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Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques

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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 (16 December 2022) | Viewed by 37145

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Special Issue Editors

Dr. Amin Beiranvand Pour

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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

Dr. Omeid Rahmani

E-Mail Website
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

Dr. Mohammad Parsa

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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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multispectral remote sensing data afford a synoptic view of immense metallogenic provinces, such as Archaean granite-greenstone terranes for mineral exploration and ore-related lithological mapping, and the macroseepage and microseepage associated with hydrocarbon reservoirs and CO₂ sequestration in Ophiolite complexes. Landsat data series, Syste'm Pour l‘Observation de la Terre (SPOT) data series, Worldview-3 data series, Advanced Land Imager (ALI) data and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data have been successfully and continuously used for regional-scale mineralogical-lithological-structural mapping in metallogenic provinces and hydrocarbon exploration. Regional-scale mineral exploration in metallogenic provinces and hydrocarbon exploration in remote areas are challenging due to the difficultly of processing remote sensing big data and the variety of remote sensing datasets needed for different applications. Space-borne remote sensing big data sources are available, appropriate and free to low-cost for mineral and hydrocarbon exploration projects. Numerous image processing algorithms and Geographic Information System (GIS) modeling can be used for extracting spectral information related to alteration minerals, ore-related lithological units and microseepage related geochemical alterations. The fusion of extracted information using deep fusion learning techniques is developing progressively and is crucial for unraveling several image processing challenges. Although the techniques are subject to scientific interest for remote sensing in the mineral and hydrocarbon exploration community, generic implementation is still in the initial stages. This Special Issue is focused on the recent developments in the applications of multispectral remote sensing satellite data for mineral exploration in metallogenic provinces and onshore oil slick detection to offshore oil spill monitoring and hydrocarbon exploration. We are interested in innovative solutions for deep fusion learning techniques for remote sensing data processing and difficulties, and manuscript submissions are encouraged on a broad range of related mineral and hydrocarbon exploration themes. Researchers are encouraged to submit novel research or case studies that may include, but are not limited to, the following topics:

Innovative methods for fusing multispectral remote sensing satellite data for prospectivity mapping in vast metallogenic provinces;
Multispectral remote sensing satellite data for both macroseepage direct detection and microseepage indirect detection;
Mapping Ophiolite complexes to understand mineral carbonation and CO₂ sequestration using novel remote sensing approaches and modeling;
Geographic Information System (GIS) modeling for integrating different remote sensing datasets and geophysical and geochemical techniques for mineral exploration.

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

Multispectral remote sensing data
Big remote sensing data
Regional mineral mapping
Landsat data
ASTER
Mineral and hydrocarbon exploration projects
Ore minerals
Archaean granite-greenstone terranes
Data fusion
Ophiolite complexes
Hydrocarbon macroseepage and microseepage
Mineral carbonation
GIS

Published Papers (13 papers)

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Editorial

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5 pages, 201 KiB

Open AccessEditorial

Editorial for the Special Issue: “Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques”

by Amin Beiranvand Pour, Omeid Rahmani and Mohammad Parsa

Minerals 2023, 13(2), 193; https://doi.org/10.3390/min13020193 - 28 Jan 2023

Cited by 3 | Viewed by 1301

Abstract

Using multispectral remote sensing data in cooperation with big data processing and deep fusion learning techniques provides a new approach for mineral and hydrocarbon exploration [...] Full article

(This article belongs to the Special Issue Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques)

Research

Jump to: Editorial

31 pages, 8188 KiB

Open AccessEditor’s ChoiceArticle

Investigating the Capabilities of Various Multispectral Remote Sensors Data to Map Mineral Prospectivity Based on Random Forest Predictive Model: A Case Study for Gold Deposits in Hamissana Area, NE Sudan

by Abdallah M. Mohamed Taha, Yantao Xi, Qingping He, Anqi Hu, Shuangqiao Wang and Xianbin Liu

