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Minerals
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GIS, AI, and Modelling of Mineralization Process and Prospectivity
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GIS, AI, and Modelling of Mineralization Process and Prospectivity

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 (20 March 2022) | Viewed by 10272

Special Issue Editors

Prof. Dr. Xiancheng Mao

E-Mail Website
Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: 3D GIS; 3D modeling; 3D prospectivity modeling; big geoscience data; numerical simulation of geology process
Dr. Chengbin Wang

E-Mail Website
Guest Editor
School of Earth Resources, China University of Geosciences, Wuhan 430074, China
Interests: GIS-based mineral predication; uncertainty modeling; mineral big data mining; geoscience semantic network
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

Special Issue Information

Dear Colleagues,

Mineral deposits are the results of coupled multiple geology processes in a certain period and space. Determining the spatial association among components of the mineral system and understanding their coupling processes are particularly important to probe mineralization processes and conduct reliable target appraisal. Mineral exploration and investigation allow us to acquire a wealth of geological information, which involves the spatial association underlying the mineralization process and implying mineral prospectivity. The geographic information system (GIS) is capable of storing, processing, exploring, and visualizing geological information. Due to the complexity of the mineralization process, studying the mineralization process and prospectivity from the geological information by GIS is an open question and technically challenging avenue to research communities.

In the era of big data, many GIS-based methods have been ceaselessly developed in support of probing mineralization processes and prospectivity modelling, in particular for 3D modelling, spatial analysis, and high-performance numerical simulation. Additionally, our understanding of geoscience data is greatly deepened via GIS, which benefits from the development of artificial intelligence (AI) techniques, such as transfer and deep learning.

This Special Issue is designed to gather reviews and papers on the applications of GIS and AI for modelling mineralization processes and prospectivity. Of particular interest are manuscripts reporting novel and key methods enlightening research on mineralization processes and/or prospectivity mapping. Studies with the aim of deciphering the metallogenesis of various ore deposits by computational analysis are also welcome.

Prof. Dr. Xiancheng Mao
Dr. Chengbin Wang
Dr. Zhankun Liu
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

  • advances in GIS
  • novel artificial intelligence techniques
  • big data analysis
  • 3D modelling
  • numerical simulations
  • mineral prospectivity modelling

Published Papers (4 papers)

