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

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 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 vestigations and confirmed by previous geological maps. Furthermore, comprehensive structural analysis (lin-eaments 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 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 geochemical soil surveys.

Automated summary

In this study, satellite data and image processing techniques were used to effectively identify albitized granite (ABG) in the Umm Naggat area of Central Eastern Desert, Egypt. By integrating different datasets and applying the support vector machine algorithm, high-quality lithological interpretation maps were generated. New occurrences of ABG were discovered and the location of known mineralized zones was confirmed. The spatial correlation between ABG, structural density zones, and hydrothermal alteration suggests that rare-metal mineralization is mainly structurally controlled.