Minerals 2023, 13(1), 49; https://doi.org/10.3390/min13010049 - 28 Dec 2022

Cited by 8 | Viewed by 2300

Abstract

Remote sensing data provide significant information about surface geological features, but they have not been fully investigated as a tool for delineating mineral prospective targets using the latest advancements in machine learning predictive modeling. In this study, besides available geological data (lithology, structure, lineaments), Landsat-8, Sentinel-2, and ASTER multispectral remote sensing data were processed to produce various predictor maps, which then formed four distinct datasets (namely Landsat-8, Sentinel-2, ASTER, and Data-integration). Remote sensing enhancement techniques, including band ratio (BR), principal component analysis (PCA), and minimum noise fraction (MNF), were applied to produce predictor maps related to hydrothermal alteration zones in Hamissana area, while geological-based predictor maps were derived from applying spatial analysis methods. These four datasets were used independently to train a random forest algorithm (RF), which was then employed to conduct data-driven gold mineral prospectivity modeling (MPM) of the study area and compare the capability of different datasets. The modeling results revealed that ASTER and Sentinel-2 datasets achieved very similar accuracy and outperformed Landsat-8 dataset. Based on the area under the ROC curve (AUC), both datasets had the same prediction accuracy of 0.875. However, ASTER dataset yielded the highest overall classification accuracy of 73%, which is 6% higher than Sentinel-2 and 13% higher than Landsat-8. By using the data-integration concept, the prediction accuracy increased by about 6% (AUC: 0.938) compared with the ASTER dataset. Hence, these results suggest that the framework of exploiting remote sensing data is promising and should be used as an alternative technique for MPM in case of data availability issues. Full article

(This article belongs to the Special Issue Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques)

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19 pages, 9012 KiB

Open AccessEditor’s ChoiceArticle

Fusion of Multispectral Remote-Sensing Data through GIS-Based Overlay Method for Revealing Potential Areas of Hydrothermal Mineral Resources

by Saad S. Alarifi, Mohamed Abdelkareem, Fathy Abdalla, Ismail S. Abdelsadek, Hisham Gahlan, Ahmad. M. Al-Saleh and Mislat Alotaibi

Minerals 2022, 12(12), 1577; https://doi.org/10.3390/min12121577 - 09 Dec 2022

Cited by 9 | Viewed by 2281

Abstract

Revealing prospective locations of hydrothermal alteration zones (HAZs) is an important technique for mineral prospecting. In this study, we used multiple criteria inferred from Landsat-8 OLI, Sentinel-2, and ASTER data using a GIS-based weighted overlay multi-criteria decision analysis approach to build a model for the delineating of hydrothermal mineral deposits in the Khnaiguiyah district, Saudi Arabia. The utilized algorithms revealed argillic, phyllic, and propylitic alteration characteristics. The HAZs map resulted in the identification of six zones based on their mineralization potential, providing a basis for potential hydrothermal mineral deposit assessment exploration, which was created by the fusion of mineral bands indicators designated very low, low, moderate, good, very good, and excellent and covers 31.36, 28.22, 20.49, 10.99, 6.35, and 2.59%. Based on their potential for hydrothermal mineral potentiality, the discovered zones match gossans related to sulfide mineral alteration zones, as demonstrated by previous studies. Full article

(This article belongs to the Special Issue Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques)

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16 pages, 5913 KiB

Open AccessArticle

Depositional Environment and Hydrocarbon Distribution in the Silurian–Devonian Black Shales of Western Peninsular Malaysia Using Spectroscopic Characterization

by Monera Adam Shoieb, Haylay Tsegab Gebretsadik, Syed Muhammad Ibad and Omeid Rahmani

