2022
DOI: 10.3390/rs14143456
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Hydrothermal Alteration Mapping Using Landsat 8 and ASTER Data and Geochemical Characteristics of Precambrian Rocks in the Egyptian Shield: A Case Study from Abu Ghalaga, Southeastern Desert, Egypt

Abstract: This study evaluates the geological attributes of rocks within the Abu Ghalaga area using spatial, geochemical, and petrographic approaches. ASTER and Landsat imagery processed using band ratio and principal component analysis were used to map hydrothermal alterations, while a regional tectonic evaluation was based on automated extraction of lineaments from a digital elevation model. Geochemical and petrographic analyses were then employed for discrete scale evaluation of alteration patterns of rocks across th… Show more

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Cited by 15 publications
(4 citation statements)
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“…The area consists mainly of metavolcanics, gabbroic rocks, and gneissose granites (granodiorite and tonalite), as well as late tectonic intrusions associated with a great deal of mafic to felsic dykes and quartz veins [14]; see Figure 3. The gabbroic rocks are considered as one of the main host rock types of Fe-Ti ore deposits and contain the famous ilmenite ore.…”
Section: Geological Settingmentioning
confidence: 99%
“…The area consists mainly of metavolcanics, gabbroic rocks, and gneissose granites (granodiorite and tonalite), as well as late tectonic intrusions associated with a great deal of mafic to felsic dykes and quartz veins [14]; see Figure 3. The gabbroic rocks are considered as one of the main host rock types of Fe-Ti ore deposits and contain the famous ilmenite ore.…”
Section: Geological Settingmentioning
confidence: 99%
“…As a crucial role in determining the formation deposits, the geological structures can be identified through various spatial data 16,17 . In this research, the focus will be on automatic lineament extraction.…”
Section: Automated Lineament Extractionmentioning
confidence: 99%
“…Various approaches have been developed to identify lithological units for geological mapping. These encompass a range of approaches, from statistical techniques such as principal component analysis, to spectral analysis methods such as band ratios and spectral indices 1 6 . In addition, machine learning algorithms, both supervised and unsupervised, such as random forests (RF), support vector machines (SVM) and K-Means, have emerged as powerful tools.…”
Section: Introductionmentioning
confidence: 99%