Összesen 2 találat.


001-es BibID:BIBFORM104549
035-os BibID:(cikkazonosító)105184 (WoS)000881771600001 (Scopus)85142453471
Első szerző:Ahmed, Abdelhakam Esmaeil Mohamed (Food Engineer)
Cím:Effective delineation of rare metal-bearing granites from remote sensing data using machine learning methods: A case study from the Umm Naggat Area, Central Eastern Desert, Egypt / Mohamed A. Abdelkader, Yasushi Watanabe, Ali Shebl, Hanna A. El-Dokouny, Maher Dawoud, Árpád Csámer
Megjegyzések:Albitized granite (ABG) is considered as one of the most significant hosts of rare metals (RMs). Consequently, adequate recognition of ABG through proper lithological discrimination highly increases the targeting of rare metal resources. In order to delineate outcrops of ABG from satellite data, our study integrates eight image enhancement techniques, including optimum index factor, false color composites, band rationing, relative band depth, independent component analysis, principal component analysis, decorrelation stretch, minimum noise fraction transform, and spectral indices ratios, for the interpretation of ASTER and Sentinel-2 (S2) datasets. This integrated approach allows the effective discrimination of AGB outcrops in the Umm Naggat area, Central Eastern Desert, Egypt. The interpretation maps derived from these integrated image processing techniques were systematically verified in the field and formed the base for the feature selection process (i.e., training and testing data delineation) of different lithologies supported by the support vector machine algorithm (SVM). In order to produce a high-quality lithological interpretation map, SVM was applied to Sentinel-2, ASTER, and combined ASTER-S2 datasets. The fused ASTER-S2 classification properly delineates ABG, as verified by our field investigations and confirmed by previous geological maps. Furthermore, comprehensive structural analysis (lineaments extraction and their density map) and hydrothermal alteration detection were performed to check the spatial association between the distribution of ABG, higher density zones, and highly altered areas, that in turn, could shed light on new potentially mineralized zones and proposed exploration targets. Our study reveals new ABG occurrences mainly situated in the southern and southwestern parts of the study area, and it confirms the location of known mineralized zones in the northern part of the Umm Naggat region. The distribution of ABG and its spatial correlation with alteration and high structural density zones suggest that rare-metal mineralization is mostly structurally controlled (NW, NNW, NNE, and N-S), demonstrating the higher possibility of metasomatic enrichment of rare-metals within the study area. Our study provides an updated geological map of the study area based on the SVM-supported interpretation of ASTER-S2 data. Importantly, the results reveal a high exploration potential for rare-metal mineralization at Umm Naggat and defining new anomalies for follow-up work by geochemical soil surveys.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
Support Vector Machine
Albitized granite
Integrated image processing techniques
Rare-metal exploration
Central Eastern Desert
Megjelenés:Ore Geology Reviews. - 150 (2022), p. 1-26. -
További szerzők:Watanabe, Yasushi Shebl, Aly (1992-) (geológus) El-Dokouny, Hanna A. Dawoud, Maher Csámer Árpád (1976-) (geológus)
Internet cím:Szerző által megadott URL
Intézményi repozitóriumban (DEA) tárolt változat


001-es BibID:BIBFORM116157
035-os BibID:(cikkazonosító)105652 (WoS)001078595200001 (Scopus)85171617945
Első szerző:Shebl, Aly (geológus)
Cím:PRISMA hyperspectral data for lithological mapping in the Egyptian Eastern Desert : Evaluating the support vector machine, random forest, and XG boost machine learning algorithms / Ali Shebl, David Abriha, Amr S. Fahil, Hanna A. El-Dokouny, Abdelmajeed A. Elrasheed, Arpád Csámer
Megjegyzések:In essence, targeting mineralization necessitates exact structural delineation and thorough lithological mapping. The latter is still a challenge for geologists and its lack hinders meticulous exploration for various mineralizations. Here we show for the first time over a case study from Arabian Nubian Shield (ANS), the application of hyperspectral PRISMA (PRecursore IperSpettrale della Missione Applicativa) data for objective lithological mapping using the well-known Random Forest (RF), XGboost (XGB), and Support Vector Machine (SVM) algorithms. Our results manifested the worthiness of PRISMA data in further lithological mapping, especially with SVM with a resultant accuracy depending mainly on the input data combination. Upon field verification, the current research reveals the usefulness of PRISMA and its preceding four principal components in delivering a detailed lithological map for the study area. Additionally, the eligibility of RF, XGB, and SVM was confirmed in delivering acceptable results. SVM exceeds XGB and RF in their overall accuracy (95 %, 92 %, and 90 % for SVM, XGB, and RF respectively). Our research strongly recommends blending the vantages of Machine Learning Algorithms' (MLAs) objectivity and the wealth of PRISMA spectral coverage for further precise lithological mapping before applicable mineral exploration programs in similar terrains.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
Lithological mapping
Arabian Nubian Shield
Megjelenés:Ore Geology Reviews. - 161 (2023), p. 1-16. -
További szerzők:Abriha Dávid (1995-) (geográfus) Fahil, Amr S. El-Dokouny, Hanna A. Abdelmajeed, Adam Elrasheed Ali Csámer Árpád (1976-) (geológus)
Pályázati támogatás:Stipendium Hungaricum scholarship
Internet cím:Szerző által megadott URL
Intézményi repozitóriumban (DEA) tárolt változat
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