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Minerals
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Mineral Prospectivity Mapping (MPM) Using Multi-Source Datasets, Geo-Statistical Algorithms and...
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Mineral Prospectivity Mapping (MPM) Using Multi-Source Datasets, Geo-Statistical Algorithms and Machine Learning Techniques

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 4870

Special Issue Editors

Dr. Amin Beiranvand Pour

E-Mail Website
Guest Editor
1. Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu (UMT), Kuala Nerus 21030, Terengganu, Malaysia
2. Geoscience and Digital Earth Centre (INSTeG), Research Institute for Sustainable Environment, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Interests: remote sensing; environment; satellite image processing; geological mapping; minerals; exploration geology; mining; exploration geophysics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ardeshir Hezarkhani

E-Mail Website
Guest Editor
Department of Mining Engineering, Amirkabir University of Technology, Tehran 1591634311, Iran
Interests: mineral prospectivity mapping (MPM); multi-dimensional data fusion; applied geophysics; applied geochemistry; mineral exploration; remote sensing
Dr. Aref Shirazi

E-Mail Website
Guest Editor
Department of Mining Engineering, Amirkabir University of Technology, Tehran 1591634311, Iran
Interests: mineral prospectivity mapping (MPM); multi-dimensional data fusion; applied geophysics; applied geochemistry; mineral exploration; remote sensing
Dr. Adel Shirazy

E-Mail Website
Guest Editor
Department of Mining Engineering, Amirkabir University of Technology, Tehran 1591634311, Iran
Interests: mineral prospectivity mapping (MPM); multi-dimensional data fusion; applied geophysics; applied geochemistry; mineral exploration; remote sensing

Special Issue Information

Dear Colleagues,

Mineral prospectivity mapping (MPM) is one the most important approaches to mineral exploration using a multi-source dataset. It normally uses a multivariable decision-making contrivance to define and highlight potential zones of ore mineralizations in metallogenic areas. MPM is a vital concern in  mineral exploration and mining to diminish the exploration costs by offering an outline of drilling high potential zones of ore mineralizations. Remote sensing, geological, geophysical, and geochemical datasets can be combined to generate a mineral potential map of a study area at local to regional scales. Fusion, analysis, and the selection of information layers using geo-statistical algorithms and machine learning techniques provide a vital phase on the way to accomplishing accurate MPM for mineral exploration in metallogenic provinces. The main goal of this Special Issue is to focus on miscellaneous ideas about data fusion for MPM with a focus on elevating integration methods and intensifying methods for mineral exploration. Multidisciplinary innovative studies of mineral exploration established on a variety of datasets, algorithms, field, and laboratory techniques covering different research aspects to address ore mineral exploration are highly welcome and encouraged.

The topics of interest include but are not limited to

  • Remote sensing analysis of multispectral and hyperspectral imagery for mineral exploration;
  • Fusion of remote sensing, geophysical, geological, and geochemical datasets;
  • GIS and remote sensing integration for mineral exploration modeling;
  • Reflectance spectroscopy and geochemistry of rocks and minerals for mineral potential mapping;
  • Interpretation and fusion of ASD spectroscopy and XRD, XRF, and ICP-MS analysis for mineral exploration;
  • Recent advances in multi-source remote sensing information fusion for mineral exploration;
  • Machine learning techniques for integrating remote sensing, geophysical, geological, and geochemical data;
  • Multivariate, compositional, and geo-statistical techniques for mineral prospectivity mapping (MPM);
  • Delineation of weak geochemical and geophysical anomalies pertaining to blind or covered deposits;
  • Three-dimensional modeling of geochemical and geophysical anomalies.

