A new method for determining geochemical anomalies: U-N and U-A fractal models

Undoubtedly, determining the threshold of anomalies and separating geochemical anomalies from background is one of the most important stages of minerals exploration. In the discussion of the separation of geochemical anomalies from background, there are different methods that structural methods have shown much greater efficiency than nonstructural methods. Among structural methods (methods that consider the position and location of samples), U-statistic and fractal methods have a special place. In this study, by using the algorithm of the abovementioned methods and combining them, a new method as U values fractal model (U-N and U-A) is introduced for the first time. Then, the proposed method is employed to determine the boundaries of background and anomalous populations (about the gold (Au) and arsenic (As) elements in Susanvar district). Results show that in U-N and U-A fractal models, the first fracture boundary is much clearer and more accurate than previous fractal models (C-N and C-A) in the same condition. In U-N model, due to the nature of the U method algorithm, there is a discontinuity as the exact threshold between background and anomaly that in U-A model, this does not exist due to the homogenization of U values. In this method, the exact threshold between background and anomaly is determined by the U-statistic method and by its combination with the fractal method, in each population, sub-populations are identified more accurately and simply than the concentration fractal model. Finally, a lithogeochemical map of the study area is provided for Au and As which has been prepared using U-N and U-A fractal methods. In these maps (especially the prepared maps by U-A model), the delineated Au-As mineralization is closely associated with the defined Au ore indications in the study area

Lateral Dispersion Pattern of Main Indicators at the Glojeh Polymetallic Deposit, NW Iran

The criterion-base iterative stepwise Backward Elimination (BE) method was used to predict Au according to the main variables (Ag, Cu, Pb, and Zn). The optimization process of the quadratic polynomial model are carried out on different trenches. Whereas, Pb and Zn with Ag×Zn and Pb×Zn are significant to determine the lateral dispersion of Au. It means Zn is the predominant element in near surface zone. Therefore, it point out that the polymetallic (Au-Ag-Cu-Pb-Zn) high-sulfidation hydrothermal veins may be related to a porphyry deposit at depth. Laterally, 2D surface contour maps using kriging confirms all the results of the dispersion pattern of elements at Glojeh

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

Investigation of Magneto-/Radio-Metric Behavior in Order to Identify an Estimator Model Using K-Means Clustering and Artificial Neural Network (ANN) (Iron Ore Deposit, Yazd, IRAN)

The study area is located near Toot village in the Yazd province of Iran, which is considered in terms of its iron mineralization potential. In this area, due to radioactivity, radiometric surveys were performed in a part of the area where magnetometric studies have also been performed. According to geological studies, the presence of magnetic anomalies can have a complex relationship with the intensity of radioactivity of radioactive elements. Using the K-means clustering method, the centers of the clusters were calculated with and without considering the coordinates of radiometric points. Finally, the behavior of the two variables of magnetic field strength and radioactivity of radioactive elements relative to each other was studied, and a mathematical relationship was presented to analyze the behavior of these two variables relative to each other. On the other hand, the increasing and then decreasing behavior of the intensity of the Earth’s magnetic field relative to the intensity of radioactivity of radioactive elements shows that it is possible to generalize the results of magnetometric surveys to radiometry without radiometric re-sampling in this region and neighboring areas. For this purpose, using the general regression neural network and backpropagation neural network (BPNN) methods, radiometric data were estimated with very good accuracy. The general regression neural network (GRNN) method, with more precision in estimation, was used as a model for estimating the radiation intensity of radioactive elements in other neighboring areas

Geostatistical and Remote Sensing Studies to Identify High Metallogenic Potential Regions in the Kivi Area of Iran

The Kivi area in the East Azerbaijan Province of Iran is one of the country’s highest-potential regions for metal element exploration. The primary goal herein was to process the data obtained from geochemical, geostatistical, and remote sensing tools (in the form of stream sediment samples and satellite images) to identify metallic mineralization anomalies in the region. After correcting the raw stream sediment geochemical data, single-variable statistical processing was performed, and Ti and Zn were identified as the elements with the highest degree of contrast. The relationship among these elements was further investigated using correlation and hierarchical clustering analyses. Principal component analysis was then applied to determine the principal components related to these elements, which were subsequently plotted on a regional geological map. Elements related to Ti and Zn were identified using threshold limits of anomalous samples determined via linear discriminant analysis. Lithological units and alteration patterns were detected through remote sensing investigations on Landsat-8 images. Stream sediment geochemical and remote sensing survey results identified anomalous areas of Ti and Zn in the eastern part of the study region. Our results indicate that Ti and Zn are good pathfinder elements for further exploratory investigation in this area

Geochemical relations among elements in stream sediment samples from Siojan Prospecting Area, Iran using geostatistical methods

Stream sediment samples play an important role in identifying potential areas of metallic and non-metallic mineralization in mineral exploration studies. The relationship of geochemical elements with each other shows how the elements are distributed in the area. Also, by identifying related elements, sampling and targeted chemical analysis can be used in the next stages of exploration. The purpose of this study is to investigate the elements related to the copper element in the Siojan prospecting area, which is located in South-Khorasan province and 30 km northwest of Birjand city of Iran. In Siojan area, 120 stream sediment samples of a 60 square kilometer area were collected to detect geochemical anomalies and were consequently analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for 45 elements. Preliminary geological studies showed that the studied area has copper mineralization potential, and therefore, copper was selected as the target element in this study. Copper trace elements were identified in the area and the results were used to identify copper mineralized anomalies. For the elemental analysis data, methods of Principal Component Analysis (PCA), Factor Analysis (FA), Hierarchical Cluster Analysis (HCA) and K-Means Clustering were performed to identify the relevant elements and relationships among them. Statistical analysis of the concentration of geochemical elements in the region revealed that copper and cobalt elements were identified as two elements of the same family in terms of geochemical genetics. The average value for copper and cobalt elements in the analyzed samples was 27.2 ppm and 15.5 ppm, respectively. Finally, the relationship between copper and cobalt elements was modeled as an equation using the K-Means Clustering algorithm

