Investigating the alteration of igneous rocks in relation to bentonite mineral mapping using remote sensing data in Khor and Biabanak, central Iran

The alteration of igneous rocks indicates the formation and identification of valuable mineral deposits, such as bentonite, which is in increasing demand across various industries, including oil and steel. Remote sensing methods, leveraging the spectral characteristics of minerals, can detect hydrothermal alteration zones associated with various ore deposits, including bentonite, thereby reducing the cost of time and fieldwork. This study investigates the alteration of igneous rocks in the Khor and Biabanak region, located in the eastern part of Isfahan province, Iran, northeast of Nain city. Using ETM+ satellite data and field observations, we aimed to identify new potential bentonite resources. The ETM+ satellite data was processed using preprocessing stages including Layer Stacking, Subseting, Radiometric Corrections, Geometric Corrections, SLC-off Gap Filling, Noise Reduction (Destriping), Cloud and Water Masking, and Band Scaling/Normalization). This was followed by applying False Color Composite (FCC), Principal Component Analysis (PCA), the Enhanced Crosta technique, and the Least Squares Fitting (LS-Fit) method, which enabled us to identify promising mineral zones. Field surveys and X-ray Diffraction (XRD) analysis of samples confirmed significant concentrations of smectite (montmorillonite), indicative of substantial bentonite deposits. These findings suggest that integrating remote sensing techniques with field validation successfully identified areas with significant bentonite potential for further exploration. The successful application of these methods, particularly in a region with limited prior bentonite-focused remote sensing studies, highlights their utility in similar geological settings, offering a valuable approach for mineral exploration.

کلیدواژه ها:

crosta method ، igneous rocks alteration ، least squares fitting method ، Mineral exploration ، remote sensing