Application of K-means and PCA approaches to estimation of gold grade in Khooni district(central Iran)

Grade estimation is an important phase of mining projects, and one that is considered a challenge due in part to the structural complexities in mineral ore deposits.To overcome this challenge, various techniques have been used in the past. This paper introduces an approach for estimating Au ore grades within a mining deposit using k-means and principal component analysis(PCA). The Khooni district was selected as the case study. This region is interesting geologically, in part because it is considered an important gold source. The study area is situated approximately 60km northeast of the Anarak city and 270km from Esfahan. Through PCA, we sought to understand the relationship between the elements of gold,arsenic, and antimony. Then, by clustering, the behavior of these elements was investigated. One of the most famous and efficient clustering methods is k-means, based on minimizing the total Euclidean distance from each class center. Using the combined results and characteristics of the cluster centers, the gold grade was determined with a correlation coefficient of 91%. An estimation equation for gold grade was derived based on four parameters: arsenic and antimony content, and length and width of the sampling points. The results demonstrate that this approach is faster and more accurate than existing methodologies for ore grade estimation.

Geostatistics Studies and Geochemical Modeling Based on Core Data,Sheytoor Iron Deposit,Iran

In general,the purpose of the mineralization modeling is the advancement of a mineral exploration project and ultimately,the extractive design of a deposit,which is one of the most important stages in mining engineering.Mineralization modeling is divided into two general categories,superficial and deep modeling.In surface modeling,the aim is finding abnormal locations in terms of mineralization at the study area,which is commonly used in the early stages of exploration as one of the means for locating exploratory boreholes.After drilling in the study area with the aim of identifying mineralization and reserve estimation it is necessary to obtain deep mineralization position and its geometric features,using statistical and modeling methods.Using mathematical,statistical and modeling methods,we can predict the position of iron mineralization in places where drilling is not done and eventually reach a three-dimensional model of the mineral materials underground.As a case study,the deep information about the boreholes of the sheytoor mining area in Yazd province of Iran was investigated.Iron mineralization was modeled as 2D cumulative model and 3D block model,and the results were presented.Finally the geochemical threshold and the anomalous limit of iron element are calculated by concentration-volume(C-V)fractal method in this deposit.Geochemical threshold and the anomalous limit for Fe in this deposit are 24.7%and 34.3%respectively.

Discrimination geochemical interaction effects on mineralization at the polymetallic Glojeh deposit,NW Iran by interative backward quadratic modeling

The physico-chemical exchanges between hydrothermal fluids and the host rock are usually controlled by elemental interaction effects.A criterion-based backward elimination approach applies the iterative regression analysis and analysis of variance to investigate the geochemical features of the polymetallic Glojeh(Au-Ag-CuPb-Zn)deposit in NW Iran.A statistical definition of the elemental interaction effects(X(i-j)^2,Xi×Xj)could elucidate the relationship between variables and the performance of a full quadratic polynomial model(QPM).The model optimization procedure was carried out by the removal of insignificant predictors(P value 95%CL)based on R^2(pred.)criterion.In order to straighten the convergent trend with R^2 and R^2(adj.),R^2(pred.)gradually increased from 0%to 77.8%by 15-steps optimization.The miniature-scale geochemical changes indicate double ordinal Au(Ag,Pb)and Au(Ag,Zn)interactions within the vein and host rock,in QPM.Results show that the Au(Pb-Zn)commonly presents ordinal effect at the vein and disordinal interaction at the host rock.This ordinal-disordinal interaction revealed that elements Pb and Zn have similar geochemical features during mineralization.In addition,Akima's polynomial contour map confirms the results from Pb-Zn interaction effects by dependency tracing between Au-Pb-Zn at different populations.However,it is noteworthy that Pb and Zn occur together in the second phase of Pb-Zn-Cu±(Ag±Cd)sulfide mineralization at Glojeh,which implies intergrowth and interaction of Pb-Zn on Au concentration.Pb and Zn demonstrate relatively high mobility and are generally concentrated in the near surface zones.Nb is an immobile element during alteration and high content Hg zone is mainly restricted to narrow stripes above ore vein and veinlets.

