Mineral exploration and Remote Sensing Data Analysis

BFP/RGP/EBR/22/283

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Mineral exploration and Remote Sensing Data Analysis – the use of satellite data for lithological mapping and mineral exploration in potential areas in the Sultanate of Oman 

Abstract

The research proposal aims to explore specific areas of the Sultanate of Oman that are potential sites of ore deposit with relevant economic interest, according and in collaboration with the Ministry of Energy and Minerals – Directorate of Minerals, using the analysis and interpretation of Remote Sensing Data (hereafter RSD) with the support of field work as ground control. RSD satellite imageries with different spectral resolutions (multispectral and hyperspectral images), available in open access database (e.g., NASA Earthdata, USGS Earthexplorer database), are currently used for geological and mineralogical exploration. The north-eastern sector of Oman (Saih Hatat area) is characterized by the exposition of continental crust and oceanic lithosphere involved in a complex geological structure. This wide exposition of different lithologies allow to observe different spectral reflected information detected by RS sensors. The RSD acquisition is related with the detection of the electromagnetic characteristics of a specific mineral and/or specific lithologies. RSD analysis and interpretation is currently wide used for mineral exploration. Theminerals associations are associated with specific lithologies, controlled by structures, and associated withcharacteristic morphological and spectral features. RSD interpretation with the support of field work and mineralogical/chemical analysis of target ground control points are fundamental for the exploration and evaluation of potential natural resources. The research project involves the Ministry of Energy and Minerals for the evaluation of the area of interest, the field work, the mineralogical data analysis and for the scientific outcomes

ASTER® 14 bands multispectral resolution satellite imagery of Saih Hatat region, Sultanate of Oman.

Supervised classification clusters pixels in a dataset into classes based on user-defined training data from Black and white bands imagery (top right), resolution 15 m, ENVI® sw.