Mapping of the Gold Mineralization Associated Alteration Zones by Processing of the ASTER Data, Shanagh Area, Iran

Abstract

Remote sensing is a valuable tool in the regional exploration phase for identifying mineral prospective zones in a costeffective and time-efficient manner. This study focused on identifying alteration zones related to the gold mineralization in the Shanagh area, central Iran. Various satellite image processing methods including color composite, band ratio, selective principal component analysis, least square fitting (LS-Fit), spectral feature fitting (SFF), and matched filtering (MF) were employed on the advanced spaceborne thermal emission and reflection radiometer (ASTER) multispectral image. After processing the satellite images, a supervised classification technique was applied to the entire study area using the spectral angle mapper (SAM) technique. The SAM was found to have a favorable performance in differentiating alteration zones in the Shanagh prospective area, with an overall accuracy coefficient of 76% and a Kappa value of 65%. The results of this study demonstrated the effectiveness of remote sensing in identifying prospective zones for gold mineralization, in a cost-effective and efficient manner. Resulted information can be used by mining companies to make informed decisions about where to focus exploration efforts, ultimately leading to more successful and profitable mining operations.