Dr. Amin Beiranvand Pour
Prof. Dr. Ardeshir Hezarkhani
Dr. Aref Shirazi
Dr. Adel Shirazy
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

  • mineral propectivity mapping (MPM)
  • ore mineral exploration
  • remote sensing
  • multi-dimensional data fusion
  • machine learning techniques
  • knowledge-driven models
  • data-driven models
  • geophysical exploration
  • geochemical exploration
  • geographic information system (GIS) modeling
  • supervised and unsupervised algorithms
  • geo-statistical algorithms
  • deep learning techniques
  • novel hybrid methods

Published Papers (3 papers)

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28 pages, 36683 KiB  
Article
Remote Sensing, Petrological and Geochemical Data for Lithological Mapping in Wadi Kid, Southeast Sinai, Egypt
by Wael Fahmy, Hatem M. El-Desoky, Mahmoud H. Elyaseer, Patrick Ayonta Kenne, Aref Shirazi, Ardeshir Hezarkhani, Adel Shirazy, Hamada El-Awny, Ahmed M. Abdel-Rahman, Ahmed E. Khalil, Ahmed Eraky and Amin Beiranvand Pour
Minerals 2023, 13(9), 1160; https://doi.org/10.3390/min13091160 - 31 Aug 2023
Cited by 1 | Viewed by 1317
Abstract
The Wadi Samra–Wadi Kid district in southeastern Sinai, Egypt, has undergone extensive investigation involving remote sensing analysis, field geology studies, petrography, and geochemistry. The main aim of this study is the integration between remote sensing applications, fieldwork, and laboratory studies for accurate lithological [...] Read more.
The Wadi Samra–Wadi Kid district in southeastern Sinai, Egypt, has undergone extensive investigation involving remote sensing analysis, field geology studies, petrography, and geochemistry. The main aim of this study is the integration between remote sensing applications, fieldwork, and laboratory studies for accurate lithological mapping for future mineral exploration in the study region. The field relationships between these coincident rocks were studied in the study area. Landsat-8 (OLI) data that cover the investigated area were used in this paper. The different rock units in the study area were studied petrographically using a polarizing microscope, in addition to major and trace analysis using ICP-OES tools. The Operational Land Imager (OLI) images were used with several processing methods, such as false color composite (FCC), band ratio (BR), principal component analysis (PCA), and minimum noise fraction (MNF) techniques for detecting the different types of rock units in the Wadi Kid district. This district mainly consists of a volcano-sedimentary sequence as well as diorite, gabbro, granite, and albitite. Geochemically, the metasediments are classified as pelitic graywackes derived from sedimentary origin (i.e., shales). The Al2O3 and CaO contents are medium–high, while the Fe2O3 and TiO2 contents are very low. Alkaline minerals are relatively low–medium in content. All of the metasediment samples are characterized by high MgO contents and low SiO2, Fe2O3, and CaO contents. The granitic rocks appear to have alkaline and subalkaline affinity, while the subalkaline granites are high-K calc-alkaline to shoshonite series. The alkaline rocks are classified as albitite, while the calc-alkaline series samples vary from monzodiorites to granites. The outcomes of this study can be used for prospecting metallic and industrial mineral exploration in the Wadi Kid district. Full article
(This article belongs to the Special Issue Mineral Prospectivity Mapping (MPM) Using Multi-Source Datasets, Geo-Statistical Algorithms and Machine Learning Techniques)
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27 pages, 23397 KiB  
Article
Geochemical Modeling of Copper Mineralization Using Geostatistical and Machine Learning Algorithms in the Sahlabad Area, Iran
by Aref Shirazi, Ardeshir Hezarkhani, Adel Shirazy and Amin Beiranvand Pour
Minerals 2023, 13(9), 1133; https://doi.org/10.3390/min13091133 - 27 Aug 2023
Viewed by 1151
Abstract
Analyzing geochemical data from stream sediment samples is one of the most proactive tools in the geochemical modeling of ore mineralization and mineral exploration. The main purpose of this study is to develop a geochemical model for prospecting copper mineralization anomalies in the [...] Read more.
