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001-es BibID:	BIBFORM131916
035-os BibID:	(Scopus)105013101506 (WoS)001559258400001
Első szerző:	Daoud Abazar Mohamed Ahmed (földtudományi kutató, geológus, geográfus)
Cím:	Machine Learning-Based Lithological Mapping and Mineral Prospecting Using Hyperspectral and Multispectral Remote Sensing in Wadi Halfa, North Sudan / Abazar M. A. Daoud, Ali Shebl, Mutwakil Nafi, Abdelmajeed A. Elrasheed, Arpád Csámer, Péter Rozsa
Dátum:	2025
ISSN:	1464-343X
Megjegyzések:	At present, the global demand for mineral resources is critical, leading nations to focus on exploration. Remote sensing is a cost-effective tool, especially in harsh terrains. This study conducted lithological mapping in Wadi Halfa, North Sudan, using algorithm-based remote sensing, field observations, and petrographical analysis to detect iron ore and barite deposits. Multisensor optical datasets (L9, L8, and S2) were integrated to effectively delineate the lithological units. In addition, PRISMA hyperspectral data, with its detailed spectral signatures, improved spatial distribution patterns of barite and iron oxides across the study area. Image processing techniques (false colour composites, principal component analysis, minimum noise friction, band ratios) detected hydroxyl-bearing minerals, ferric, and ferrous oxides. Support Vector Machine (SVM), Artificial Neural Network (ANN), and Mahalanobis Distance Classifier (MDC) achieved overall accuracies of 95.51 %, 94.59 %, and 98.99 %, respectively. The study helped interpret the spatial relationship between barite and iron oxides. Four types of iron ore with more than three distinct layers were identified, including (a) oolitic ironstone, (b) ferruginous sandstone, (c) ferruginous ironstone, and (d) Banded Iron Formation (BIF) during field investigations, petrographic examinations, and chemical analysis validated remote sensing findings, revealing iron ore (62.7 % Fe) and barite (63.9 % Ba) concentrations. An economic assessment confirmed the presence of economic reserves suitable for exploitation. This research is recommended for broader application, particularly in machine learning for delineating iron ore and barite deposits in complex sedimentary sequences. The realization of machine learning algorithms emphasizes their potential to enhance lithological mapping in sedimentary sequences, suggesting a promising direction for future research.
Tárgyszavak:	Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Support vector machine Artificial neural network Mahalanobis Iron ore Barite Economic reserves
Megjelenés:	Journal Of African Earth Sciences. - 232 (2025), p. 1-22. -
További szerzők:	Shebl, Ali (1992-) (geológus) Nafi, Mutwakil Abdelmajeed, Adam Elrasheed Ali (1988-) (Geologist) Csámer Árpád (1976-) (geológus) Rózsa Péter (1956-) (petrográfus)
Pályázati támogatás:	Stipendium Hungaricum Scholarship Program Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM124592
Első szerző:	Daoud Abazar Mohamed Ahmed (földtudományi kutató, geológus, geográfus)
Cím:	Exploring Iron Ore and Barite Deposits through Multiscale Analysis: A Case Study in Wadi Halfa, North Sudan / Abazar M A Daoud, Mutwakil Nafi, Péter Rózsa
Dátum:	2024
Megjegyzések:	Within the framework of modern mineral exploration, particularly in conflict-susceptible regions, rapid and cost-effective remote sensing has become an available tool for geologists. This paper focuses on the examination of clastic sediments around the region of Wadi Halfa North Sudan, employing an innovative approach that integrates remote sensing, field observations, and petrographical analysis to identify iron ore and barite deposits. Utilization of image processing techniques such as band ratios (BR), false color composites (FCC) were applied for the detection of hydroxyl-bearing minerals, ferric, and ferrous iron oxides (B6/B7), (B4/B2), (B5/B6) and barite (B7/B6) respectively; four types of iron ore and barite with different distinct layers were detected and identified. Petrographical and chemical analysis of rock samples validate the remote sensing findings, indicating significant concentrations of iron ore (46.01% for Fe+2) and barite (63.9% for Ba+), respectively. The final geological map generated by composed bands R(B6/B7), G(B4/B3), and B(B5/B7), R(B6/B7), G(B4/B2), B(B4/B11) in Landsat 8 OLI and Sentinel S2 respectively reveals major geological variations between lithological units of different ages with new finding of another resources of iron ores around the study area. Results obtained from the combination of remote sensing and field observations provide valuable information for future exploration and assessment of these critical mineral resources, which are of significant economic importance for Sudan and the region and can be applied for similar areas in other arid semi-arid regions.
ISBN:	978-963-8161-23-9
Tárgyszavak:	Természettudományok Földtudományok előadáskivonat könyvrészlet
Megjelenés:	54. Meeting of Young Geoscientists - 54. Ifjú Szakemberek Ankétja: Absztrakt kötet. - p. 18. -
További szerzők:	Nafi, Mutwakil Rózsa Péter (1956-) (petrográfus)
Internet cím:	Szerző által megadott URL Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon: