Enhancing Zn-bearing gossans from GeoEye-1 and Landsat 8 OLI data for non-sulphide Zn deposit exploration

Abstract

This study aims to map the non-sulphide Zinc (Zn)-bearing gossans at the Gujer Zn deposit area, Central Iran, using Landsat 8 Operational Land Imager (OLI) and GeoEye-1 satellites. The colour composites, Principal Component Analysis (PCA), and Support Vector Machine (SVM) were adopted for image analysis. Zn-bearing gossans contain Fe-oxyhydroxide minerals displaying spectral characteristics in visible and infrared (IR) wavelengths. The application of colour composites using GeoEye-1 images resulted in the delineation of gossans (real target) and ferruginous sandstones (false targets) having the same colour tone in the study area. IR spectroscopy of ore samples showed that hemimorphite exhibits low absorption in shortwave infrared (SWIR) wavelengths. Consequently, the Crosta-PC analysis was conducted using bands 4, 5, SWIR-1, and SWIR-2 of Landsat OLI to enhance only ore gossans. Five target zones were specified using the Crosta technique. The SVM method was performed to increase the accuracy of image analysis using the Radial Basis Function (RBF) kernel. The SVM-RBF method accomplished enhancing ore gossans by defining a new target zone. According to the results, the application of the Crosta technique using bands 4, 5, SWIR-1, and SWIR-2 of Landsat OLI can specify ore gossans and eliminate the interfering effect of ferruginous sandstones in similar geological settings. The SVM-RBF can improve the results of image processing using PC entry of Landsat OLI bands. GeoEye-1 images are useful for the initial assessment of geological units in the region and for delineating the accurate boundary of ore gossans derived from Landsat 8 OLI data.

Keywords

• Non-sulphide Zn deposit
• Gossan
• Landsat 8 OLI
• GeoEye-1
• Principal component analysis (PCA)
• Support vector machine (SVM)