Lithological mapping using satellite images, particularly the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data help in effectively defining the best initial targets for regional exploration. ASTER data allow for the discrimination of rock units in the broader region. This research work is focused on the use of remote sensing techniques for geological mapping using ASTER satellite image and generating a geological map of Sangan region. The study area is located in southeast of Khorasan-e-Razavi province and at the eastern edge of the Khaf-Kashmar-Bardskan Volcano-Plutonic Metallogenic Belt in northeast Iran. Band ratio (BR), spectral angle mapper (SAM) and support vector machines (SVMs) methods were used for classifying the main lithologic units in Sangan region. The results of BR, SAM and SVM techniques were quantitatively compared with geological boundaries mapped in the field showing an accuracy of nearly 79 %. SVMs method, in comparison to conventional methods of classification, was known to provide superior results. As the final result of this research, integration of remote sensing and field investigations led to generating high accuracy geological map in the Sangan region. Application of the methods has invaluable implications for geological mapping and mineral exploration in inaccessible regions.
Estimation of ore grade is a time and cost consuming process that requires laboratory-based and exploratory information to present the shape and the ore grade distribution of ore deposit in three dimensional space. The block size is one of the most important parameters which impacts the quality of grade estimates in a resource model. This study aims at spatial modeling of iron ore deposit using geostatistical estimation methods such as Ordinary Kriging based on error estimation, selection of the appropriate size for mining blocks using Vlse Kriterijumsk Optimizacija Kompromisno Resenje method, and performing a three-dimensional block modeling along a grade estimation study for the resource estimation in the C-North iron ore deposit, NE Iran. The variogram that was used in OK estimation was cross validated. Cross validation results showed that compared with the local model, OK with the global model was the most appropriate model for the ore body. Detailed distribution maps of total iron contents in the C-North ore deposit showed a close relationship between structural features and higher iron contents, relative to other areas of the ore deposit. Structural features included the major faults and fault zones along the axial plane. These structures are interpreted to have played a significant role in (re) mobilisation and concentration of the metals, in agreement with observations made elsewhere in the Sangan iron ore complex. Based on the estimation results, 83 million tons of resource was estimated at an average grade of 41.86 % Fe using OK method. The C-North ore deposit has been classified based on the relative estimation error variance and the Australasian Code for Reporting of Mineral Resources and Ore Reserves. It is hoped that this example, taken from very different application fields, will encourage practitioners in applying an OK method with variety of ore deposits.
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