Vol 13 (2): 110-128, December 2022 eISSN: 2536-8400 Faculty of Science http://doi.org/10.4038/rjs.v13i2.119 University of Ruhuna SRI LANKA Faculty of Science, University of Ruhuna Sri Lanka 110 Geochemical relations among elements in stream sediment samples from Siojan Prospecting Area, Iran using geostatistical methods Aref Shirazi1, Adel Shirazy1,*, Seyed Omid Hosseini2 , Ardeshir Hezarkhani1, and Amin Beiranvand Pour3,4 1Faculty of Mining Engineering, Amirkabir University of Technology, Tehran, Iran 2 Department of Mine Engineering, Imam Khomeini International University, Qazvin, Iran 3 Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu (UMT), Kuala Nerus 21030, Terengganu, Malaysia 4 Geoscience and Digital Earth Centre (INSTeG), Research Institute for Sustainable Environment, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia *Correspondence: Adel.Shirazy@gmail.com; ORCID https://orcid.org/0000-0001-7756-3205 Received: 8th September 2021, Revised: 3rd October 2022, Accepted: 2nd December 2022 Abstract 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. Keywords: Anomaly separation, Clustering, Copper trace elements, PCA, Factor analysis 1 Introduction Geochemical investigations in mineral exploration are carried out to identify anomalous elements of mineralization (metallic or non-metallic) in the area (Roonwal https://rjs.ruh.ac.lk/index.php/rjs/index http://doi.org/10.4038/rjs.v13i2.119 https://creativecommons.org/licenses/by-nc/4.0/ Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 111 2018). In the reconnaissance stage, based on geological and geochemical investigations (sampling), the location of the anomalies of the target element is determined. The importance of this task is to separate the potential areas and barren areas from each other (Gourley 2019, Talapatra 2020). Geochemical relationships between the elements from the stream sediment samples are analyzed by various methods. The most important and practical methods that have been used by researchers in different areas are multifractal analysis, Principal Component Analysis (PCA), clustering, Artificial Neural Network (ANN) and Machine Learning Algorithms (Yin et al. 2021, Shirazi 2022, Shirazi et al. 2022). Each of the mentioned methods has advantages and disadvantages, which are chosen according to the study purpose (Garcia et al. 2020, Mohammadi et al. 2018). In the present study, the aim is to identify the elements related to the copper element in the exploratory area of Siojan. For this purpose, Principal Component Analysis (PCA), Factor Analysis (FA), Hierarchical Cluster Analysis (HCA) and K-Means clustering algorithm methods have been chosen. In this way, elemental analysis is performed among all analyzed data from stream sediment samples, and elements related to copper element are identified. The elements detected are called copper trace elements in the area. The Siojan prospecting area is located in South-Khorasan province (East Iran) and 30 km northwest of Birjand city. In terms of structural geology, this area is located in the structural zone of Sistan (Tirrul et al. 1983). The studied area has been confirmed in terms of copper mineralization, so that extensive copper oxide and copper sulfide mineralization have been observed in it. Considering that the topic of the current research in this area has not been investigated comprehensively so far, the main objectives of the study are: (1) Descriptive statistics analysis; (2) Determining the geochemical threshold limit of copper element in stream sediment samples of the area; (3) Investigating of geochemical relationships among elements; (4) Separation of mineral elements and rock-forming elements in the area. 2 Methodology 2.1 Study area and geological setting The study region is located in 32° 58’ 23’’ N latitude and 58° 56’ 7’’ E longitude 30 km northwest of Birjand city in South Khorasan of Iran (Figure 1). Siojan area geologically and structurally located in the Philish zone of eastern Iran. Exposed rock units in this area include Phyllis-like sediments (Paleocene-Eocene), limestones, Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 112 sedimentary-volcanic facies including marl, tuff and ignimbrite (Eocene) and volcanic rocks (Eocene- Oligocene) including agglomerate, dacite, andesite, and ignimbrite. Fig 1. Location and geological map of the area Alteration zones in this region are seen in relation to Eocene-Oligocene volcanic and around the location of faults and fractures. Alteration zones include argillic alteration in the center and west of the area, siliceous in the center and parts of the south of the area, and carbonation (Malekian et al. 2022). In general, the main structural control faults of the region can be divided into two general categories, which include, 1) Northwest-southeast faults that these faults have a greater role in determining the structural status of the region than the second category faults and they often have a non-slip function, and 2) faults with north, northeast-south, southwest, which are more of normal slip slope. In addition to metal mineralization in the study area, small and scattered masses of gypsum can be seen in the south of the study area. Fig 2. The location of stream sediment samples Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 113 2.2 Sampling Geochemical sampling in Siojan area was prepared through stream sediment sampling method. The initial design of the sampling points was mainly based on determining the center of stream’s gravity. For this purpose, a map of the drainage systems (Shirazi et al. 2022b) of the study area was generated using topographic maps and aerial images (Figure 2). Stream sediment samples with a particle size of 80 mesh were collected from the study area. The area was covered with 120 stream sediment samples in the form of ICP MS 44 elements analysis (Figure 2) (Shirazi et al. 2018a, b, c). 