B. Shokouh Saljoughi; A. Hezarkhani; E. Farahbakhsh
Abstract
The study area, located in the southern section of the Central Iranian volcano–sedimentary complex, contains a large number of mineral deposits and occurrences which is currently facing a shortage of resources. Therefore, the prospecting potential areas in the deeper and peripheral spaces has become ...
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The study area, located in the southern section of the Central Iranian volcano–sedimentary complex, contains a large number of mineral deposits and occurrences which is currently facing a shortage of resources. Therefore, the prospecting potential areas in the deeper and peripheral spaces has become a high priority in this region. Different direct and indirect methods try to predict promising areas for future explorations, most of which are very time-consuming and costly. The main goal of mineral prospecting is applying a transparent and robust approach for identifying high potential areas to be explored further in the future. This work presents the procedure taken to create two different Cu-mineralization prospectivity maps. The first map is created using a knowledge-driven fuzzy technique and the second one by a data-driven Artificial Neural Network (ANN) approach. In this study aim is to investigate the results of applying the ANN technique and to compare them with the outputs of applying the fuzzy logic method. The geo-datasets employed for creating evidential maps of porphyry Cu mineralization include the solid geology map, alteration map, faults, dykes, airborne total magnetic intensity, airborne gamma-ray spectrometry data (U, Th, K and total count), and known Cu occurrences. Based on this study, the ANN technique is a better predictor of Cu mineralization compared to the fuzzy logic method. The ANN technique, due to capabilities such as classification, pattern matching, optimization, and prediction, is useful in identifying the anomalies associated with the Cu mineralization.
B. A. Mert
Abstract
This paper presents the procedures used for determining and defining the tonnage and grade of the coalfields of Kangal basin from the developed GIS-aided block model. In this work, firstly, all the lithological logs of drill holes and chemical analysis data of core in the basin were analyzed with the ...
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This paper presents the procedures used for determining and defining the tonnage and grade of the coalfields of Kangal basin from the developed GIS-aided block model. In this work, firstly, all the lithological logs of drill holes and chemical analysis data of core in the basin were analyzed with the help of geostatistics, and then the digital raster maps of each one of the attributes such as the thickness, calorific value (LCV), ash content (AC%), moisture content (MC%), and surface maps of lignite seams were mapped in GIS environment. In the second stage, quantities of the overburden and resources with different categories were calculated on the basis of field-based quality and volume queries with the help of the digital maps on GIS platform. As a result, it was estimated that the Kalburçayırı field had a tonnage of 116 Mt of lignite with an LCV of 1308 kcal/kg, the Hamal field had a tonnage of 30 Mt of lignite with an LCV of 987 kcal/kg, and the Etyemez field had a tonnage of 48 Mt of lignite with an LCV of 1282 kcal/kg. Also it was estimated that almost 24,278,151 tons of lignite in the Hamal and Etyemez fields had a quality of less than 950 kcal/kg that could be directly fired without the blending process in the power plant. As a consequence, the Hamal and Etyemez fields should go into production as soon as possible and be fired in the power plant after being mixed with the lignite in the Kalburcayırı field so that they can be redounded to economy.