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Image semantic segmentation using deep learning algorithms plays a vital role in identifying different rock-forming minerals. In this paper, we employ the U-net model for its architecture that guarantees precise localization and efficient data utilization. We implement this deep learning model across two distinct datasets: (1) the first dataset from the ALEX Streckeisen website, and (2) the second dataset from the Gabal Nikeiba area, South Eastern Desert of Egypt. Our model exhibits excellent performance in both datasets, with an average accuracy of precision at 0.89 and 0.83, recall at 0.80 and 0.78, and F1 score at 0.82 and 0.79, respectively, helping in identifying and detecting rock-forming minerals in thin-section images. The model's most exceptional performance is clearly in eleven different basement rock-forming minerals with precision up to 0.89, recall at 0.80, and F1 score at 0.82 on average. This study is significant as it represents the key to identifying and detecting minerals in the thin sections of rock samples in Egypt and the Arabian–Nubian Shield as a whole. By significantly reducing analysis time and improving accuracy compared to manual methods, it revolutionizes geological research and resource exploration in the region. [ABSTRACT FROM AUTHOR]

The Igla Ahmr region in the Central Eastern Desert (CED) of Egypt comprises mainly syenogranites and alkali feldspar granites, with a few tonalite xenoliths. The mineral potential maps were presented in order to convert the concentrations of total rare earth elements (REEs) and associated elements such as Zr, Nb, Ga, Y, Sc, Ta, Mo, U, and Th into mappable exploration criteria based on the line density, five alteration indices, random forest (RF) machine learning, and the weighted sum model (WSM). According to petrography and geochemical analysis, random forest (RF) gives the best result and represents new locations for rare metal mineralization compared with the WSM. The studied tonalites resemble I-type granites and were crystallized from mantle-derived magmas that were contaminated by crustal materials via assimilation, while the alkali feldspar granites and syenogranites are peraluminous A-type granites. The tonalites are the old phase and are considered a transitional stage from I-type to A-type, whereas the A-type granites have evolved from the I-type ones. Their calculated zircon saturation temperature TZr ranges from 717 °C to 820 °C at pressure < 4 kbar and depth < 14 km in relatively oxidized conditions. The A-type granites have high SiO2 (71.46–77.22 wt.%), high total alkali (up to 9 wt.%), Zr (up to 482 ppm), FeOt/(FeOt + MgO) ratios > 0.86, A/CNK ratios > 1, Al2O3 + CaO < 15 wt.%, and high ΣREEs (230 ppm), but low CaO and MgO and negative Eu anomalies (Eu/Eu* = 0.24–0.43). These chemical features resemble those of post-collisional rare metal A-type granites in the Arabian-Nubian Shield (ANS). The parent magma of these A-type granites was possibly derived from the partial melting of the I-type tonalitic protolith during lithospheric delamination, followed by severe fractional crystallization in the upper crust in the post-collisional setting. Their rare metal-bearing minerals, including zircon, apatite, titanite, and rutile, are of magmatic origin, while allanite, xenotime, parisite, and betafite are hydrothermal in origin. The rare metal mineralization in the Igla Ahmr granites is possibly attributed to: (1) essential components of both parental peraluminous melts and magmatic-emanated fluids that have caused metasomatism, leading to rare metal enrichment in the Igla Ahmr granites during the interaction between rocks and fluids, and (2) structural control of rare metals by the major NW–SE structures (Najd trend) and conjugate N–S and NE–SW faults, which all are channels for hydrothermal fluids that in turn have led to hydrothermal alteration. This explains why rare metal mineralization in granites is affected by hydrothermal alteration, including silicification, phyllic alteration, sericitization, kaolinitization, and chloritization. [ABSTRACT FROM AUTHOR]

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