Abo Khashaba, Saif M., Hassan, Safaa M., Sadek, Mohamed F., Laban, Noureldin, Gabr, Safwat S., Metwalli, Mohamed R., Sami, Mabrouk, Alhejji, Suhail S., Sanislav, Ioan V., and Nagy, Mostafa (2026) Integrated remote sensing and aeromagnetic datasets for mapping iron mineralization potential in the El-Bahariya depression in the Western Desert of Egypt. Scientific Reports, 16. 19600.

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Abstract

The El-Bahariya depression in the Western Desert of Egypt is well-known for its iron ore deposits, with mineralization recorded in five well-known locations. This study is the first integration of hyperspectral PRISMA and aeromagnetic analysis for the Bahariya iron ore deposits. Automated lithological mapping of the study area was performed using machine learning algorithms (MLA) such as Random Forest (RF) and Support Vector Machine (SVM), applied to stacked (ASTER + Sentinel-2) data, achieving an overall accuracy of up to 91.73%, Kappa accuracy of 89.99%, and F1-score of 93.98%. Hyperspectral PRISMA data analysis identified diagnostic absorption features (1.95–2.3 μm) associated with hematite, goethite, and limonite. Advanced spectral methods, including Pixel Purity Index (PPI) and Spectral Angle Mapper (SAM), successfully discriminated between hematite and limonite concentrations, as validated by field surveys, an ASD spectroradiometer, and USGS laboratory spectra. Results revealed ferruginous sandstone distributions, iron-rich zones, and previously undetected high-potential mineralization targets. Complementary high-resolution aeromagnetic data, processed using edge detection filters and CET techniques (CET-GA, CET-PA), resolved structural controls on mineralization. Major NE-SW and NW-SE trending lineaments, alongside minor N-S and E-W structures, were identified as fluid conduits for hydrothermal iron oxide emplacement. Euler deconvolution constrained magnetic sources to shallow depths (< 2 km), aligning with surface-derived anomalies. High magnetic susceptibility zones correlated strongly with remote sensing-identified iron-rich areas, including known mines, and highlighted unexplored anomalies in the northwestern and central regions. Geochemically, we identified economic ore-grade ironstones distinct (FeOt = 26–46 wt%, MnO < 0.11 wt%, P₂O₅ 0.45–0.84 wt%) from non-economic Mn-rich carbonate lenses (MnO 4.85–7.76 wt%) and barren siliceous rocks. The integration of spectral and magnetic datasets confirmed the spatial coherence of mineralization signals, along with the detection of new high-potential zones for iron mineralization, demonstrating the synergistic utility of these datasets in defining exploration targets. The proposed methods were highly effective in mapping iron oxide deposits within the four major zones: Nasser, Gabal-Ghurabi, El-Gadidah, and El-Harrah.

Item ID:	92551
Item Type:	Article (Research - C1)
ISSN:	2045-2322
Copyright Information:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
Date Deposited:	08 Sep 2026 02:25
FoR Codes:	37 EARTH SCIENCES > 3705 Geology > 370508 Resource geoscience @ 40% 37 EARTH SCIENCES > 3706 Geophysics > 370601 Applied geophysics @ 30% 37 EARTH SCIENCES > 3704 Geoinformatics > 370402 Earth and space science informatics @ 30%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280107 Expanding knowledge in the earth sciences @ 100%
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