Assessing Lateral Continuity within the Yammama Reservoir in the Foroozan Oilfield, Offshore Iran: An Integrated Study

Located in Iranian sector of the Persian Gulf, Foroozan Oilfield has been producing hydrocarbons via seven different reservoirs since the 1970 s. However, understanding fluid interactions and horizontal continuity within each reservoir has proved complicated in this field. This study aims to determine the degree of intra-reservoir compartmentalization using gas geochemistry, light hydrocarbon components, and petroleum bulk properties, comparing the results with those obtained from reservoir engineering indicators. For this purpose, a total of 11 samples of oil and associated gas taken from different producing wells in from the Yammama Reservoir were selected. Clear distinctions, in terms of gas isotopic signature and composition, between the wells located in northern and southern parts of the reservoir(i.e. lighter δ13 C1, lower methane concentration, and negative sulfur isotope in the southern part) and light hydrocarbon ratios(e.g. nC 7/toluene, 2,6-dmC7/1,1,3-tmcyC5 and m-xylene/4-mC8) in different oil samples indicated two separate compartments. Gradual variations in a number of petroleum bulk properties(API gravity, V/Ni ratios and asphaltene concentration) provided additional evidence on the reservoir-filling direction, signifying that a horizontal equilibrium between reservoir fluids across the Yammama Reservoir is yet to be achieved. Finally, differences in water-oil contacts and reservoir types further confirmed the compartmentalization of the reservoir into two separate compartments.

Lead and zinc geochemical behavior based on geological characteristics in Parkam Porphyry Copper System, Kerman, Iran

Parkam(Sarah) porphyry system is located on the metallogenic belt of Kerman, Iran. Due to existence of some copper-rich resources in this region, finding out the exact statistical characteristics such as distribution of data population, mean, variance and data population behavior of elements like Cu, Mo, Pb and Zn is necessary for interpreting their geological behavior. For this reason, precise calculation of statistical characteristics of Pb and Zn grade datasets was performed and results were interpreted geologically. The natures of Pb and Zn distributions were initially identified and their distributions were normalized through statistical treatment. Subsequently, the variograms were calculated for each exploration borehole and show that both Pb and Zn geochemical variates are spatially correlated. According to the similarity of the behavior of Pb and Zn in these calculations, it is decided to measure their exact behavior applying K-means clustering method. K-means clustering results show that the Zn grade varies linearly relative to that of Pb values and their behavior is similar. Based on the geochemical behavior similarity of Pb and Zn, throughout the pervasive secondary hydrothermal activity, they are remobilized in the similar manner, from the deep to the shallow levels of the mineralization zones. However, statistical analysis suggests that hydrothermal activity associated with secondary waters in Parkam is effective in remobilizing and enriching both Pb and Zn since they have similar geochemical characteristics. However, the process does not result in generation of economic concentrations.

Geophysical Study: Estimation of Deposit Depth Using Gravimetric Data and Euler Method (Jalalabad Iron Mine, Kerman Province of IRAN)

Mineral exploration is done by different methods. Geophysical and geochemical studies are two powerful tools in this field. In integrated studies, the results of each study are used to determine the location of the drilling boreholes. The purpose of this study is to use field geophysics to calculate the depth of mineral reserve. The study area is located 38 km from Zarand city called Jalalabad iron mine. In this study, gravimetric data were measured and mineral depth was calculated using the Euler method. 1314 readings have been performed in this area. The rocks of the region include volcanic and sedimentary. The source of the mineralization in the area is hydrothermal processes. After gravity measuring in the region, the data were corrected, then various methods such as anomalous map remaining in levels one and two, upward expansion, first and second-degree vertical derivatives, analytical method, and analytical signal were drawn, and finally, the depth of the deposit was estimated by Euler method. As a result, the depth of the mineral deposit was calculated to be between 20 and 30 meters on average.

