Mineral Exploration Based on Remote Sensing

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 (31 May 2023) | Viewed by 6164

Special Issue Editors


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Guest Editor
Department of Earth and Environmental Sciences, Laboratory of Geoinformatics, College of Science, Yarmouk University, Irbid 21163, Jordan
Interests: remote sensing; mineral exploration; sabkha environments; environmental pollution; sand dunes

E-Mail Website
Guest Editor
Earth Sciences Department, Sultan Qaboos University, Al Seeb 123, Oman
Interests: economic geology; precambrian geology; geochemistry

Special Issue Information

Dear Colleagues,

In many environments where traditional field surveys are difficult and time-consuming, remote sensing technology enables quick delineation of mineralized zones over wide areas at minimum cost and effort. Minerals and rocks are identified based on their mineral absorption properties present in the visible, near-infrared (VNIR), shortwave infrared (SWIR) (0.4–2.5 μm), and thermal infrared (TIR) (8-12 μm) wavelength regions. Iron oxides and hydroxides are characterized in the spectra region of the VNIR (0.4 to 1.1 µm) region, whereas the SWIR absorption features (2.0 to 2.5 µm) distinguish the spectra of carbonates, clay minerals, and sulphates. TIR distinguishes the spectra of silicates. Multispectral and hyperspectral remote sensing data are used for mineral exploration in various environments to delineate structural elements that may have controlled mineralization as well as alteration zones. Mineral exploration in these environments is examined using numerous image processing techniques, such as minimum noise fraction (MNF), principal component analysis (PCA), band ratio (BR), matched filtering (MF), machine learning, and image classification.

Dr. Habes A. Ghrefat
Dr. Salah Al-Khirbash
Guest Editors

Manuscript Submission Information

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Keywords

  • hydrothermal alteration
  • multispectral and hyperspectral remote sensing
  • arid and semi-arid environments
  • mineralization

Published Papers (3 papers)

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Research

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19 pages, 16300 KiB  
Article
Machine Learning and EPCA Methods for Extracting Lithology–Alteration Multi-Source Geological Elements: A Case Study in the Mining Evaluation of Porphyry Copper Ores in the Gondwana Metallogenic Belt
by Chunhui Liu, Xingyu Liu, Man Hou, Sensen Wu, Luoqi Wang, Jie Feng and Chunxia Qiu
Minerals 2023, 13(7), 858; https://doi.org/10.3390/min13070858 - 25 Jun 2023
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Abstract
The location and development of porphyry copper deposits is a key issue for the mining industry. In this study, the Gondwana metallogenic belt was chosen as the study area to compare multiple methods for extracting multi-source geological elements to maximize the accuracy of [...] Read more.
The location and development of porphyry copper deposits is a key issue for the mining industry. In this study, the Gondwana metallogenic belt was chosen as the study area to compare multiple methods for extracting multi-source geological elements to maximize the accuracy of the datasets used for mining evaluation and to use them to assess porphyry copper mineability. By comparison, a support vector machine (SVM) with an overall classification accuracy of 97.6573% and a Kappa coefficient of 0.9806 was used to extract the lithological distribution of the study area. Spectral feature-enhanced principal component analysis (EPCA) was combined with ASTER images to extract alteration information, with significant improvements in spatial aggregation and overall area compared to other alteration extraction methods, while a hierarchical alteration interpolation method was proposed to overcome the limitations of relying solely on remote sensing images to obtain surface alteration information and qualitatively extend deep alteration information. In addition, by overlaying various geoscientific factors affecting copper mineralization and mining, a Pearson correlation analysis is carried out in conjunction with currently proven or mined copper occurrences, and a weight of evidence approach is used to classify the study area into four mineability classes, which is important for narrowing down potential target areas for mineral exploration and assessing their mining value while contributing to an in-depth understanding of the role of geological elements in mineralization and development. Full article
(This article belongs to the Special Issue Mineral Exploration Based on Remote Sensing)
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21 pages, 59889 KiB  
Article
Application of ASTER Remote Sensing Data to Porphyry Copper Exploration in the Gondwana Region
by Chunhui Liu, Chunxia Qiu, Luoqi Wang, Jie Feng, Sensen Wu and Yuanyuan Wang
Minerals 2023, 13(4), 501; https://doi.org/10.3390/min13040501 - 31 Mar 2023
Cited by 2 | Viewed by 2034
Abstract
Porphyry copper ore is a vital strategic mineral resource. It is often associated with significant hydrothermal alteration, which alters the original mineralogical properties of the rock. Extracting alteration information from remote sensing data is crucial for porphyry copper exploration. However, the current method [...] Read more.
Porphyry copper ore is a vital strategic mineral resource. It is often associated with significant hydrothermal alteration, which alters the original mineralogical properties of the rock. Extracting alteration information from remote sensing data is crucial for porphyry copper exploration. However, the current method of extracting hydrothermal alteration information from ASTER remote sensing data does not consider the influence of disturbing factors, such as topography, and ignores the weak report of surface minerals, which has significant limitations. Therefore, this paper selects the Gondwana region of the East Tethys–Himalayan tectonic domain as the study area, combines waveform calculation with principal component analysis methods, proposes a spectral feature-enhanced principal component analysis (EPCA) method, and constructs a model to complete the automatic selection of principal components for each scene image. The results show that the etching information extracted by the EPCA method is significantly better than the traditional Crosta method in terms of etching area and spatial aggregation and discovers several prospective mineralization areas that have not yet been explored and exploited, such as Sakya and Xietongmen counties in Rikaze, providing theoretical support for subsequent mineralization exploration and large-scale mineral extraction. Meanwhile, obtaining the alteration information of the whole area can help to understand the distribution of mineralizing elements from a macroscopic perspective in the future, which is of great scientific significance in order to deeply analyze the formation process of metal deposits in mineralizing areas and improve the theory of porphyry mineralization. Full article
(This article belongs to the Special Issue Mineral Exploration Based on Remote Sensing)
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Review

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26 pages, 13183 KiB  
Review
Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities
by Yansi Chen, Yunchen Wang, Feng Zhang, Yulong Dong, Zhihong Song and Genyuan Liu
Minerals 2023, 13(9), 1153; https://doi.org/10.3390/min13091153 - 31 Aug 2023
Cited by 2 | Viewed by 1918
Abstract
Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in [...] Read more.
Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions which includes the extensively reviewed prior research concerning the identification of lithology in vegetated regions, encompassing the utilized remote sensing data sources, and classification methodologies. Moreover, it offers a comprehensive overview of the application of remote sensing techniques in the domain of lithological mapping. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact the accuracy of lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to improve classification accuracy and the exploration of novel RS techniques and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas. Full article
(This article belongs to the Special Issue Mineral Exploration Based on Remote Sensing)
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