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.
Hossein Shahi; Abulghasem Kamkar Rouhani
Abstract
The method of weights of evidence is one of the most important data driven methods for mineral potential mapping in GIS. In this method, considering the characteristics of known mineralized locations, we can prospect new mineralized areas. In this research work, the method of weights of evidence has ...
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The method of weights of evidence is one of the most important data driven methods for mineral potential mapping in GIS. In this method, considering the characteristics of known mineralized locations, we can prospect new mineralized areas. In this research work, the method of weights of evidence has been used for hydrothermal gold potential mapping in Torbat-e-Heydarieh area, east of Iran. As a relatively large number of gold mineral occurrences (i.e., exactly 27 known gold mineralized locations) have been recognized in the study area, the use of the weights of evidence method for prospecting new gold mineralized zones in the area may be quite efficient. In this study, a combination of the results of the airborne geophysical, geological, argillic, propillitic and iron oxide alteration, geochemical and structural data based on the method of weights of evidence, has been made to determine probable gold mineralization zones in the form of a posteriori map of the survey area. Consequently, four major zones in this area have been identified as high gold mineralization potential zones, in which many vein and veinlet mineralization forms can be found.