Delineation of Geochemical Anomalies Based on Cu by the Boxplot as an Exploratory Data Analysis (EDA) Method and Concentration-Volume (C-V) Fractal Modeling in Mesgaran Mining Area, Eastern Iran

The target in this investigation is separation and delineation of geochemical anomalies for the single element Cu in Mesgaran mining area, eastern Iran. Mesgaran mining area is located in south part of Sarbishe county with about 29 Km distance to the county center. This region is part of an Ophiolite sequence and the copper anomalies seem to be related to a volcanic massive sulfide (VMS) deposit whose main part (massive sulfide Lens) has been eroded. In order to delineate Cu anomalies, the boxplot as an Exploratory Data Analysis (EDA) method and concentration-volume (C-V) Fractal modeling are employed. Both of the methods reveal low-deep anomalies which are highly correlated with geological and geophysical studies. As the main result of this study we show that Fractal modeling in spite of the Boxplot, is not recommended for complex geological settings. The proved shallow anomalies recorded by geophysical studies and defined by the used methods are in accordance to the stringer zone of a volcanic massive sulfide (VMS) deposit in Mesgaran mining area which means this region is the bottom of a VMS deposit and geochemical anomalies are related to the remained parts of the deposit.

Application of Self-Organizing Map for Exploration of REEs’ Deposition

Varieties of approaches and algorithms have been presented to identify the distribution of elements. Previous researches based on the type of problem, categorized their data in proper clusters or classes. This means that the process of solution could be supervised or unsupervised. In cases, where there is no idea about dependency of samples to specific groups, clustering methods (unsupervised) are applied. About geochemistry data, since various elements are involved, in addition to the complex nature of geochemical data, clustering algorithms would be useful for recognition of elements distribution. In this paper, Self-Organizing Map (SOM) algorithm, as an unsupervised method, is applied for clustering samples based on REEs contents. For this reason the Choghart Fe-REE deposit (Bafq district, central Iran), was selected as study area and dataset was a collection of 112 lithology samples that were assayed with laboratory tests such as ICP-MS and XRF analysis. In this study, input vectors include 19 features which are coordinates x, y, z and concentrations of REEs as well as the concentration of Phosphate (P2O5) since the apatite is the main source of REEs in this particular research. Four clusters were determined as an optimal number of clusters using silhouette criterion as well as k-means clustering method and SOM. Therefore, using self-organizing map, study area was subdivided in four zones. These four zones can be described as phosphate type, albitofyre type, metasomatic and phosphorus iron ore, and Iron Ore type. Phosphate type is the most prone to rare earth elements. Eventually, results were validated with laboratory analysis.

Investigation of Environmental and Biological Effects of Rare Earth Elements (REEs) with a Special Focus on Industrial and Mining Pollutions in Iran:A Review

The present article is a review study on the types of rare earth elements(REEs),environmental and biological effects as well as the sources of emission of these elements as pollution in nature.The purpose of this study is to provide a vision in environmental planning and control of pollution caused by REEs.The evaluation of rare earth elements was studied in human life and its environmental and biological effects,which have particular importance and are entering the life cycle through industrial and mining pollution sources.Since mining activities intensify the dispersion of these elements in the environment and the existence of industrial factories located around urban drainage system plays a unique role in creating and spreading pollution caused by rare earth elements;As a result,two case studies were conducted on two mining and industrial areas.The first case is the Choghart mine in Yazd province as an example of mining pollution,and the second case study is performed on the Kor river as an example of industrial pollution which is caused by industrial activities around it,Then the results are well explained to show both two environments of litho and hydro.Due to this fact that produced environmental pollution can cause exchange pollutant compounds with the surrounding environment besides its long-lasting destructive effects;It can cause irreversible biological effects on living organisms.By targeting this evaluation,several techniques can be proposed to prevent the entry and dispersal of rare earth elements from pollution sources besides methods to reduce the damage of these elements to the ecosystem.

