Mafic–ultramafics complexes are crucial for their tectonic implication, upper mantle condition, and for hosting industrial minerals in a region. This study aims to highlight and characterize the mafic–ultramafic rocks of the Logar Ophiolite Complex using the integration of geospatial technology and field data. The spatial distribution of the ophiolitic complex was examined in this study using the mineralogical indices (MI), band ratio (BR), and spectral angle mapper (SAM) methods within the framework of geospatial technology using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Additionally, several samples were collected from the identified complexes for validation, petrographic, and mineralogical analyses. Combining geospatial technology and conventional approaches, e.g., field sampling and geological data analysis yields efficient discrimination of mafic–ultramafic rocks with their associated hydrothermal altered minerals. The serpentinization and carbonate processes are predominantly seen along the eastern side of the active fault zone following the detection of ophiolites. Detailed mapping of the ophiolitic complex and associated rocks was achieved using refined mafic index (MI), band ratio 12/14 and 4/8 for rocks and SAM for highlighting the mafic–ultramafic altered minerals, and petrographic analysis of the collected samples. The field works verified the results of the ASTER data. The findings of this study can significantly contribute to detailed tectonic and geologic studies of the detected ophiolites in terms of their emplacement mechanism and ages.

Ophiolites are remnants of oceanic crust that have been thrust onto continental crust due to tectonic processes. They are composed of mostly mafic and ultramafic rocks, which are genetically associated with gold, silver, platinum group element (PGE), chrome, manganese, titanium, cobalt, copper, and nickel deposits. The main objective of this research was to identify the spatial distribution of Mesozoic ophiolitic complexes within the Central Afghan Block in Middle Afghanistan using optical remote sensing data and spectral analyses. Distinct algorithms, such as false color composite (FCC), proposed band ratios (PBR), principal component analysis (PCA), and spectral angle mapper (SAM), were used to map the targeted ophiolitic complexes. New band ratios were proposed in this study based on the spectral properties of mafic-ultramafic minerals and rocks, which showed high efficiency. Based on the results, four different ophiolitic complexes were delineated within this study area. These complexes are consistent with previous studies. The accuracy assessment of this study showed an overall accuracy of 72.2%. The findings of this study can significantly contribute to further studies on the emplacement mechanism and paleo-Tethys history of Middle Afghanistan. Also, the spatial distribution of the ophiolitic complexes identified in this study can be used to constrain models of the tectonic evolution of the Central Afghan Block. Additionally, the identification of new band ratios for mapping ophiolitic complexes can be used in future studies of other ophiolite-bearing regions.

Granites are widely distributed, phaneritic igneous rocks renowned for their high compressive strength and durability, making them a premier choice for dimension stone applications. This study aims to detect and map granitic complexes using geospatial data and spectral algorithms within the arid and semi-arid environment of northwestern Parwan Province, northeastern Afghanistan. Also, to establish an effective and optimized supervised classification approach specifically tailored for identifying granitic complexes in similar terrains. This study utilizes FCC imagery to highlight lithology and employs various machine learning algorithms, including ML, MD, SVM, and SAM, for mapping granitic complexes within the study area. Training and test data were collected from field observations, Google Earth imagery, and geological maps. Our analysis identified two primary granitic complexes within the study area, measuring approximately 19 km × 13 km² and 7 km × 3 km², respectively. Ground truth data validation yielded an accuracy of 75%, indicating a positive correlation between the predicted and observed distributions. This enhanced understanding of granite distribution can serve as a valuable guide for future exploration endeavors targeting metallic and non-metallic resources, including aluminum, iron, manganese, rare earth elements, tungsten, etc.