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Minerals
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3D Modeling of Mineral Deposits
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3D Modeling of Mineral Deposits

A special issue of Minerals (ISSN 2075-163X).

Deadline for manuscript submissions: closed (1 March 2024) | Viewed by 1921

Special Issue Editors

Dr. Zhankun Liu

E-Mail Website
Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: 3D prospectivity modelling; spatial analysis; numerical simulation; economic geology; mineral system
Special Issues, Collections and Topics in MDPI journals
Dr. Nan Li

E-Mail Website
Guest Editor
MLR Laboratory of Metallogeny and Mineral Resource Assessment, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
Interests: 3D modeling; prospectivity mapping; uncertainty modeling; big geoscience data digging
Dr. Hao Deng

E-Mail Website
Guest Editor
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Ministry of Education), School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: implicit modeling; 3D prospectivity modeling; machine learning; 3D GIS

Special Issue Information

Dear Colleagues,

Mineral deposits are the result of coupled multiple geology processes in a certain period and space. Traditional geology investigation on mineral deposits is, however, limited in 3D view. With the fast increase in drill data in mines, three-dimensional (3D) modeling has been demonstrated as one of the important roles in mineral exploration. The 3D models of mineral deposits not only include several geology-geophysics-geochemistry objects (e.g., orebody, structure, alteration and anomaly) but also present their attributes that record mineralization formation processes, which can thus minimize the risk associated with geology understanding. With the technological advances in 3D modeling, spatial analysis, artificial intelligence and numerical simulation in recent years, the 3D models of mineral deposits have already been greatly improved in quality and efficiency. This Special Issue is open to all research about 3D modeling of mineral deposits from the mine scale and above. Of particular interest are manuscripts reporting novel and key 3D geology modeling methods enlightening the research of mineral metallogeny and/or exploration.

Dr. Zhankun Liu
Dr. Nan Li
Dr. Hao Deng
Guest Editors

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

  • 3D modeling
  • visualization of mineral deposits
  • 3D model analysis
  • AI application in modeling
  • mineral exploration
  • 3D mineral prospectivity modeling

Published Papers (1 paper)

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Research

19 pages, 12636 KiB  
Article
3D Mineral Prospectivity Mapping from 3D Geological Models Using Return–Risk Analysis and Machine Learning on Imbalance Data
by Qingming Peng, Zhongzheng Wang, Gongwen Wang, Wengao Zhang, Zhengle Chen and Xiaoning Liu
Minerals 2023, 13(11), 1384; https://doi.org/10.3390/min13111384 - 29 Oct 2023
Viewed by 1468
Abstract
Three-dimensional Mineral Prospectivity Mapping (3DMPM) is an innovative approach to mineral exploration that combines multiple geological data sources to create a three-dimensional (3D) model of a mineral deposit. It provides an accurate representation of the subsurface that can be used to identify areas [...] Read more.
Three-dimensional Mineral Prospectivity Mapping (3DMPM) is an innovative approach to mineral exploration that combines multiple geological data sources to create a three-dimensional (3D) model of a mineral deposit. It provides an accurate representation of the subsurface that can be used to identify areas with mineral potential. These 3D geological models are the typical data source for 3D prospective modeling. Geological data sets from multiple sources are used to construct 3D geological models. Since in practice there is a significant imbalance in the ratio of mineralized to non-mineralized classes, the classification results will be biased in favor of the more observed classes. Borderline-SMOTE (BLSMOTE) is an oversampling technique used to solve the problem of unbalanced datasets and works by generating synthetic data points along the boundary line between the minority and majority classes. This helps to create a more balanced dataset without introducing too much noise. Non-mineralized samples can be generated by randomly selecting non-mineralized locations, which means that uncertainties are generated. In this paper, we take the shallow-forming low-temperature hydrothermal deposit Guizhou Lannigou gold deposit as an example to extract the ore-controlling elements and establish a 3D geological model. A total of 50 training samples are generated using the sampling method described above, and 50 mineralization prospects are generated using Random Forests. A return–risk analysis was used to explore the uncertainties associated with synthetic positive samples and randomly selected negative samples, and to determine the final mineral potential values. Based on the evaluation metrics G-mean and F-value, the model using BLSMOTE outperforms the model without the synthetic algorithm and the models using SMOTE and KMeansSMOTE. The optimal model BLSMOTE18 has an AUC of 0.9288. The methodology also performs superiorly with different levels of class imbalance datasets. Excluding the predictions where the results highly overlap with known deposits, five target zones were circled for the targets using a P-A plot, all of which have obvious metallogenic geological features. Among them, Target1 and Target2 have good potential for mineralization, which is of great significance for future mineral exploration work. Full article
(This article belongs to the Special Issue 3D Modeling of Mineral Deposits)
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