Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.9
2.5
Journals
Minerals
Special Issues
Geological, Structural, Geochemical, Hyperspectral, and Geostatistical Modeling for Mineral Exploration
share announcement

Geological, Structural, Geochemical, Hyperspectral, and Geostatistical Modeling for Mineral Exploration

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 3816

Special Issue Editors

Dr. Anup Krishna Prasad

E-Mail Website
Guest Editor
Department of Applied Geology, Indian Institute of Technology, Indian School of Mines, Dhanbad 826004, India
Interests: mineral exploration; hyperspectral remote sensing; geostatistics; image processing; spatial analysis and GIS
Dr. Bhabesh Chandra Sarkar

E-Mail Website
Guest Editor
Department of Applied Geology, Indian Institute of Technology, Indian School of Mines, Dhanbad 826004, India
Interests: geostatistics; mineral exploration; exploration economics

Special Issue Information

Dear Colleagues,

This Special Issue aims to present research that focuses on the exploration of ores in diverse geological settings, such as shear zone mineralization, sediment-hosted stratiform deposits, placer deposits, and hydrothermal settings, through the detailed investigation, characterization, and geospatial mapping and modeling of ore deposits. Submissions pertaining to mineralogy, petrography, alteration geochemistry, structural controls, geochemical and biogeochemical signatures, and genesis-related submissions are highly encouraged. Moreover, preference is given to studies that highlight field and laboratory research, spectral signatures and mineral detection, characterization and classification, and multi-spectral and hyperspectral remote sensing. Studies on the application of machine learning and artificial intelligence to the study of ore deposits are welcome, as is research into 3D/4D orebody modeling and mineral resource estimation for specific industrial metals and nonmetals, rare earth minerals (REEs), and nanomaterials.

The Special Issue also welcomes submissions on cutting-edge methodologies and technologies such as 3D/4D geological and geostatistical modeling for mineral exploration using Kriging, Co-Kriging, multivariate geostatistics, and geostatistical simulations; resource estimation using geostatistical modeling; and decision-making processes supported by machine learning and artificial intelligence.

Dr. Anup Krishna Prasad
Dr. Bhabesh Chandra Sarkar
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

  • geology, mineralogy, and petrography
  • hyperspectral remote sensing
  • geochemical and biogeochemical exploration
  • geospatial mapping and mineral prospecting
  • structural controls and alteration geochemistry
  • shear zone mineralization
  • spectral and chemical characterization
  • genesis of ore deposits
  • geological modeling and simulation
  • kriging, co-kriging, and multivariate geostatistics
  • artificial intelligence and machine learning

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

29 pages, 5274 KiB  
Article
Machine Learning-Based Lithological Mapping from ASTER Remote-Sensing Imagery
by Hazhir Bahrami, Pouya Esmaeili, Saeid Homayouni, Amin Beiranvand Pour, Karem Chokmani and Abbas Bahroudi
Minerals 2024, 14(2), 202; https://doi.org/10.3390/min14020202 - 16 Feb 2024
Viewed by 999
Abstract
Accurately mapping lithological features is essential for geological surveys and the exploration of mineral resources. Remote-sensing images have been widely used to extract information about mineralized alteration zones due to their cost-effectiveness and potential for being widely applied. Automated methods, such as machine-learning [...] Read more.
Accurately mapping lithological features is essential for geological surveys and the exploration of mineral resources. Remote-sensing images have been widely used to extract information about mineralized alteration zones due to their cost-effectiveness and potential for being widely applied. Automated methods, such as machine-learning algorithms, for lithological mapping using satellite imagery have also received attention. This study aims to map lithologies and minerals indirectly through machine-learning algorithms using advanced spaceborne thermal emission and reflection radiometer (ASTER) remote-sensing data. The capabilities of several machine-learning (ML) algorithms were evaluated for lithological mapping, including random forest (RF), support vector machine (SVM), gradient boosting (GB), extreme gradient boosting (XGB), and a deep-learning artificial neural network (ANN). These methods were applied to ASTER imagery of the Sar-Cheshmeh copper mining region of Kerman Province, in southern Iran. First, several spectral features that were extracted from ASTER bands were used as input data. Second, correlation coefficients between the original spectral bands and features were extracted. The importance of the random forest features (RF’s feature importance) was subsequently computed, and features with less importance were removed. Finally, the remained features were given to the models as input data in the second scenario. Accuracy assessments were performed for lithological classes in the study region, including Sar-Cheshmeh porphyry, quartz eye, late fine porphyry, hornblende dike, granodiorite, feldspar dike, biotite dike, andesite, and alluvium. The overall accuracy results of lithological mapping showed that ML-based algorithms without feature extraction have the highest accuracy. The overall accuracy percentages for ML-based algorithms without conducting feature extraction were 84%, 85%, 80%, 82%, and 80% for RF, SVM, GB, XGB, and ANN, respectively. The results of this study would be of great interest to geologists for lithological mapping and mineral exploration, particularly for selecting appropriate ML-based techniques to be implemented in similar regions. Full article
(This article belongs to the Special Issue Geological, Structural, Geochemical, Hyperspectral, and Geostatistical Modeling for Mineral Exploration)
►▼ Show Figures

