Mapping the Expressions of Ore-Forming Processes Using Remote Spectral Imagery and Geochemical Techniques

A special issue of Mining (ISSN 2673-6489).

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 5224

Special Issue Editors


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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
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Guest Editor
Department of Earth Sciences, University of New Brunswick, Fredericton, NB, Canada
Interests: geochemical exploration; remote sensing; geomatics; geological mapping; mineral exploration; mining
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Guest Editor
Department of Natural Resources Engineering and Management, School of Science and Engineering, University of Kurdistan Hewlêr (UKH), Erbil 44001, Kurdistan Region, Iraq
Interests: mineral carbonation; CO2 sequestration; reservoir engineering; enhanced oil recovery (EOR); geochemical analysis; elemental analysis; energy conservation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The upsurge in consuming raw materials entices geologists to pinpoint further mineralized zones—a challenging undertaking to which mapping the expressions of ore-forming processes is fundamental. Remote sensing and geochemical techniques are broadly applied to serve this purpose and are pushing the envelope of mineral exploration. Remote sensing data offer synoptic observation from the Earth, which can be especially suitable of regional geological mapping and ore mineral exploration. Recently, orogenic gold, porphyry copper/gold, massive sulfide, epithermal gold, podiform chromite, uranium, magnetite, and iron oxide copper–gold deposits have been successfully explored using remote sensing satellite imagery. Remote sensing image processing methods for mineral exploration are developing and assimilating with other sources of geological data for detailed mapping high potential zones of ore mineralizations. Geochemical techniques in conjunction with remote sensing data can provide comprehensive information about mineralogy, geochemistry, and spectral characteristics of the alteration zones and mineral assemblages associated with ore mineralization. Integration of spectral and geochemical properties of alteration zones can provide detailed/low-cost information to locate drilling points in exploration campaigns for the discovery and evaluation of ore deposits. Further to this, recent advances in geochemical techniques can facilitate the recognition of deep-seated, blind, or even buried mineral deposits. This Special Issue is particularly motivated by the latest achievements of statistical techniques and machine learning models, which can be specifically applied to fuse remote sensing and geochemical data for mineral exploration purposes. 

The topics of interest include but are not limited to:

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

Dr. Amin Beiranvand Pour
Dr. Mohammad Parsa
Dr. Omeid Rahmani
Guest Editors

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Keywords

  • Remote sensing
  • Ore mineral exploration
  • Geochemical data
  • Integration of exploration data
  • Deep learning techniques
  • Machine learning
  • Geostatistics

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Published Papers (1 paper)

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Research

12 pages, 5418 KiB  
Article
Hybrid Fuzzy-Analytic Hierarchy Process (AHP) Model for Porphyry Copper Prospecting in Simorgh Area, Eastern Lut Block of Iran
by Vahid Khosravi, Aref Shirazi, Adel Shirazy, Ardeshir Hezarkhani and Amin Beiranvand Pour
Mining 2022, 2(1), 1-12; https://doi.org/10.3390/mining2010001 - 21 Dec 2021
Cited by 13 | Viewed by 3939
Abstract
The eastern Lut block of Iran has a high potential for porphyry copper mineralization due to the subduction tectonic regime. It is located in an inaccessible region and has harsh arid conditions for traditional mineral exploration campaigns. The objective of this study is [...] Read more.
The eastern Lut block of Iran has a high potential for porphyry copper mineralization due to the subduction tectonic regime. It is located in an inaccessible region and has harsh arid conditions for traditional mineral exploration campaigns. The objective of this study is to use Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data for porphyry copper exploration in Simorgh Area, eastern Lut block of Iran. Hydrothermal alteration zones such as argillic, phyllic and propylitic zones associated with porphyry copper systems in the study were identified using false color composition (FCC), band ratio (BR), principal component analysis (PCA) and minimal noise fraction (MNF). The thematic alteration layers extracted from FCC, BR, PCA and MNF were integrated using hybrid Fuzzy-AHP model to generate a porphyry copper potential map for the study area. Four high potential zones were identified in the central, western, eastern and northeastern of the study area. Fieldwork was used to validate the approach used in this study. This investigation exhibits that the use of hybrid Fuzzy-AHP model for the identification of hydrothermal alteration zones associated with porphyry copper systems that is typically applicable to ASTER data and can be used for porphyry copper potential mapping in many analogous metallogenic provinces. Full article
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