Minerals 2022, 12(12), 1501; https://doi.org/10.3390/min12121501 - 24 Nov 2022

Cited by 1 | Viewed by 1452

Abstract

The present study aimed to evaluate the hydrocarbon functional groups, aromaticity degree, and depositional environment in the Silurian–Devonian Kroh black shales of western peninsular Malaysia. Fourier transform infrared spectroscopy (FTIR) was applied to measure the hydrocarbon functional groups in the sedimentary succession and associated organic matter of the black shale samples. The results showed that aromatic C=C stretching, aromatic C-H out-of-plane, aromatic C-H in-plane, and aliphatic =C–H bending are the major hydrocarbon functional groups in the Kroh shales. Also, ultraviolet-visible spectroscopy (UV-Vis) was used to evaluate the type of humic substance and analyze the sample extract ratios of E4/E6. It was revealed that the methanol-treated Kroh shale samples ranged from 0.00048 to 0.12 for E4 and 0.0040 to 0.99 for E6. The lower E4/E6 ratio (>5) indicates the dominance of humic acid over fulvic acid in the Kroh shales. The Kroh shale samples’ total organic carbon content (TOC) ranges from 0.33 to 8.5 wt.%, analyzed by a multi-N/C 3100 TOC/TNb analyzer. The comparison study revealed that the TOC content of the Kroh shale has close obtainable values for the Montney shales of Canada. Furthermore, both hydrocarbon functional groups from FTIR, and the E4/E6 ratio from UV-Vis show no correlation with TOC content. It is revealed that humic acid, aromatic, and aliphatic hydrocarbons are not the controlling factors of the enrichment of organic matter in the Kroh shales. Conversely, a positive correlation between aliphatic and aromatic hydrocarbons in the Kroh shales indicated that organic matter is thermally overmatured. The presence of humic acid and enrichment of aromatic hydrocarbons in the Kroh shales demonstrated that the organic matter in these shales contains plant-derived hydrophilic minerals, i.e., terrestrial in origin. These findings may provide clues on the depositional and thermal maturation of organic matter for the exploration efforts into the pre-Tertiary sedimentary successions of the peninsular. Full article

(This article belongs to the Special Issue Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques)

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21 pages, 16746 KiB

Open AccessArticle

Identification of Radioactive Mineralized Lithology and Mineral Prospectivity Mapping Based on Remote Sensing in High-Latitude Regions: A Case Study on the Narsaq Region of Greenland

by Li He, Pengyi Lyu, Zhengwei He, Jiayun Zhou, Bo Hui, Yakang Ye, Huilin Hu, Yanxi Zeng and Li Xu

Minerals 2022, 12(6), 692; https://doi.org/10.3390/min12060692 - 30 May 2022

Cited by 5 | Viewed by 2300

Abstract

The harsh environment of high-latitude areas with large amounts of snow and ice cover makes it difficult to carry out full geological field surveys. Uranium resources are abundant within the Ilimaussaq Complex in the Narsaq region of Greenland, where the uranium ore body is strictly controlled by the Lujavrite formation, which is the main ore-bearing rock in the complex rock mass. Further, large aggregations of radioactive minerals appear as thermal anomalies on remote sensing thermal infrared imagery, which is indicative of deposits of highly radioactive elements. Using a weight-of-evidence analysis method that combines machine-learned lithological classification information with information on surface temperature thermal anomalies, the prediction of radioactive element-bearing deposits at high latitudes was carried out. Through the use of Worldview-2 (WV-2) remote sensing images, support vector machine algorithms based on texture features and topographic features were used to identify Lujavrite. In addition, the distribution of thermal anomalies associated with radioactive elements was inverted using Landsat 8 TIRS thermal infrared data. From the results, it was found that the overall accuracy of the SVM algorithm-based lithology mapping was 89.57%. The surface temperature thermal anomaly had a Spearman correlation coefficient of 0.63 with the total airborne measured uranium gamma radiation. The lithological classification information was integrated with surface temperature thermal anomalies and other multi-source remote sensing mineralization elements to calculate mineralization-favorable areas through a weight-of-evidence model, with high-value mineralization probability areas being spatially consistent with known mineralization areas. In conclusion, a multifaceted remote sensing information finding method, focusing on surface temperature thermal anomalies in high-latitude areas, provides guidance and has reference value for the exploration of potential mineralization areas for deposits containing radioactive elements. Full article