Figure 1

20 pages, 15919 KiB  
Article
The Temporal Distribution of the Host Rocks to Gold, the Archean Witwatersrand Basin, South Africa
by Neil Phillips, Julian Vearncombe, Dave Craw and Arthur Day
Minerals 2024, 14(2), 199; https://doi.org/10.3390/min14020199 - 15 Feb 2024
Viewed by 1207
Abstract
The hosts to gold around the Witwatersrand Basin span over 400 my, through 14 km of stratigraphy in a variety of host rocks and in tectonic settings that include periods of rifting, thermal subsidence, foreland basin, flood basalt outpouring, graben development, and further [...] Read more.
The hosts to gold around the Witwatersrand Basin span over 400 my, through 14 km of stratigraphy in a variety of host rocks and in tectonic settings that include periods of rifting, thermal subsidence, foreland basin, flood basalt outpouring, graben development, and further thermal subsidence. A geological model that assumes placer processes to explain this diversity implies a super-long-lived and special source of the detrital gold, transport, and highly effective sorting processes over a time span of 400 my. There is no evidence of a special source and sorting over such a long time period. In the Phanerozoic, this would be equivalent to the special source and sorting processes operating continually over an equivalent period of geological time spanning from the Devonian up until the present day; this is as yet recognised nowhere else on the planet. With regard to the geological model that assumes a placer process, this is untenable because of these scientific shortcomings and its lack of success in exploration. A better use of funds may be to consider alternative approaches and epigenetic models in exploration. Full article
(This article belongs to the Special Issue Geological, Structural, Geochemical, Hyperspectral, and Geostatistical Modeling for Mineral Exploration)
►▼ Show Figures

Graphical abstract

18 pages, 19936 KiB  
Article
The Indicative Significance of Interlayer-Sliding Fault Deformation in a Thrust–Fold Structure of the Huize Mine District to the Variation of Ore-Hosting Space: Insights from Analogue Modeling
by Mao Yang, Runsheng Han, Weiwei Zhou, Yan Zhang and Fei Liu
Minerals 2024, 14(2), 142; https://doi.org/10.3390/min14020142 - 28 Jan 2024
Viewed by 858
Abstract
Interlayer-sliding faults play a crucial role in governing the distribution of metal deposits. Nevertheless, the mechanism by which these faults control the spatial arrangement of ore bodies throughout the evolution of fault–fold structures remains unclear. Here, we formulated three series of experimental models [...] Read more.
Interlayer-sliding faults play a crucial role in governing the distribution of metal deposits. Nevertheless, the mechanism by which these faults control the spatial arrangement of ore bodies throughout the evolution of fault–fold structures remains unclear. Here, we formulated three series of experimental models to explore variations in deformation and alterations in the mechanical characteristics of interlayer-sliding faults throughout the evolution of the thrust–fold structures. The experimental results indicate that the thrust faults formed in the three series of experiments all propagate in a piggyback propagation, displaying an imbricate thrust in cross-sections. Compared with Model 1 and Model 2, Model 3 demonstrates the longest transmission distance of the deformation front, the smallest thrust wedge taper angle, the fewest thrust faults with the largest spacing, and a reduction in the dip angle of the thrust fault. Particle image velocimetry (PIV) showed that in the top view, the position of minimum horizontal strain in each stage is the position of thrust faults. In the cross-sectional view, the development location of thrust faults shows the low-value area of the velocity field and surface strain field, and the development location of the interlayer-sliding fault and tensile space in the core of the fold displays the high-value area of velocity field and surface strain field. The structural characteristics of experiment 3 are highly similar to the actual geological model, indicating that there is a certain ore-hosting space in the Dengying Formation deep in the deposit. Although the expansion zone in the deep area is smaller than that in the shallow area, it still has favorable prospecting prospects. Full article
(This article belongs to the Special Issue Geological, Structural, Geochemical, Hyperspectral, and Geostatistical Modeling for Mineral Exploration)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop