Synergizing remote sensing, support vector machine, and aeromagnetic data for precise lithological and mineral potential mapping: a case study from Egypt

Precise lithological mapping is not only a strong key for exploring new mineralized areas but also could help in achieving the optimal exploitation of mineralized regions. In the present case, cause lithological mapping may provide advice not only to the distribution of possible placer deposit occurrences within the study region but also to small-scale blocks of gold-bearing rocks that are not specifically described in prior geological maps. Therefore, we effectively mapped the exposed rock units in the Hamash region of the Eastern Desert of Egypt by using Support Vector Machine (SVM) to Sentinel 2 data through executing machine learning algorithms (MLAs). This goal involves extensive fieldwork, petrographical examinations, and image processing remote sensing techniques to accurately reveal the exposed lithologies distribution besides the potential relic areas. Moreover, the results disclosed several occurrences of the gold-bearing rocks (e.g. alkali feldspar granites) within the study area besides highlighting the distribution of placer deposits that are mainly concentrated around the old mining workings. The lithological overall allocation accuracy (93.8%) was assessed through a confusion matrix, ground truth rock samples, and previous geological maps. Additionally, the airborne magnetic data were processed and interpreted to detect the main lineament structures controlling the favorable locations with high potential for gold mineralization and associated minerals. Also, the Center for Exploration Targeting (CET) porphyry map depicts the significant gold mineralization zones, which are regarded as targets for follow-up and ground studies. The current contribution greatly encourages further investigations for brownfields as their results could at least enhance a considered area of economic placer deposits, if not highlighting non-exploited lithological blocks.