m.j. mohammadzadeh

This research aims to simultaneously use geochemical modeling and geological parameters for gold grade estimation to identify promising zones of epithermal gold mineralization in the Zailik region, northwest of Iran. For this purpose, the employed geological evidence includes lithology and alterations like silicification, iron oxides, phyllic, and propylitic. For  geochemical modeling two methods were utulized: 1) artificial neural network (ANN),  2) integrating ANN with the Firefly algorithm. Geological evidence after quantification, along with the estimated amounts of gold in artificial intelligence methods, was entered into the hierarchical system in Expert Choice software for weighting. In this method, the weighting and determination of the degree of relative importance of geological parameters were attempted after consulting geological and exploration experts. Subsequently, artificial intelligence methods were also compared with each other using quantitative criteria such as the coefficient of determination and the root mean square error function. The results showed that the combined method of artificial neural networks with the Firefly algorithm provides better results due to the higher coefficient of determination (R2=0.643) and lower error function (RMSE=0.754). Therefore, it has a higher degree of importance to identify promising areas for mineralization. Finally, all the above parameters were combined with each other in the Arc GIS software using the fuzzy overlay method, and the optimal exploration targets were detected in the north and northeast of the region, enabling to continue the exploration targets along the root of gold mineralization in the neighboring areas according to the introduced model.

In this paper a hybrid approach of Sequential Gaussian Simulation (SGSIM) and Concentration-Volume (C-V) fractal method was applied to boreholes data for classification of major geochemical parameters and associated features with alteration zones of the Haftcheshmeh Cu deposit in NW of Iran. Thereupon, Cu parameters were detected with higher efficiency and lower uncertainty. The purpose is also extended to delineate the alteration zones pertinent to Cu mineralization. Firstly, the most straightforward simulation (SGSIM) was utilized for projecting different lithogeochemical parameters of Cu. Then the C-V fractal model was used to discriminate these parameters by thresholds, obtained from the C-V Log-Log plot. The Fractal based resulting maps of 10 realizations and their E-type indicate their association with potassic alteration that has imposed on porphyry granodiorite. Moreover, these maps illustrate that the boreholes (1, 9, 23, and 31) at about longitudes of 643400 to 643800 are more promising than others. This fact explicitly had been correlated with reality of the studied area and denoted in its primary surface map. The results based on SGSIM, C-V confirmed enhanced mineralization in three-dimensional maps of the Haftcheshmeh deposit as a powerful combined method that can be used to detect the similar ore zones in continuation of the ore roots in adjacent areas.