(This article belongs to the Special Issue Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques)

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19 pages, 5829 KiB

Open AccessArticle

Fusion of Lineament Factor (LF) Map Analysis and Multifractal Technique for Massive Sulfide Copper Exploration: The Sahlabad Area, East Iran

by Aref Shirazi, Ardeshir Hezarkhani and Amin Beiranvand Pour

Minerals 2022, 12(5), 549; https://doi.org/10.3390/min12050549 - 28 Apr 2022

Cited by 13 | Viewed by 2041

Abstract

Fault systems are characteristically one of the main factors controlling massive sulfide mineralization. The main objective of this study was to investigate the relationship between fault systems and host lithology with massive sulfide copper mineralization in the Sahlabad area, South Khorasan province, east of Iran. Subsequently, the rose diagram analysis, Fry analysis, lineament factor (LF) map analysis and multifractal technique were implemented for geological and geophysical data. Airborne geophysical analysis (aeromagnetometric data) was executed to determine the presence of intrusive and extrusive masses associated with structural systems. Accordingly, the relationship between the formation boundaries and the fault system was understood. Results indicate that the NW-SE fault systems are controlling the lithology of the host rock for copper mineralization in the Sahlabad area. Hence, the NW-SE fault systems are consistent with the main trend of lithological units related to massive sulfide copper mineralization in the area. Additionally, the distance of copper deposits, mines and indices in the Sahlabad area with fault systems was calculated and interpreted. Fieldwork results confirm that the NW-SE fault systems are entirely matched with several massive sulfide copper mineralizations in the area. This study demonstrates that the fusion of lineament factor (LF) map analysis and multifractal technique is a valuable and inexpensive approach for exploring massive sulfide mineralization in metallogenic provinces. Full article

(This article belongs to the Special Issue Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques)

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15 pages, 11079 KiB

Open AccessArticle

Depth Estimation of Sedimentary Sections and Basement Rocks in the Bornu Basin, Northeast Nigeria Using High-Resolution Airborne Magnetic Data

by Stephen E. Ekwok, Ogiji-Idaga M. Achadu, Anthony E. Akpan, Ahmed M. Eldosouky, Chika Henrietta Ufuafuonye, Kamal Abdelrahman and David Gómez-Ortiz

Minerals 2022, 12(3), 285; https://doi.org/10.3390/min12030285 - 24 Feb 2022

Cited by 15 | Viewed by 2655

Abstract

This study involves the use of high-resolution airborne magnetic data to evaluate the thicknesses of sedimentary series in the Bornu Basin, Northeast Nigeria, using three depth approximation techniques (source parameter imaging, standard Euler deconvolution, and 2D GM-SYS forward modelling methods). Three evenly spaced profiles were drawn in the N-S direction on the total magnetic intensity map perpendicular to the regional magnetic structures. These profiles were used to generate three 2-D models. The magnetic signatures were visually assessed to determine the thickness of depo-centres and the position of intrusions. The thicknesses of sedimentary series based on source parameter imaging results are approximately ranged 286 to 615 m, 695 to 1038 m, and 1145 to 5885 m for thin, intermediate, and thick sedimentation, respectively. Similarly, the standard Euler deconvolution result shows thin (130 to 917 m), intermediate (1044 to 1572 m), and thick (1725 to 5974 m) sedimentation. The magnetic model of Profile 1, characterized by two major breaks, shows that the igneous intrusions and basement rocks are covered by sediments with thickness varying from 300 to <3500 m, while Profile 2 has a maximum estimated depth value of about 5000 m at the southern part. Furthermore, the Profile 3 model shows sediment thicknesses of 2500 and 4500 m in the northern and southern flanks of the profile, respectively. The maximum sediment thickness value from the various depth estimation methods used in this study correlate relatively well with each other. Furthermore, the anomalous depth zone revealed by the 2D forward models coincides with the locality of the thick sedimentation revealed by the source parameter imaging and standard Euler-deconvolution (St-ED) methods. The maximum depth values obtained from the various depth estimation methods used in this study correlated strongly with each other. The widespread occurrence of short-wavelength anomalies in the southern part of the study area as indicated by the jagged nature of the magnetic signature was validated by the analytic signal and upward-continuation results. Generally, it was observed that the southern part of the research area is characterized by thick sedimentation and igneous intrusions. Full article

