فهرست مطالب ahmadreza mokhtari

Distinguishing between anomalous and background communities and determining the threshold of geochemical communities are usually done by structural and non-structural methods. The U spatial statistical method is one of the structural methods for geochemical anomaly separation, which is known for its high capabilities in determining geochemical anomalous areas in mineral exploration. In this investigation, U statistics, S-A, and C-A fractal methods have been used for modeling Pb-contaminated areas.

Methodology and Approaches:

The geochemical distribution maps have fractal dimensions. Fractal dimensions of the anomaly and the background will be different from each other, which are used to separate the anomaly from the background. The S-A fractal method has been performed on the geochemical data in the frequency domain. The geochemical log-log diagram of the power spectrum values and cumulative areas is delineated, and straight lines are fitted on the diagram to show the trends of fractal populations. The U-spatial statistics method is a kind of moving averaging method that changes the dimensions of the window in which the averaging takes place at any specific point. Therefore, for each particular point, several U statistic values are calculated using the surrounding points, thus the spatial relationship of the samples is completely considered. 

In this study, the Pb contaminations resulting from the distribution of this element in the surrounding areas of the Irankuh mine have been investigated. The C-A, S-A fractal, and U statistics methods were performed for pollution anomaly mapping. Four geochemical communities with different dimensions were obtained using C-A fractal method. The higher fractal dimensions are associated with Pb contamination of mining activities or human impacts and are known as anomalous communities. The communities with lower fractal dimensions are considered as background. The results of the fractal method show that the source of contamination has originated from the mine area and dispersed in the surrounding areas. The S-A fractal method separated the frequency signals in 4 geochemical communities. The population 2 including medium to low-frequency signals properly determined the contamination locations. The geochemical community with high concentrations correctly determined the contamination districts around the mining activities and tailings dam using U modeling. The middle community showed the agricultural and residential lands have a smaller contamination halo than other methods.

The classification of mineralized areas into different groups based on mineral grade and prospectivity is a practical problem in the area of optimal risk, time, and cost management of exploration projects. The purpose of this paper was to present a new approach for optimizing the grade classification model of an orebody. That is to say, through hybridizing machine learning with a metaheuristic algorithm called Harmony Search (HS), a proper model for the spatial distribution of the grade classes was obtained, while improving the computational cost of the traditional classification methods. The HS is an algorithm inspired by the simulation of the process where a composer tries to harmonize a piece of music. By interpolating the dataset of Cu and Mo concentrations in the surface rock samples taken from Kooh-Panj deposit district, the grid data of the two elements were extracted. To estimate the true number of groups in the dataset, five popular indices were used in this regard, which in turn determined two classes as the optimal number of groups. Harmony Search Learning (HSL) was used to classify the grid dataset of Cu and Mo. The comparison of the results of the proposed approach with the conventional k-means clustering suggested that the use of HSL method significantly reduced the cost function of the problem (up to 13%). The adaptation of the mineralization class derived from HSL and k-means clustering to borehole locations proved that the results of the HSL were more successful in the accurate estimation of the economic mineralization class identified by the exploratory excavations. In this respect, the HSL technique could significantly improve the k-means performance by 25%. Furthermore, the results of the HSL were more consistent with the lithological units and alteration zones involved in the ore-forming processes. The use of the HS-based learning rectified the disadvantages arising from the typical clustering methods regarding the entrapment in local optimums. It also led to the extraction of weak mineralization signals, numerically laid in boundary conditions. This approach can be extended to more than two geochemical variables and can be a valuable tool for the classification of the mineralized areas to design and optimally manage the mineral exploration projects.

Mining and its various stages is one of the important factors in spreading pollution in natural environments. Contaminations may affect soil and water resources and actually enter the food chain due to absorption by plants. Among mineral elements, lead is more important because of its toxic effects. In order to investigate distribution of lead in downstream soils of Kushk Lead and Zinc Mine and identify anthropogenic and geogenic natures of pollution spread, a classified random systematic sampling was carried out. Sampling interval was 500 to1500 meters and soils were sampled in 0-5 and 5-30 centimeters depths. A control sample area with similar characteristics was considered in the vicinity of the region. The samples were analyzed by ICP-MASS method and the lead content of the samples was determined. Using Enrichment Factor and Geoaccumulation Index, the extension and severity of pollution are determined and related maps are prepared. Conclusions show that average amounts of lead in topsoil is about 4 times of subsoil and one kilometer far from mine, lead content of soil is more than 290 PPM that is more than permissible amounts. Moreover, the difference between lead content in topsoil and subsoil shows that the contamination in these areas is anthropogenic. Contamination reduces by going further from mine and the highest amount of emission of lead is around the main watercourse.

