Exploration and Mining
Amir Salimi; Samaneh Barak; Mahyar Yousefi
Abstract
An accurate threshold value makes a precise geochemical separation into anomaly and background areas. The threshold assignment using structural methods are preferred to non-structural methods. In this research, the U-spatial statistics, a structural based method, was used to study soil type geochemical ...
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An accurate threshold value makes a precise geochemical separation into anomaly and background areas. The threshold assignment using structural methods are preferred to non-structural methods. In this research, the U-spatial statistics, a structural based method, was used to study soil type geochemical data of the Neysian region. The optimal U-values obtained by this method for each sample were successfully utilized to separate the abnormal and background samples, accurately. In addition, based on the optimal distance of each sample, the abnormal samples identified in the previous step were classified in terms of geochemical intensity into strong, medium and weak samples. The goodness of U-spatial statistics performance in identifying abnormal areas were validated using drilled boreholes in the area. The U-spatial statistics not only succeeded in correctly identifying anomalous samples, but it also correctly identified some samples as the background whiles they had been recognized as anomaly by a non-structural method. All results obtained were validated by the several drilled boreholes.
Exploration and Mining
Saeid Ghasemzadeh; Abbas Maghsoudi; Mahyar Yousefi
Abstract
The Baft district in Kerman province is located in the southeastern segment of the Urumieh-Dokhtar magmatic arc. This arc is characterized by thick accumulations of Cenozoic plutonic and volcanic rocks and provide favorable conditions to the development of hydrothermal systems and mineral deposition, ...
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The Baft district in Kerman province is located in the southeastern segment of the Urumieh-Dokhtar magmatic arc. This arc is characterized by thick accumulations of Cenozoic plutonic and volcanic rocks and provide favorable conditions to the development of hydrothermal systems and mineral deposition, in particular porphyry copper mineralization. For mineral prospectivity mapping (MPM) to delineate prospective areas some individual maps of evidence including distance to intrusive contacts, fault density, distance to hydrothermal alterations and multi-element geochemical signature were generated. Spatial evidence values in each map were transformed using a logistic function of unbounded values into the [0,1] range. Thus continuous maps of fuzzy evidence layers were integrated using geometric average function. To evaluate results of final potential map a data-driven prediction-area was used. The results showed that for the geometric average prospectivity model, 87% of the known mineral occurrences are predicted in 13% of the study area. Hence, this method can be utilized for mineral prospectivity mapping to delineate target areas for further exploration of a certain deposit-type.
Mahyar Yousefi; R. Gholami; A. Kamkarr-Ruhani; A. Moradzade
Abstract
In the systematic exploration plan for prospecting the mineral deposit, we can design an exploration algorithm using the modeling of known mineral occurrences. Such an algorithm is a key to recognize the area where is high probability of mineralization, reduce the risk of exploration and increases the ...
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In the systematic exploration plan for prospecting the mineral deposit, we can design an exploration algorithm using the modeling of known mineral occurrences. Such an algorithm is a key to recognize the area where is high probability of mineralization, reduce the risk of exploration and increases the probability of exploration success. In this paper, we introduce an algorithm for optimizing mineral potential model and target generation in the exploration operation with focus on the gold exploration. In this way, after descriptive and conceptual modeling of gold deposit, all of the characteristics that can be used as an exploration criterion have been identified and assembled as a target model. Then, various data layers have been used to generate significant evidential maps. Then all of the evidential maps should be combined to generate mineral potential model (map) of the mineralization type sought. Recent map shows the probability location of gold mineralization as target area. Finally an algorithm has been introduced in which all of the exploration stage and methods have been identified base on priority.