(This article belongs to the Special Issue Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques)

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17 pages, 10330 KiB

Open AccessArticle

Towards Understanding the Source of Brine Mineralization in Southeast Nigeria: Evidence from High-Resolution Airborne Magnetic and Gravity Data

by Stephen E. Ekwok, Anthony E. Akpan, Ogiji-Idaga M. Achadu, Cherish E. Thompson, Ahmed M. Eldosouky, Kamal Abdelrahman and Peter Andráš

Minerals 2022, 12(2), 146; https://doi.org/10.3390/min12020146 - 25 Jan 2022

Cited by 21 | Viewed by 2806

Abstract

Investigation into understanding the genesis of brines in southeast Nigeria was carried out utilizing high-resolution potential field (HRPF) data. This study reveals that igneous intrusions and associated hydrothermal fluids are responsible for brine generation. The obtained result of the analytic signal revealed the locations and spatial distribution of short- and long-wavelength geologic structures associated with igneous intrusions. The low pass filtering, upward continuation, and 2D modelling procedures showed key synclinal structures which coincided well with the location of brine fields. The results showed that salt ponds are common in the neighborhood of igneous intrusions. To validate this finding, a conceptual model describing igneous-related hydrothermal circulation systems that are driven by convective cells of the hydrothermal fluid and overburden loads was generated. This model fits reasonably well into the overall stratigraphic and geologic framework of the study area. Full article

(This article belongs to the Special Issue Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques)

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24 pages, 14292 KiB

Open AccessArticle

Geochemical and Hydrothermal Alteration Patterns of the Abrisham-Rud Porphyry Copper District, Semnan Province, Iran

by Timofey Timkin, Mahnaz Abedini, Mansour Ziaii and Mohammad Reza Ghasemi

Minerals 2022, 12(1), 103; https://doi.org/10.3390/min12010103 - 16 Jan 2022

Cited by 10 | Viewed by 4030

Abstract

In this study, the zonality method has been used to separate geochemical anomalies and to calculate erosional levels in the regional scale for porphyry-Cu deposit, Abrisham-Rud (Semnan province, East of Iran). In geochemical maps of multiplicative haloes, the co-existence of both the supra-ore elements and sub-ore elements local maxima implied blind mineralization in the northwest of the study area. Moreover, considering the calculated zonality indices and two previously presented geochemical models, E and NW of the study have been introduced as ZDM and BM, respectively. For comparison, the geological layer has been created by combining rock units, faults, and alterations utilizing the K-nearest neighbor (KNN) algorithm. The rock units and faults have been identified from the geological map; moreover, alterations have been detected by using remote sensing and ASTER images. In the geological layer map related to E of the study area, many parts have been detected as high potential areas; in addition, both geochemical and geological layer maps only confirmed each other at the south of this area and suggested this part as high potential mineralization. Therefore, high potential areas in the geological layer map could be related to the mineralization or not. Due to the incapability of the geological layer in identifying erosional levels, mineralogy investigation could be used to recognize this level; however, because of the high cost, mineralogy is not recommended for application on a regional scale. The findings demonstrated that the zonality method has successfully distinguished geochemical anomalies including BM and ZDM without dependent on alteration and was able to predict erosional levels. Therefore, this method is more powerful than the geological layer. Full article