The Choghart iron ore is one of Iron deposits located in central Iran in the volcanic-sedimentary basin of Bafgh. Like other natural occurrences of the earth, it has grade fluctuations throughout the deposit. Understating and awareness of these fluctuations is vital in order to increase the recovery and concentration of iron. Choghart mineral processing factory has been designed and optimized based on factory input feed tolerant to some degree of variations in grade. The aim of this paper is predicting phosphorous concentration after mineral processing based on the analysis of raw samples applying mathematical models. As a result, non-linear regression model including quadratic polynomial model showed high accuracy and by employing stepwise regression the number of predictor variables were kept as low as possible. Also, discriminant analysis has separated high and low phosphorus samples having 88% accuracy. Also, the artificial neural network could predict phosphorus grade effectively and accurately. The natural occurring earth materials including mineral resources usually include fluctuations in parameters like. Knowing and awareness of these fluctuations is vital in order to apply the necessary measures to increase the recovery. Mineral processing factories are optimized and designed based on the feed which its fluctuations should be inside an allowed limit. Most studies have been done on grade changes identifications, which mostly focused on the errors caused by different, staged of sampling and laboratory, block variance and grade dispersion. The aim of the present study is considering plant feed changes to predict the output phosphorous grade of the Choghart mineral processing factory at the Iran Central Iron Ore Company (ICIOC), Bafgh. Different statistical and artificial intelligent techniques have been employed for prediction of concentrated or based on analysis of raw materials. In the present study 110 samples (including 7 duplicates) were collected from boreholes. Samples were sent to Zarazma laboratory at Tehran for analysis and the remaining samples were returned for test with Davis tube at ICIOC. The concentrated ore after magnet separator were send to chemical laboratory in ICIOC to determine Fe and P content of samples. Different techniques including mathematical models, discriminant analysis, regression and artificial neural network have been employed in the present work to predict the P content in concentrate after Davis tube from elemental contents in the original samples. A mathematical model is applied in order to better and more effective control the grade fluctuations. In this paper methods including regression, discriminant analysis and artificial neural network were used in order to predict the target variable. Although the non-linear regression containing quadratic polynomial has a high accuracy. The step wise regression has a better performance because of the limitation number of predictor variables. Discriminant analysis and neural network have divided high and low-p content samples with proper accuracy.

Summary In the present study the sample catchment basin approach has been used in addition to multivariate statistical analysis over geochemical dataset collected in Kervian area in Iran. Sample catchment basin has reasonably detected the favorable zones in the present study and the proven Au mineralization has validated the results.
Introduction Stream sediment data application method has been widely used and accepted in exploration of different deposit types in mineral exploration industry and has shown its efficiency in this context. The sample catchment basin approach is a technique for processing of stream sediment data that is employed for removal of lithological impact from existing variation in chemical content of the samples.
Methodology and Approaches The target area covers 617 km2 in which 299 stream sediment samples were collected for chemical analysis. In addition, digitized 1:100000 geological and 1:50000 topographical maps were used for creation of sample catchment basins considering the sampling point positions. In order to remove the impact of background contribution to geochemistry of stream sediment by this technique the following procedure has been taken into account: 1- estimation of uni-element background concentration of element of interest for every lithology and then estimation of uni-element background value for every sample catchment basin 2- removal of the background concentration from measured concentration of element for each sample 3- dilution correction of residual values by taking the catchment area size into account 4- analysis of dilution correction values and delineating the anomalous basins.
Results and Conclusions The sample catchment basin method was employed and data processing was completed as mentioned above which resulted in delineation of anomalous Au values extended south-westerly. The results are compatible with known gold occurrences located in this part of the map. There are weak to moderate anomalous basins positioned south east and east of the known mineralization covered with the same sedimentary units as mineralization area that could be assumed promising. However, sample catchment basin has successfully highlighted the anomalous basins which were not detected through analysis of raw dataset. Employing this method has led to introduction of more areas for further prospecting, decreasing the chance of false negative anomaly. Comparison between processed dataset with the sample catchment basin approach and raw data set has demonstrated the superiority of the sample catchment basin approach.

