-Based Uranium Prospectivity Mapping and Model Explainability Research
by Weihao Kong, Jianping Chen and Pengfei Zhu
Minerals 2024, 14(2), 128; https://doi.org/10.3390/min14020128 - 24 Jan 2024
Cited by 1 | Viewed by 1441
Abstract
Sandstone-hosted uranium deposits are indeed significant sources of uranium resources globally. They are typically found in sedimentary basins and have been extensively explored and exploited in various countries. They play a significant role in meeting global uranium demand and are considered important resources [...] Read more.
Sandstone-hosted uranium deposits are indeed significant sources of uranium resources globally. They are typically found in sedimentary basins and have been extensively explored and exploited in various countries. They play a significant role in meeting global uranium demand and are considered important resources for nuclear energy production. Erlian Basin, as one of the sedimentary basins in northern China, is known for its uranium mineralization hosted within sandstone formations. In this research, machine learning (ML) methodology was applied to mineral prospectivity mapping (MPM) of the metallogenic zone in the Manite depression of the Erlian Basin. An ML model of 92% accuracy was implemented with the random forest algorithm. Additionally, the confusion matrix and receiver operating characteristic curve were used as model evaluation indicators. Furthermore, the model explainability research with post hoc interpretability algorithms bridged the gap between complex opaque (black-box) models and geological cognition, enabling the effective and responsible use of AI technologies. The MPM results shown in QGIS provided vivid geological insights for ML-based metallogenic prediction. With the favorable prospective targets delineated, geologists can make decisions for further uranium exploration. Full article
(This article belongs to the Special Issue Advances in 3D Geological Modeling and Metallogenic Prediction)
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22 pages, 15962 KiB  
Article
Metallogenic Prediction of the Zaozigou Gold Deposit Using 3D Geological and Geochemical Modeling
by Cheng Li, Bingli Liu, Keyan Xiao, Yunhui Kong, Lu Wang, Rui Tang, Miao Xie and Yixiao Wu
Minerals 2023, 13(9), 1205; https://doi.org/10.3390/min13091205 - 13 Sep 2023
Cited by 2 | Viewed by 1593
Abstract
Deep-seated mineralization prediction is an important scientific problem in the area of mineral resources exploration. The 3D metallogenic information extraction of geology and geochemistry can be of great help. This study uses 3D modeling technology to intuitively depict the spatial distribution of orebodies, [...] Read more.
Deep-seated mineralization prediction is an important scientific problem in the area of mineral resources exploration. The 3D metallogenic information extraction of geology and geochemistry can be of great help. This study uses 3D modeling technology to intuitively depict the spatial distribution of orebodies, fractures, and intrusive rocks. In particular, the geochemical models of 12 elements are established for geochemical metallogenic information extraction. Subsequently, the front halo element association of As-Sb-Hg, the near-ore halo element association of Au-Ag-Cu-Pb-Zn, and the tail halo element association of W-Mo-Bi are identified. Upon this foundation, the 3D convolutional neural network model is built and used for deep-seated mineralization prediction, which expresses a high performance (AUC = 0.99). Associated with the metallogenic regularity, two mineral exploration targets are delineated, which might be able to serve as beneficial achievements for deep exploration in the Zaozigou gold deposit. Full article
(This article belongs to the Special Issue Advances in 3D Geological Modeling and Metallogenic Prediction)
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