(This article belongs to the Special Issue Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques)

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25 pages, 10380 KiB

Open AccessArticle

Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery

by Mastoureh Yousefi, Seyed Hassan Tabatabaei, Reyhaneh Rikhtehgaran, Amin Beiranvand Pour and Biswajeet Pradhan

Minerals 2021, 11(11), 1235; https://doi.org/10.3390/min11111235 - 06 Nov 2021

Cited by 15 | Viewed by 2257

Abstract

The application of machine learning (ML) algorithms for processing remote sensing data is momentous, particularly for mapping hydrothermal alteration zones associated with porphyry copper deposits. The unsupervised Dirichlet Process (DP) and the supervised Support Vector Machine (SVM) techniques can be executed for mapping hydrothermal alteration zones associated with porphyry copper deposits. The main objective of this investigation is to practice an algorithm that can accurately model the best training data as input for supervised methods such as SVM. For this purpose, the Zefreh porphyry copper deposit located in the Urumieh-Dokhtar Magmatic Arc (UDMA) of central Iran was selected and used as training data. Initially, using ASTER data, different alteration zones of the Zefreh porphyry copper deposit were detected by Band Ratio, Relative Band Depth (RBD), Linear Spectral Unmixing (LSU), Spectral Feature Fitting (SFF), and Orthogonal Subspace Projection (OSP) techniques. Then, using the DP method, the exact extent of each alteration was determined. Finally, the detected alterations were used as training data to identify similar alteration zones in full scene of ASTER using SVM and Spectral Angle Mapper (SAM) methods. Several high potential zones were identified in the study area. Field surveys and laboratory analysis were used to validate the image processing results. This investigation demonstrates that the application of the SVM algorithm for mapping hydrothermal alteration zones associated with porphyry copper deposits is broadly applicable to ASTER data and can be used for prospectivity mapping in many metallogenic provinces around the world. Full article

(This article belongs to the Special Issue Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques)

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17 pages, 12017 KiB

Open AccessArticle

ASTER-Based Remote Sensing Image Analysis for Prospection Criteria of Podiform Chromite at the Khoy Ophiolite (NW Iran)

by Behnam Mehdikhani and Ali Imamalipour

Minerals 2021, 11(9), 960; https://doi.org/10.3390/min11090960 - 02 Sep 2021

Cited by 9 | Viewed by 2843

Abstract

A single chromite deposit occurrence is found in the serpentinized harzburgite unit of the Khoy ophiolite complex in northwest Iran, which is surrounded by dunite envelopes. This area has mountainous features and extremely rugged topography with difficult access, so prospecting for chromite deposits by conventional geological mapping is challenging. Therefore, using remote sensing techniques is very useful and effective, in terms of saving costs and time, to determine the chromite-bearing zones. This study evaluated the discrimination of chromite-bearing mineralized zones within the Khoy ophiolite complex by analyzing the capabilities of ASTER satellite data. Spectral transformation methods such as optimum index factor (OIF), band ratio (BR), spectral angle mapper (SAM), and principal component analysis (PCA) were applied on the ASTER bands for lithological mapping. Many chromitite lenses are scattered in this ophiolite, but only a few have been explored. ASTER bands contain improved spectral characteristics and higher spatial resolution for detecting serpentinized dunite in ophiolitic complexes. In this study, after the correction of ASTER data, many conventional techniques were used. A specialized optimum index factor RGB (8, 6, 3) was developed using ASTER bands to differentiate lithological units. The color composition of band ratios such as RGB ((4 + 2)/3, (7 + 5)/6, (9 + 7)/8), (4/1, 4/7, 4/5), and (4/3 × 2/3, 3/4, 4/7) produced the best results. The integration of information extracted from the image processing algorithms used in this study mapped most of the lithological units of the Khoy ophiolitic complex and new prospecting targets for chromite exploration were determined. Furthermore, the results were verified by comprehensive fieldwork and previous studies in the study area. The results of this study indicate that the integration of information extracted from the image processing algorithms could be a broadly applicable tool for chromite prospecting and lithological mapping in mountainous and inaccessible regions such as Iranian ophiolitic zones. Full article