The Alishar Cu-Au index is located in the Markazi province within the Uromieh-Dokhtar magmatic belt. The mineralized zone is hosted by andesitic to andesite basalt lava units، andesitic-andesite basalt tuff and rhyolitic to dacitic tuff (Eocene-Oligocene). The area is affected later by young granodiorite and monzodiorite plutonic units (Oligo-Momiocene). As a result of hydrothermal alterations، phyllic and propylitic alterations as well as kaolinitization and silicification developed in the area. In addition، Cu -Au mineralization has been introduced into the area، mainly associated with silicic veins. Based on geochemical data the Cu and Au contents of the samples are about 0. 5% and 0. 3 ppm respectively. Also، the Bi content of the studied samples is about 0. 5 ppm. The index comprises oxidation، hypogene and supergene zones and contains chalcopyrite، pyrite، chalcocite، magnetite، hematite، goethite and malachite. Fluid inclusion studies of the samples taken from silicic veins show that the homogenization temperature and salinity vary between 200 to 300 °C and 21. 6% wt NaCl respectively. On the base of obtained data، it can be concluded that the dominant mechanism for mineralization is the mixing of hydrothermal solutions with meteoritc waters. Geological، mineralogical and fluid inclusion investigations have demonstrated that mineralization in this area is correlated with other IOCG deposits in the world.

Alishar index with high potential to Cu-Au mineralization is located in Urumieh-Dokhtar magmatic belt and in Arak province. Geological units of the area consists mainly of andesitic, Trachyandesitic-basaltic andesite lava, andesite to basaltic andesite tuff and rhyolitic to dacitic tuff which is affected by hydrothermal solutions so the igneous rock units display some kinds of alterations such as phyllic, propylitic, argilic and silicic alterations as well as iron oxide (hematite, goethite) and copper mineralization. Plentiful silicified and mineralized veins and veinlets is a favorable environment for gold mineralization. High correlation between Cu and Fe can be explained by the presence of chalcopyrite and correlation between Au, As and Bi are attributed to substitutions in Cu, Bi minerals and arsenopyrite. Multivariate statistical analyses have demonstrated three stages in mineralization in the area. The first stage shows: Cu, Au, Ag, Bi, Mn, Co and Zn; the second stage includes: Fe, Sb and the last stage comprises as, Cd and Mo enrichment. The evidences comprising host rock composition, ore minerals, alteration types, tectonic regime, geochemical analysis results and high correlation between Fe, Cu and Au and also elevation in concentration of these elements demonstrates that Alishar mineralization is similar to IOCG.

Different techniques have been considered to separate mixed populations so far and one of the most important of them is Sinclair’s suggested technique which is based on the finding conjunction point of combinational graph and then separating the populations. In this study، it has been tried to develop an algorithm to carry out the aforementioned technique in order to separate the populations. Based on the investigation، it is possible to apply tangents in the first separated point، which is obtained from the Sinclair’s method. The proposed algorithm is able to complete the separation of population with high speed and precision. In order to show the efficiency and applicability of the algorithm Cr concentration from an environmental study is used for separation of underlying populations. The results displayed that the populations were precisely separated.

Due to the presence of outliers and existence of skewness in distribution of geochemical data، these types of data sets usually are compositional in type that closed form data system. Closed data system includes a data set that one or more constraints are applied on data. Summation of these data is always fixed and is equal to the one (100%). By calculating the correlation coefficients for open data set and comparing the result with the ones calculated for closed data set، it can be found that there is an increase for correlation values in the closed system that is unrealistic. Characteristics of closed data prevent from applying standard statistical techniques; thus، different methods are expressed to open the system. In this study،additive logratio transformation، centered logratio transformation and isometric logratio transformation method have been used to open geochemical data in Kuh-e Panj area. In order to compare the results of these three transformations to the closed condition، the correlation coefficient between the elements are calculated and cluster analysis is performed over the data. According to the results، data analysis after opening the system delivers outputs closer to the reality، and centered logratio transformation method can be considered as more preferable method than the other methods.