Analyzing geochemical data from stream sediment samples is one of the most proactive tools in the geochemical modeling of ore mineralization and mineral exploration. The main purpose of this study is to develop a geochemical model for prospecting copper mineralization anomalies in the Sahlabad area, South Khorasan province, East Iran. In this investigation, 709 stream sediment samples were analyzed using inductively coupled plasma mass spectrometry (ICP-MS), and geostatistical and machine learning techniques. Subsequently, hierarchical analysis (HA), Spearman’s rank correlation coefficient, concentration–area (C–A) fractal analysis, Kriging interpolation, and descriptive statistics studies were performed on the geochemical dataset. Machine learning algorithms, namely K-means clustering, factor analysis (FA), and linear discriminant analysis (LDA) were employed to deliver a comprehensive geochemical model of copper mineralization in the study area. The identification of trace elements and the predictor composition of copper mineralization, the separation of copper geochemical communities, and the investigation of the geochemical behavior of copper vs. its trace elements were targeted and accomplished. As a result, the elements Ag, Mo, Pb, Zn, and Sn were distinguished as trace elements and predictors of copper geochemical modeling in the study area. Additionally, geochemical anomalies of copper mineralization were identified based on trace elements. Conclusively, the nonlinear behavior of the copper element versus its trace elements was modeled. This study demonstrates that the integration and synchronous use of geostatistical and machine learning methods can specifically deliver a comprehensive geochemical modeling of ore mineralization for prospecting mineral anomalies in metallogenic provinces around the globe. Full article
(This article belongs to the Special Issue Mineral Prospectivity Mapping (MPM) Using Multi-Source Datasets, Geo-Statistical Algorithms and Machine Learning Techniques)
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22 pages, 22595 KiB  
Article
Evaluating the Performance of Machine Learning and Deep Learning Techniques to HyMap Imagery for Lithological Mapping in a Semi-Arid Region: Case Study from Western Anti-Atlas, Morocco
by Soufiane Hajaj, Abderrazak El Harti, Amine Jellouli, Amin Beiranvand Pour, Saloua Mnissar Himyari, Abderrazak Hamzaoui and Mazlan Hashim
Minerals 2023, 13(6), 766; https://doi.org/10.3390/min13060766 - 31 May 2023
Cited by 4 | Viewed by 1589
Abstract
Accurate lithological mapping is a crucial juncture for geological studies and mineral exploration. Hyperspectral data provide the opportunity to extract detailed information about the geology and mineralogy of the Earth’s surface. Machine learning (ML) and deep learning (DL) techniques provide an accurate and [...] Read more.
Accurate lithological mapping is a crucial juncture for geological studies and mineral exploration. Hyperspectral data provide the opportunity to extract detailed information about the geology and mineralogy of the Earth’s surface. Machine learning (ML) and deep learning (DL) techniques provide an accurate and effective mapping of various types of lithologies in arid and semi-arid regions. This article discusses the use of machine learning algorithms, specifically Support Vector Machines (SVM), one-dimensional Convolutional Neural Network (1D-CNN), random forest (RF), and k-nearest neighbor (KNN), for lithological mapping in a complex area with strong hydrothermal alteration. The study evaluates the performance of the four algorithms in three different zones in the Ameln valley shear zone (AVSZ) area at eastern Kerdous inlier, Moroccan western Anti-Atlas. The results demonstrated that 1D-CNN achieved the best classification results for most lithological units. Additionally, the LK-SVM demonstrated good mapping results compared to the other SVM models, as well as RF and KNN. Our study concludes that the combination of the CNN and HyMap data can provide the most accurate lithologic mapping for the three selected region, with an overall accuracy of ~95%. However, this study highlights the challenges in identifying different lithological units using remotely sensed data due to spectrum similarities induced by similar chemical and mineralogical compositions. This study emphasizes the importance of carefully considering and evaluating ML and DL methods for lithological mapping studies, then recommends the high-resolution hyperspectral data and DL models for accurate results. The implications of this study would be fascinating to exploration geologists for Mineral Prospectivity Mapping (MPM), especially in selecting the most appropriate techniques for highly accurate mineral mapping in metallogenic provinces. Full article
(This article belongs to the Special Issue Mineral Prospectivity Mapping (MPM) Using Multi-Source Datasets, Geo-Statistical Algorithms and Machine Learning Techniques)
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