Choghart iron deposit is located in central Iran and 12 km north east of Bafgh, 130 km southeast of Yazd city. The dominant host rock in this area is the complex of intermediate-acidic igneous infiltrations and Mafic dykes, which have been highly altered in some parts and are known as metasomatitis. These igneous rocks have been transformed in some places to the extent of the green schist facies. Magnetite is the most abundant oxidizing ore in the region, in layered, massive and diffuse forms in the host rock of Choghart deposit. The main ore mineralization of the deposit occurred in the late Precambrian formations of high-grade (magnetite + apatite + amphibole). The data from the study of fluid transitions in monolithic apatite with magnetite shows that the Changar iron mass canal is in the range of 370 to 385 ° C. The highest salinity rate was found to be 20-39% by weight, equivalent to salt. Field evidence and micro-measurements of fluid transitions in Chaghart mineral deposit apatite show that ore-bearing fluid is very similar to that of magmatic-hydrothermal deposits of gold-copper-iron oxide (IOCG).

Geo-statistical methods for reserve estimation are difficult to use when stationary conditions are not satisfied. Artificial Neural Networks (ANNs) provide an alternative to geo-statistical techniques while considerably reducing the processing time required for development and application. In this paper the ANNs was applied to the Choghart iron ore deposit in Yazd province of Iran. Initially, an optimum Multi Layer Perceptron (MLP) was constructed to estimate the Fe grade within orebody using the whole ore data of the deposit. Sensitivity analysis was applied for a number of hidden layers and neurons, different types of activation functions and learning rules. Optimal architectures for iron grade estimation were 3-20-10-1. In order to improve the network performance, the deposit was divided into four homogenous zones. Subsequently, all sensitivity analyses were carried out on each zone. Finally, a different optimum network was trained and Fe was estimated separately for each zone. Comparison of correlation coefficient (R) and least mean squared error (MSE) showed that the ANNs performed on four homogenous zones were far better than the nets applied to the overall ore body. Therefore, these optimized neural networks were used to estimate the distribution of iron grades and the iron resource in Choghart deposit. As a result of applying ANNs, the tonnage of ore for Choghart deposit is approximately estimated at 135.8 million tones with average grade of Fe at 56.14 percent. Results of reserve estimation using ANNs showed a good agreement with the geo-statistical methods applied to this ore body in another work.

The Late Neoproterozoic-Early Cambrian albite-bearing metasomatite, rhyolites and rhyodacites predominantly constitute the host rocks of the Choghart magnetite-apatite deposit in Central Iran. The geologic evidences show three types of albites in the host albite-bearing metasomatite. The performed mineralogical and geochemical investigations display enrichment of REE-Y-Ti-Th in the pink and fleshy red albites, whereas the white albites are barren without any ore mineralization. The concentration of REE-Y-Ti-Th-U bearing minerals along the fractures, the variation of Th/U ratio and result of stable isotopes studies of the calcites syn-paragenesis with the abiltes reveal the involvement of mixed magmatic and high-midium temperature hydrothermal processes play an important role in the ore genesis. The similarity pattern of the REEs and trace elements in different types of abilte-bearing metasomatite and rhyolite manifest the origin of REE-Y-Ti-Th mineralization as the rhyolitic-rhyodacitic magmas, related to a continental/oceanic subduction zone. According to this research, tectono-magmatic setting of the albite-bearing metasomatite in the Choghart deposit is suggested as a Calc-alkaline magmatism, associated with the active continental margin and oceanic island arcs.

The metallogenic zone of Bafgh-Saghand in central Iran hosts huge low-titanium iron oxide-apatite (IOA) deposits (also called Kiruna type iron deposits) with more than 1500 Million tons grading 55% iron. The genesis of these deposits including Chadormalu, Choghart, She-Chahun, and Esfordi has long been a subject of debate. In this regard, several hypotheses such as magmatic, hydrothermal, carbonatititc, BIF, and sedimentary-exhalative have been proposed so far. In this study, 20samples of the magnetite ore from the deposits of Chadormalu, Choghart, She-Chahun, and Esfordi were selected and analyzed for their oxygen isotope values. Based on the analyses results, the variations of δ18O values in the deposits are meaningful and result from the characteristics of the ore forming processes involved. The δ18O values of the analyzed magnetite samples range from -0.1 to +2.2‰ and indicate the role of both orthomagmatic (>0.9‰) and hydrothermal (<0.9‰) processes in the formation of these deposits. On the other hand, the values lower than +0.3‰ can be attributed to secondary oxidation or hydrothermal processes and/or a combination of both. The oxygen isotope data of the investigated samples are identical to the deposits such as El Laco of Chile, Kiruna and Grängesberg of Sweden, and Zhibo and Chagangnuoer of China with a magmatic-hydrothermal genesis. According to the geological and analytical evidence obtained from the iron oxide-apatite deposits of the Bafgh-Saghand area, first a tonalite-trondhjemite-granodiorite, diorite, and granite magmatism related to a continental margin subduction at 533 to 525Ma has caused a magmatic mineralization of iron in the area, while a later hydrothermal process related to an alkaline intrusion (syenite and monzosyentie) has caused a hydrothermal mineralization. Therefore a magmatic-hydrothermal source can be suggested for the formation of the low-titanium iron oxide-apatite deposits of the Bafgh-Saghand area.

Scientific uncertainties make the grade estimation very complicated and important in the metallic ore deposits. This paper introduces a new hybrid method for estimating the iron ore grade using a combination of two artificial intelligence methods; it is based on the single layer-extreme learning machine and the particle swarm optimization approaches, and is designed based on the location of the boreholes, depth of the boreholes, and drill hole information from an orebody, and applied for the ore grade estimation on the basis of a block model. In this work, the two algorithms of optimization clustering and neural networks are used for the iron grade estimation in the Choghart iron ore north anomaly in the central Iran. The results of the training and testing the algorithms indicate a significant ability of the optimized neural network system in the ore grade estimation.

