Integration of Remotely Sensed Data and Field Studies for Lithological and Structural Mapping of Orogenic Gold Occurrences in the Barramiya–Mueilha Sector, Central Eastern Desert, Egypt

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Throughout human life history, minerals in general and specifically gold have had a voluminous influence. Currently, gold demand remains strong. With the advent of new scientific technologies, tools, and datasets, exploration programs can maximize their efficiency in investigating various types of gold mineralizations and identifying associated co-products and by-products in both greenfields and brownfields. Therefore, this study integrates diverse remote sensing datasets (including optical sensors—multispectral and hyperspectral, active radar data such as Sentinel-1, and digital elevation models), machine learning algorithms (ANN, MLC, SVM, RF, and XGB), geophysical measurements (magnetic and radiometric), fieldwork, petrographic investigations (using a transmitted polarized light microscope), XRD, SEM-EDX, and geochemical analyses to provide a reliable lithological and hydrothermal alteration mapping of the studied terrain. Additionally, structural mapping, which is crucial for mineral deposits, was performed using lineament extraction, textural analysis, Circular Feature Transform (CFT), and Amplitude Contrast Transform (ACT). This revealed the main structural features within the study area and highlighted three phases of deformation (D1, D2, and D3). Regarding hydrothermal alteration zones, the study concluded that the area is significantly affected by iron- and hydroxyl-bearing alterations. Airborne gamma-ray data were also integrated to identify these alteration zones. Petrographic and XRD analyses of these alteration zones revealed a wide range of alterations, including carbonatization (calcite, dolomite, ankerite, and magnesite), ferrugination (hematite, goethite, and siderite), propylitization (chlorite and epidote), serpentinization (serpentine and talc), argillization (kaolinite and smectite), and phyllitization (muscovite and illite). SEM-EDX investigations revealed the presence of several ores, including scheelite, molybdenite, apatite, marcasite, hematite, magnetite, and gold. Notably, a remarkable consistency was observed among the results obtained from the integrated techniques across various levels. Thus, we strongly recommend this applied methodology for further implementation in arid terrains, as it provides significant assistance for exploration programs.

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Lithological mapping, Remote sensing, Machine Learning Algorithms, Central Eastern Desert, Structural analysis
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