Economic Geology
Meysam Nikfarjam; Ardeshir Hezarkhani
Abstract
In this research, we have used different integration methods for creating the geochemical evidential map that is one of the most important layers in mineral potential mapping. The Study area (Varzaghan 1:100,000 sheet) is located in East Azarbaijan province and Ahar-Arasbaran metallogenic zone. This ...
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In this research, we have used different integration methods for creating the geochemical evidential map that is one of the most important layers in mineral potential mapping. The Study area (Varzaghan 1:100,000 sheet) is located in East Azarbaijan province and Ahar-Arasbaran metallogenic zone. This region, because of its geological situation and presence of several porphyry copper deposits like Sungun porphyry-skarn deposit, is considered as an important metallogenic province in the northwest of Iran. In this study, we have used 1067 stream sediment samples as primary data that picked up by Geological Survey & Mineral Explorations organization of Iran. By selecting indicator elements of porphyry copper deposit, like Cu, Mo, Au, Ag, Pb, Zn, Au and As, the evidential map of each element have generated by the continuous fuzzy method. In the next step, by using Union Score (US) method, fuzzy OR operation, and geometric average, the individual geochemical maps have integrated. Finally, Prediction-Area plots have drawn to validate the evidential maps. This plot showing that geochemical evidential map that produced by US method, can predict 76 percent of known mineral occurrences and it can consider as a proper method for creating the geochemical evidential map for porphyry copper deposits.
Exploration and Mining
Maliheh Abbaszadeh; Ardeshir Hezarkhani; Saeed Soltani-Mohammadi
Abstract
In recent years, economic geology studies have become very popular method in mineral exploration studies. Modeling fluid inclusion data is one of the common studies in economic geology. In this research artificial neural networks method, as one of the machine learning algorithms, is used for three-dimensional ...
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In recent years, economic geology studies have become very popular method in mineral exploration studies. Modeling fluid inclusion data is one of the common studies in economic geology. In this research artificial neural networks method, as one of the machine learning algorithms, is used for three-dimensional modeling and application of the results of fluid inclusion analysis in Sungun porphyry copper deposit. For this purpose, fluid inclusion data is used for directly separation of related alteration zones with mineralization (Potassic, Phyllic and Potassic- Phyllic). Due to the relation that exists between alteration zones and mineralization areas, based on 173 fluid inclusion data the separation of alteration zones is modeled by artificial neural networks method in Sungun porphyry copper deposit. According to the validation studies, it can be concluded that precision of this model is appropriate (83%) and trained model could be used for separation of alteration zones in Sungun porphyry copper deposit.
Economic Geology
Meysam Nikfarjam; Ardeshir Hezarkhani; Kaveh Pazand
Abstract
The study area (Varzaghan 1:100,000 Sheet) is located in eastern Azarbaijan province and Ahar-Arasbaran metallogenic zone. The magmatism in this area is happened widespreadly that leads to several important deposits like Sungun world class deposit. According to the importance of this region from Cu-Mo ...
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The study area (Varzaghan 1:100,000 Sheet) is located in eastern Azarbaijan province and Ahar-Arasbaran metallogenic zone. The magmatism in this area is happened widespreadly that leads to several important deposits like Sungun world class deposit. According to the importance of this region from Cu-Mo porphyry deposits, we used structural methods like multifractal method to determinate Cu and Mo geochemical anomalies as indicator minerals of Cu-Mo porphyry deposits. In this paper two multifractal method like C-A (Concentration-Area) and N-S (Number-Size) method were used in order to separation geochemical anomalies from background and select the optimum method. In this way, first of all, we draw catchment basins for every stream sedimentary samples by using DEM and PFS (Priority-First-Search) algorithm. After drawing the catchment basins for each sample, concentrations of samples were assigned to their upstreams. In C-A method with plotting cumulative area of each sample catchment basin versus concentration content, 4 number society of each Cu and Mo elements identified. Also in N-S method, cumulative frequency of concentrations versus concentrations plotted. In this method in comparison to C-A method, 5 number society of Mo and 4 number society detected. In both performed methods can see good conformity of anomalous locations with well known deposits like Sungun worldclass deposit but N-S method has better efficiency by using logratio matrix. Also we can see the effect of lithology in anomalous places. Finally some of theses places addition to indications detected. So they require detailed exploration in future.
P. Tahmasbi; A. Hezarkhani
Abstract
In the present research, comparative evaluation of various learning algorithms in neural network modeling was performed for ore grade estimation in Sonjail porphyry copper deposit. The main goal of the following investigation would be optimizing the network architecture and to present an architectural ...
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In the present research, comparative evaluation of various learning algorithms in neural network modeling was performed for ore grade estimation in Sonjail porphyry copper deposit. The main goal of the following investigation would be optimizing the network architecture and to present an architectural optimization trend to better performing the copper grade estimation within the region. Therefore, 12 algorithms were investigated back propagation learning algorithms. Based on this research it is merged that by applying the LM and BFG algorithms, there would be the best performance. The reasons why the other algorithms have the same performance would be presented within the paper as well. The input parameters are coordinates and the outputs are the copper grades for each specified point. To obtain the optimal structure, a network with different layers has been applied, which it has acquired 12 neurons within one layer. To investigate the data normal shapes, various normal shape has been acquired in the [0 1], which could merged the best results. Finally to get the best network optimizations several transfer functions have been applied, and the sigmoid transfer function illustrated least error when the transfer function is selected. Considering the optimal conditions, the R2 value has merged 0.946 for network which could be the result showing that the optimal network architecture causes estimation improvement.
M. Abbaszadeh; Ardeshir Hezarkhani
Abstract
Rabor area is located in 160 km south of Kerman city and 40 km east of Baft. There is some evidence illustrating some porphyry copper type mineralization, co-operated with tens of within Urumieh-Dokhtar volcanic belt stocks. Identification of the high potential localities and mapping the porphyry copper ...
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Rabor area is located in 160 km south of Kerman city and 40 km east of Baft. There is some evidence illustrating some porphyry copper type mineralization, co-operated with tens of within Urumieh-Dokhtar volcanic belt stocks. Identification of the high potential localities and mapping the porphyry copper mineralization within these sites look very necessary. To aim for this goal, we aimed to identify the probable mineralization zones related porphyry copper mineralization alteration haloes in Rabor. In this research, by using the satellite image processing of ASTER sensor, applying the methods such as band ratioing, principal component analysis (PCA) and selective principal component analysis (Crosta) as well as the direct data from the Baft geological map (1:100000), available metallogenical theories and porphyry copper mineralization models, prepare images based on available clay mineral concentration maps from the region could provide evidences for an existence of a porphyry copper mineralization. Band ratioing was applied to discriminate the altered areas from the non-altered ones and also area lithology, porphyry copper deposit boundaries by identification of kaolinite, alunite and illite as indicator minerals within the studied area. Selective principal component analysis was also applied to produce the clay mineral concentration indicator maps to potential mining area recognition. Ore index cross matching called Pey Negin based recognition presumed area, demonstrates the selective principal component analysis method accuracy and its efficiency by using the satellite ASTER data from the altered area.