Deposit zoning is one of the significant issues in the field of modelling, evaluation and exploitation planning in the mining sector. In ore modelling, the site is divided into zones based on the physical features affecting mineralization or the spatial distribution of ore grade. In this research, self-organizing map (SOM) has been used for three-dimensional zoning of the mineral deposits. The cluster validity indices has been applied to define the optimal number of zones. The proposed algorithm verified using the data of Choghart iron deposit. The Clustering validation indices were executed based on assay data (iron and phosphorus) of exploratory boreholes and optimal number was resulted in two zones. The SOM clustering algorithm was utilized to determine the confine of each zone and assigning samples within the two zones. As a result, the output of SOM and K-means algorithms illustrated that about 90% of the data was similarly assigned to the same cluster. In SOM clustering, the discrimination surface of the two zones has the northeast-southwest orientation with the southeast slope, while the K-means algorithm determined the surface with east-west orientation and south slope. The resulting surface of SOM is in accordance with the dimensional and directional properties of the structural features (especially fault system) in Choghart iron deposit.

Early Cambrian rhyolite hosts the Choghart iron oxide-apatite (IOA) deposit. The ground radiometric and spectrometric surveys of alteration zone on the margin of the magnetite-apatite ore body show that the radioactive anomaly of thorium occurs in the breccia zone. The chemical analyses of the breccia zone samples by ICP–MS show thorium mineralization. The mineralogical studies by transmitted- and reflected-light microscopey and EPMA indicate that the main thorium minerals in thorium mineralization zone are thorite and sphene. The alteration mineral assemblages related to thorium mineralization of Choghart is consisted of albite ± orthoclase + calc-silicate (actinolite- augite- diopside) + carbonate (calcite) + magnetite + pyrite ± chalcopyrite ± galena + sphene + rutile ± microcline ± apatite (Na-Ca-Fe alteration). The occurrence of paragentic magnetite, pyrite and chalcopyrite with thorite and negative Eu anomaly in the thorium mineralization zone indicate a reduced condition for thorium mineralizing fluids. The similarity in chondrite- and mantle-normalized REE patterns of host rhyolite and the thorium mineralization zone suggests that thorium is originated from continental-arc rhyolitic magma.

The Origin of Iron Oxide-Apatite deposits (IOA) with low Ti or Kiruna type deposits has long been a matter of debate. In this case, several provenances have been proposed for these deposits which include: magmatic, magmatic-hydrothermal, hydrothermal, banded iron formations, and sedimentary-exhalative. Bafq-Saghand metallogenic zone is located in central Iran and hosts several large IOA type deposits including Chadormalu, Choghart, Se-Chahun, and Esfordi with nearly ~1500 mt ore with an average grade of 55%. Mineralization of REE-rich apatite is very common in these deposits, an issue that could be utilized for the study of their genesis. Fifteen apatite samples from the deposits of Chadormalu, Choghart, Se-Chahun, and Esfordi were taken and analyzed using LA ICP-MS. According to the geochemical analysis, the apatite of the abovemnetioned deposits show high enrichment of Y, Na, and Si, while very low content of Cl. Total REE content varies from 0.36-2.25% in which the LREE show an enrichment indicating strongly fractionation from HREE. Strong negative Eu anomaly (0.69-0.256) is observed. Sr and Y contents in apatites are 165-365 and 743-1410 ppm, respectively. The Fe-OH-Cl diagram shows that apatites is situated in the Hydroxil-fluoroapatite domain. The results show that these deposits are similar to those of IOA type deposits (e.g. Kiruna, El Laco, Abagong, Avnik, etc.). Apatite mineralization is unlikely related to carbonatitic magmatism, but situated in the Kiruna type and mafic rocks domain. The main mineralization event was likely related to tonalite-trondhjemite-granodiorite (TTG) and diorite-granite of arc magmatism (525-532 Ma) which were intruded into the Cambrian volcano sedimentary units (as country rock). Then the hydrothermal processes following alkaline intrusion (syenite and monzosyenite) led to mineralization. In general, the iron oxide-apatite (IOA) mineralization with low Ti has occurred through the magmatic-hydrothermal processes in the Bafgh-Saghand zone.

The origin of the Proterozoic Choghart iron oxide deposit in the Bafq mining district of Central Iran has been the subject of a long-standing dispute. Some authors believe that it was formed from magma, while others suggest metasomatic replacement of preexisting rocks. The present study on the basis of new oxygen isotope, REE and geochemical data concludes that neither of these two hypotheses can alone explain the formation of Choghart deposit. Instead, it is suggested that the separation of an iron oxide melt and the ensuing hydrothermal processes dominated by alkali metasomatism were both involved to different degrees in the formation of Choghart and other similar deposits in Central Iran.

Assessment and prediction of rock fragmentation by drilling and blasting operations, the most basic and most critical parameters affecting the economy of mine is considered. Choghart mine had one of the largest Iran iron ore mine that annual eight million tons of iron ore is extracted from it. In the mine mining operations, including drilling, blasting, loading, transportation and processing is done on a daily basis. Distribution of aggregation from blasting operations and resulting machinery production, mining costs may be reduced. There are several methods to determine the blasting fragmentation and currently, the use of image processing systems is appropriate and effective method for evaluating of blasting fragmentation. In this study, sieve analyzes of three patterns of explosive in Choghart mine and fragmentation curve obtained by the image processing method is compared and then this method for more accurate predictions in mine by factors of α and β is correct. The correction factors value respectively is 0.81108 and 0.82087.