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Research

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21 pages, 2944 KiB  
Article
Construction of Conceptual Prospecting Model Based on Geological Big Data: A Case Study in Songtao-Huayuan Area, Hunan Province
by Chang Liu, Jianping Chen, Shi Li and Tao Qin
Minerals 2022, 12(6), 669; https://doi.org/10.3390/min12060669 - 26 May 2022
Cited by 8 | Viewed by 1580
Abstract
With the era of big data, the prediction and evaluation of geological mineral resources have gradually entered into a new stage from digital prospecting to intelligent prospecting. The theoretical method of big data mining can contribute to deep mineral resource prediction and evaluation. [...] Read more.
With the era of big data, the prediction and evaluation of geological mineral resources have gradually entered into a new stage from digital prospecting to intelligent prospecting. The theoretical method of big data mining can contribute to deep mineral resource prediction and evaluation. This paper extracts ore-causing and ore-caused anomaly information based on text intelligent mining technology, and constructs a regional conceptual prospecting model based on geological prospecting big data. First, we set up a corpus based on text big data discovery and preprocessing technology. Second, we used CNN multiple scale text classification technology to analyze geological text data from the two main aspects: ore-causing anomalies and ore-caused anomalies. Third, we used a statistical method to analyze the semantic links between content-words, and we constructed chord diagrams and ternary diagrams to visualize the content-words and their links. Finally, we constructed a regional conceptual prospecting model based on the knowledge graphs. Full article
(This article belongs to the Special Issue GIS, AI, and Modelling of Mineralization Process and Prospectivity)
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22 pages, 12114 KiB  
Article
Determining the Paleostress Regime during the Mineralization Period in the Dayingezhuang Orogenic Gold Deposit, Jiaodong Peninsula, Eastern China: Insights from 3D Numerical Modeling
by Shaofeng Xie, Xiancheng Mao, Zhankun Liu, Hao Deng, Jin Chen and Keyan Xiao
Minerals 2022, 12(5), 505; https://doi.org/10.3390/min12050505 - 19 Apr 2022
Cited by 3 | Viewed by 2012
Abstract
The Dayingezhuang orogenic gold deposit, located in the northwestern Jiaodong Peninsula, is hosted by the Zhaoping detachment fault, but the paleostress regime during the mineralization period remains poorly understood. In this study, a series of numerical modeling experiments with variable stress conditions were [...] Read more.
The Dayingezhuang orogenic gold deposit, located in the northwestern Jiaodong Peninsula, is hosted by the Zhaoping detachment fault, but the paleostress regime during the mineralization period remains poorly understood. In this study, a series of numerical modeling experiments with variable stress conditions were carried out using FLAC3D software to determine the orientation of paleostress and the fluid migration processes during the ore-forming period. The results show that the simple compression or tension stress model led to fluid downward or upward flow along the fault, respectively, accompanying the expansion deformation near the hanging wall or footwall of the Zhaoping fault, which is inconsistent with the known NE oblique mineralization distribution at Dayingezhuang. The reverse and strike-slip model shows that the shear stress was distributed in the gentle dip sites of the fault, and the expansion space occurred in the geometric depression sites of the fault, which is also inconsistent with the known mineralization distribution. The normal and strike-slip model shows that shear stress was distributed in the sites where the fault geometry transforms from steep to gentle. In addition, the expansion deformation zones appeared at sites with dip angles of 35~60° in the footwall and extended along with the NE-trending distribution from shallow to deep levels. The numerical results are quite consistent with the known mineralization, suggesting that the fault movement during the mineralization stage is a combination of the local strike-slip and the NW–SE extension in the Dayingezhuang deposit. Under this stress regime (σ1 NE–SW, σ2 vertical, σ3 NW–SE), the NE dilation zones associated with fault deformation served as channels for the ore-forming fluid migration. Based on the numerical modeling results, the deeper NE levels of the No. 2 orebody in the Dayingezhuang deposit have good prospecting potential. Thus, our study not only highlights that gold mineralization at Dayingezhuang is essentially controlled by the detachment fault geometry associated with certain stress directions but also demonstrates that numerical modeling is a robust tool for identifying potential mineralization. Full article
(This article belongs to the Special Issue GIS, AI, and Modelling of Mineralization Process and Prospectivity)
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13 pages, 7003 KiB  
Article
Rock Classification in a Vanadiferous Titanomagnetite Deposit Based on Supervised Machine Learning
by Youngjae Shin and Seungwook Shin
Minerals 2022, 12(4), 461; https://doi.org/10.3390/min12040461 - 10 Apr 2022
Cited by 2 | Viewed by 1795
Abstract
As the potential locations of undiscovered ore deposits become deeper, a technique for predicting promising areas in the subsurface media has become necessary. Geoscience data on a wide range of underground media can be obtained through geophysical field exploration, but integration and interpretation [...] Read more.
As the potential locations of undiscovered ore deposits become deeper, a technique for predicting promising areas in the subsurface media has become necessary. Geoscience data on a wide range of underground media can be obtained through geophysical field exploration, but integration and interpretation of multi-geophysical data are difficult because of differences in spatial resolution. We developed a rock classifier that can predict promising vanadiferous titanomagnetite deposits from multi-geophysical data using supervised machine learning. Vanadiferous titanomagnetite ores are the main source of vanadium, which can be used as a large-scale energy storage system. Model training was conducted using rock samples from drilling cores, and the density of rock samples was used as a criterion for data labeling. We employed the support vector machine, random forest, extreme gradient boosting, LightGBM, and deep neural network for supervised learning, and the accuracy of all methods was 0.95 or greater. We applied trained models to three-dimensional geophysical field data to predict ore body locations. These candidate regions were distributed in the northeast of the geophysical survey area, and some classified areas were verified using a geological map. Full article
(This article belongs to the Special Issue GIS, AI, and Modelling of Mineralization Process and Prospectivity)
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Review

Jump to: Research

20 pages, 1343 KiB  
Review
Overview on the Development of Intelligent Methods for Mineral Resource Prediction under the Background of Geological Big Data
by Shi Li, Jianping Chen and Chang Liu
Minerals 2022, 12(5), 616; https://doi.org/10.3390/min12050616 - 12 May 2022
Cited by 17 | Viewed by 3979
Abstract
In the age of big data, the prediction and evaluation of geological mineral resources have gradually entered a new stage, intelligent prospecting. This review briefly summarizes the research development of textual data mining and spatial data mining. It is considered that the current [...] Read more.
In the age of big data, the prediction and evaluation of geological mineral resources have gradually entered a new stage, intelligent prospecting. This review briefly summarizes the research development of textual data mining and spatial data mining. It is considered that the current research on mineral resource prediction has integrated logical reasoning, theoretical models, computational simulations, and other scientific research models, and has gradually advanced toward a new model. This type of new model has tried to mine unknown and effective knowledge from big data by intelligent analysis methods. However, many challenges have come forward, including four aspects: (i) discovery of prospecting big data based on geological knowledge system; (ii) construction of the conceptual prospecting model by intelligent text mining; (iii) mineral prediction by intelligent spatial big data mining; (iv) sharing and visualization of the mineral prediction data. By extending the geological analysis in the process of prospecting prediction to the logical rules associated with expert knowledge points, the theory and methods of intelligent mineral prediction were preliminarily established based on geological big data. The core of the theory is to promote the flow, invocation, circulation, and optimization of the three key factors of “knowledge”, “model”, and “data”, and to preliminarily constitute the prototype of intelligent linkage mechanisms. It could be divided into four parts: intelligent datamation, intelligent informatization, intelligent knowledgeization, and intelligent servitization. Full article
(This article belongs to the Special Issue GIS, AI, and Modelling of Mineralization Process and Prospectivity)
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