(This article belongs to the Special Issue Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques)

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17 pages, 50371 KiB

Open AccessArticle

Spatial Component Analysis to Improve Mineral Estimation Using Sentinel-2 Band Ratio: Application to a Greek Bauxite Residue

by Roberto Bruno, Sara Kasmaeeyazdi, Francesco Tinti, Emanuele Mandanici and Efthymios Balomenos

Minerals 2021, 11(6), 549; https://doi.org/10.3390/min11060549 - 21 May 2021

Cited by 6 | Viewed by 2098

Abstract

Remote sensing can be fruitfully used in the characterization of metals within stockpiles and tailings, produced from mining activities. Satellite information, in the form of band ratio, can act as an auxiliary variable, with a certain correlation with the ground primary data. In the presence of this auxiliary variable, modeled with nested structures, the spatial components without correlation can be filtered out, so that the useful correlation with ground data grows. This paper investigates the possibility to substitute in a co-kriging system, the whole band ratio information, with only the correlated components. The method has been applied over a bauxite residues case study and presents three estimation alternatives: ordinary kriging, co-kriging, component co-kriging. Results have shown how using the most correlated component reduces the estimation variance and improves the estimation results. In general terms, when a good correlation with ground samples exists, co-kriging of the satellite band-ratio Component improves the reconstruction of mineral grade distribution, thus affecting the selectivity. On the other hand, the use of the components approach exalts the distance variability. Full article

(This article belongs to the Special Issue Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques)

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36 pages, 29426 KiB

Open AccessArticle

by Mohamed Abd El-Wahed, Basem Zoheir, Amin Beiranvand Pour and Samir Kamh

Minerals 2021, 11(5), 474; https://doi.org/10.3390/min11050474 - 30 Apr 2021

Cited by 35 | Viewed by 5121

Abstract

Space-borne multispectral and radar data were used to comprehensively map geological contacts, lithologies and structural elements controlling gold-bearing quartz veins in the Wadi Hodein area in Egypt. In this study, enhancement algorithms, band combinations, band math (BM), Principal Component Analysis (PCA), decorrelation stretch and mineralogical indices were applied to Landsat-8 OLI, ASTER and ALOS PALSAR following a pre-designed flow chart. Together with the field observations, the results of the image processing techniques were exported to the GIS environment and subsequently fused to generate a potentiality map. The Wadi Hodein shear belt is a ductile shear corridor developed in response to non-coaxial convergence and northward escape tectonics that accompanied the final stages of terrane accretion and cratonization (~680–600 Ma) in the northern part of the Arabian–Nubian Shield. The evolution of this shear belt encompassed a protracted ~E–W shortening and recurrent sinistral transpression as manifested by east-dipping thrusts and high-angle reverse shear zones. Gold-mineralized shear zones cut heterogeneously deformed ophiolites and metavolcaniclastic rocks and attenuate in and around granodioritic intrusions. The gold mineralization event was evidently epigenetic in the metamorphic rocks and was likely attributed to rejuvenated tectonism and circulation of hot fluids during transpressional deformation. The superposition of the NW–SE folds by NNW-trending, kilometer scale tight and reclined folds shaped the overall framework of the Wadi Hodein belt. Shallow NNW- or SSE-plunging mineral and stretching lineations on steeply dipping shear planes depict a considerable simple shear component. The results of image processing complying with field observations and structural analysis suggest that the coincidence of shear zones, hydrothermal alteration and crosscutting dikes in the study area could be considered as a model criterion in exploration for new gold targets. Full article

(This article belongs to the Special Issue Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques)

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