Ore Genesis of the Abu Ghalaga Ferro-Ilmenite Ore Associated with Neoproterozoic Massive-Type Gabbros, South-Eastern Desert of Egypt: Evidence from Texture and Mineral Chemistry

Massif-type mafic intrusions (gabbro and anorthosite) are known for their considerable resources of vanadium-bearing iron–titanium oxide ores. Massive-type gabbroic and anorthosite rocks are frequently associated with magmatic rocks that have significant quantities of iron, titanium, and vanadium. The most promising intrusions that host Fe-Ti oxide ores are the gabbroic rocks in the south-eastern desert. The ilmenite ore deposits are hosted in arc gabbroic and anorthosite rocks. They are classified into three types, namely black ore, red ore, and disseminated ore. The black ilmenite ore is located at the deeper level, while the oxidized red ore is mainly located at or near the surface. Petrographically, the gabbro and ilmenite ores indicate a crystallization sequence of plagioclase, titaniferous pyroxene, and ilmenite. This reveals that the ilmenite is a magmatic deposit formed by the liquid gravity concentration of ilmenite following the crystallization of feldspar and pyroxene. Meanwhile, quartz, tremolite, zoisite, and opaque minerals are accessory minerals. The Fe-Ti ores are composed of ilmenite hosting exsolved hematite lamellae of variable sizes and shapes, gangue silicate minerals, and some sulfides. The X-ray diffraction (XRD) data reveal the presence of two mineral phases: ilmenite and hematite formed by the unmixing of the ferroilmenite homogeneous phase upon cooling. As a result, the ore is mostly made up of hemo-ilmenite. Using an electron microscope (SEM), as well as by observing the textures seen by the ore microscope, ilmenite is the dominant Fe-Ti oxide and contains voluminous hematite exsolved crystals. Under the scanning electron microscope, ilmenite contained intergrowths of hematite as a thin sandwich and lens shape. The formation of hematite lamellae indicates an oxidation process. Mineral chemistry-based investigations reveal late/post-magmatic activity at high temperatures. The examined ilmenite plots on the ferro-ilmenite line were created by continuous solid solution over 800 °C, whereas the analyzed magnetite and Ti-magnetite plot near the magnetite line and were formed by continuous solid solution exceeding 600 °C

Physicochemical controls on alteration and copper mineralization in the Sungun porphyry copper deposit, Iran

The Sungun porphyry copper deposit is associated with an Andean-type, calc-alkaline diorite/granodiorite to monzonite/quartz-monzonite stock of Miocene age which intruded Eocene volcanosedimentary and Cretaceous carbonate rocks. The intrusive phases are related by fractional crystallization, although surprisingly, the diorite/granodiorite which hosts the mineralization postdates the more evolved quartz monzonite. Copper mineralization was accompanied by both potassic and phyllic alteration. The hydrothermal system involved both magmatic and meteoric waters, and boiled extensively. Molybdenum was concentrated at a very early stage in the evolution of the hydrothermal system and copper later. Early hydrothermal alteration, which was caused by high temperature (340 to >500°C), high salinity ( ~ 60 wt % NaCl equiv.) orthomagmatic fluid, produced a potassic assemblage characterized by addition of K and Cu and depletions in Na, Ca, Mg and Fe in the central part of the stock. Propylitic alteration, which is attributed to a liquid-rich, lower temperature (240--330°C), Ca-rich, evolved meteoric fluid occurred contemporaneously with potassic alteration, but in the peripheral parts of the stock. Phyllic alteration occurred later, at temperatures in the range from 300 to 360°C, overprinting these earlier alterations, and was accompanied by additions of Si (silicification) at the expense of Na, K and Fe and remobilization of Cu from the potassic zone. It resulted from the inflow of oxidized and acidic meteoric waters with decreasing temperature of the system. During potassic alteration, copper solubility is calculated to have been >100,000 ppm, whereas the copper content of the initial fluid responsible for ore deposition was 1200--3800 ppm. This indicates that the fluid was initially undersaturated with respect to chalcopyrite, which agrees with the observation that veins which formed at T > 400°C contain molybdenite but rarely chalcopyrite. Copper solub

Predictive Cu porphyry potential mapping using fuzzy modelling in Ahar–Arasbaran zone, Iran

Fuzzy set theory was successfully used to map areas of copper porphyry mineralization potential in the Ahar–Arasbaran district of Iran. Proximity to geological features is translated into fuzzy membership functions based upon qualitative and quantitative knowledge of spatial associations between known Cu porphyry occurrences and geological features in the area. Fuzzy sets of favourable lithology, geochemical anomaly, geophysical anomaly, structural feature, and alterations are combined using fuzzy logic as the inference engine. The fuzzy predictive maps delineate 84% of the known Cu porphyry occurrences. The results demonstrate the usefulness of a geologically constrained fuzzy set approach to map mineral potential and to redirect surficial exploration work in the search for yet undiscovered Cu porphyry mineralization in the mining district. The method described is applicable to other mining districts elsewhere