Detecting and mapping different types of iron mineralization in Sangan mining region, NE Iran, using satellite image and airborne geophysical data

Abstract

The Sangan mining region is the largest Fe skarn in western Asia has emplaced into the Khaf-Kashmar-Bardaskan volcano-plutonic belt in the NE Iran. In this region, carbonate rocks of Jurassic skarnified and hosted different epigenetic types of iron minerals, including magnetite, hematite, goethite, and limonite. The combination of remote sensing and airborne geophysical data is a powerful tool for mapping and interpreting iron mineralization in some area with intensely rugged topography or a broad expanse area, where systematic sampling and conventional geological mapping has some limitation and time consuming. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data and airborne geophysical magnetometry data were used for evaluating and mapping different types of iron mineralization in the Sangan mining region. Preprocessing of the datasets involved band ratio (BR), principal component analysis (PCA), spectral angle mapper (SAM), and constrained energy minimization (CEM) of the visible-near infrared and short wave infrared ASTER data were used to map four types of iron minerals (magnetite, hematite, goethite, and limonite). For preparing a lithological mapp of this region, an RGB image produced by combination of BR and PCA, (R:(5+7)/6, G:PC3, B:PC 5). Implementing SAM and CEM technique were useful for mapping and detecting magnetite, hematite, goethite, and limonite. The Reduce To the Pole (RTP) map of the airborne geophysical magnetometry data is a practical tool for iron ore exploration that were used in this region for enhancing high anomalous signature of magnetite after extracting granitoid rocks. The integration of the extracted information from the ASTER image processing algorithms and geophysical magnetometry, mapped iron mineralization and identified new potential of high magnetite mineralization. The results verified by geological map and comprehensive fieldwork. This integration model can generalize to other arid and semi-arid regions with iron potential for both regional and district scales.