A method for extracting anomaly map of Au and As using combination of U-statistic and Euclidean distance methods in Susanvar district,Iran

Quantitative descriptions of geochemical patterns and providing geochemical anomaly map are important in applied geochemistry. Several statistical methodologies are presented in order to identify and separate geochemical anomalies. The U-statistic method is one of the most important structural methods and is a kind of weighted mean that surrounding points of samples are considered in U value determination. However, it is able to separate the different anomalies based on only one variable. The main aim of the presented study is development of this method in a multivariate mode. For this purpose, U-statistic method should be combined with a multivariate method which devotes a new value to each sample based on several variables. Therefore, at the first step, the optimum p is calculated in p-norm distance and then U-statistic method is applied on p-norm distance values of the samples because p-norm distance is calculated based on several variables. This method is a combination of efficient U-statistic method and p-norm distance and is used for the first time in this research. Results show that p-norm distance of p=2(Euclidean distance) in the case of a fact that Au and As can be considered optimized p-norm distance with the lowest error. The samples indicated by the combination of these methods as anomalous are more regular, less dispersed and more accurate than using just the U-statistic or other nonstructural methods such as Mahalanobis distance. Also it was observed that the combination results are closely associated with the defined Au ore indication within the studied area. Finally, univariate and bivariate geochemical anomaly maps are provided for Au and As, which have been respectively prepared using U-statistic and its combination with Euclidean distance method.

Studying distribution of rare earth elements by classifiers,Se-Chahun iron ore,Central Iran

The increased production and price of rare earth elements(REEs) are indicative of their importance and of growing global attention. More accurate and practical exploration procedures are needed for REEs, and for other geochemical resources. One such procedure is a multivariate approach. In this study, five classifiers, including multilayer perceptron(MLP), Bayesian, k-Nearest Neighbors(KNN), Parzen, and support vector machine(SVM),were applied in supervised pattern classification of bulk geochemical samples based on REEs, P, and Fe in the Kiruna type magnetite-apatite deposit of Se-Chahun,Central Iran. This deposit is composed of four rock types:(1) High anomaly(phosphorus iron ore),(2) Low anomaly(metasomatized tuff),(3) Low anomaly(iron ore), and(4)Background(iron ore and others). The proposed methods help to predict the proper classes for new samples from the study area without the need for costly and time-consuming additional studies. In addition, this paper provides a performance comparison of the five models. Results show that all five classifiers have appropriate and acceptable performance. Therefore, pattern classification can be used for evaluation of REE distribution. However, MLP and KNN classifiers show the same results and have the highest CCRs in comparison to Bayesian, Parzen, and SVM classifiers. MLP is more generalizable than KNN and seems to be an applicable approach for classification and predictionof the classes. We hope the predictability of the proposed methods will encourage geochemists to expand the use of numerical models in future work.

The Parkam Exploration District, Kerman, Iran： Geology, Alterations, and Delineation of Cu- and Mo-Mineralized Zones Using U-Spatial Statistic with Associated Software Development

The Parkam exploration district represents an area of approximately 4 km^2 located 50 km north of Shahr-E-Babak（Kerman Province, Iran）, and has several traces of old copper mining and smelting activities. This area lies in the Kerman Copper Belt which is part of the larger Sahand-Bazman igneous and metallogenic zone hosting numerous known porphyry copper deposits and systems. The geology of the Parkam exploration district demonstrates that the area contains a diorite-type porphyry copper system hosted by volcanic and pyroclastic rocks of predominantly andesitic composition. Based on field and microscopic investigation, it was determined that the dominant types of alteration were propylitic, phyllic, argillic, and potassic, and the alteration map of the study area was produced. Expect for the propylitic alteration which was observed mainly in the host rocks, the other types of alteration are associated mainly with the dioritic subvolcanic body. Accompanied by subordinate amounts of primary sulfides, fracture-filling malachite is widespread in the potassic and phyllic zones and comprises the dominant style of mineralization at the surface of the porphyry system. Lithogeochemical data resulting from 377 samples were analyzed, and the results of background and anomaly separation by means of conventional and the U-spatial statistic method were compared. The Cu and Mo mineralizations were subsequently delineated using the U-spatial statistic. The delineated Cu mineralization is closely associated with the defined zone of potassic alteration, which is also consistent with the field and microscopic observation of the Cu mineralization in this alteration zone. The Mo mineralization delineated by the U-statistic method is mostly associated with the phyllic alteration and is spatially conformable with the zone defined for it. The source code for a software program, which was developed in the MATLAB programming language in order to perform the calculations of the U-spatial statistic method, is additionally provided. This software is compatible with geochemical variates other than Cu and Mo and can be used in similar exploration projects.