2.3 Technical flow The most important discussion in the analysis of geochemical data is to determine the background limit for each element in the study area and to separate the background from the anomalies of the relevant element (Carranza 2008). Another important issue in geochemical exploration is the simultaneous study of the elements under study (Alahgholi et al. 2018, Shirazy et al. 2021 a, b, Zhang et al. 2019). One of the most powerful methods in this field is factor analysis as well as principal component analysis. These methods have two advantages (Mohammadi et al. 2018): 1) Reducing the dimensions of data, and 2) expressing the existing relationship between different elements. Especially with the large number of elements studied and the large number of samples, the role of factor analysis becomes more apparent, so that it is much easier to understand the variability of the data . Factor analysis is based on PCA method. This is a technical method for finding a linear combination of the same initial variables to form a new coordinate system. These linear compounds are called principal component analysis and have the following properties (Carranza 2009), 1) most of the variability can be explained by a limited number of new variables, 2) new variables, which are the product of the linear combination of the initial variables, do not show correlation between themselves. Before using this method, it is necessary to pay attention to two points :If the initial variables are not correlated (or have a small correlation coefficient) there is no reason to use this method, because they do not give acceptable results .Factor analysis is performed when the number of initial variables is sufficient . Invoice analysis is performed in four steps : 1) Calculation of correlation coefficients, 2) Extraction of factors, which includes determining the number and method of calculating factors, 3) Applying special transformations on factors, in order to better interpret the relationships between data, 4) calculating the score of each factor for each sample . Clustering is the placement of data in groups where the members of each group are similar to each other on a specific parameter. The similarity between the data within each cluster is maximum and the similarity between the data within different clusters is minimal (Doodran et al. 2020; Shirazy et al. 2022, Yang et al. 2019) . The structure of these clusters or groups can be consistent with the nature of the data or the hidden Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 114 structure that lies within the data. Clustering is finding a structure in a set of data that is not categorized. The main reason for using clustering methods is to discover new structures that exist naturally in the data without any prior knowledge of the structure of classes or categories (Nazerian et al. 2021, Shirazy et al. 2018 d, e, Yu et al. 2019). The results of geochemical studies can be useful in future studies in the field of mineral exploration (Beygi et al. 2021, Shirazi et al. 2018b). For example, in remote sensing surveys and processing of satellite images, they are used in combination to verify the mapping performed (Shirazi et al. 2018 b, Shirazy et al. 2021 c,d, Shirazy et al. 2020 a,b). It is also used in exploratory geophysical studies to integrate information layers and determine the drilling location of boreholes (Khayer et al. 2020; Shirazy et al. 2021). 3 Results and Discussion 3.1 Descriptive statistical investigation Due to the polymetallicity of the region, the correlation of elements (Cu, Co, Ag, Sb, Mo, Zn) (Table 1) were identified for analysis. Because our data are not normal, Spearman method was used to determine the correlation. Table 1: Elements and their correlation with Spearman method. Co Cu Mo Sb Zn Ag Co 1 .586 .076 .369 .204 .097 Cu .586 1 .141 .385 .419 .057 Mo .076 .141 1 .448 .131 .199 Sb .369 .385 .448 1 .255 .195 Zn .204 .419 .131 .255 1 .080 Ag .097 .057 .199 .195 .080 1 The statistical results on the data are given in Table 2 . Table 2: Basic statistical results. Min Max Sum Mean Std. deviation variance skewness kurtosis Ag 0 1 42 0.35 0.202 0.041 1.298 0.221 2.659 0.438 Co 9 23 1860 15.50 2.961 8.770 - 0.013 0.221 - 0.080 0.438 Cu 15 50 3272 27.27 5.391 29.066 0.450 0.221 1.490 0.438 Mo 0 3 122 1.01 0.422 0.178 1.764 0.221 3.789 0.438 Sb 0 7 126 1.05 1.013 1.026 3.123 0.221 12.336 0.438 Zn 30 68 5417 45.14 8.063 65.015 0.419 0.221 -0.579 0.438 Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 115 3.2 Copper threshold limit investigation The most important discussion in the analysis of geochemical data is to determine the background threshold for each element in the region and to separate the background threshold from the anomaly of the relevant element (Cheng et al. 1996, Khosravi et al. 2022, Shirazy et al. 2020 c,d, Shirazi et al. 2018 a, c, Shirazy et al. 2022 a ,b). Using statistical methods, the threshold concentration of geochemical elements is identified. To be used in geochemical modelling and geochemical halo identification. Identifying the concentration threshold of the desired elements is used in mineral processing studies to decide on the economics and how the elements are extracted from the mineral (Doodran et al. 2020, Khakmardan et al. 2020b, Khakmardan et al. 2018). Anomaly threshold calculator formula: According to the above formula: MAD is 3.1 ppm and Median is 27.1 ppm as well as after insertion in the formula : Anomaly = Median + 3 MAD Our anomalous threshold was estimated to be 33.1 ppm and our anomalous map was created as Figure. Blue indicates anomaly and background is green and other colors indicate anomalous probabilities (Mohammadi et al. 2016). Fig 3. Anomalous areas with blue and green is background (Coordination in UTM, Zone : 39) (1) SXAM 2+= (2) SXAE 3+= (3) || MeanXiMeanMAD −= (4) || MedianXiMedianMAD −= Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 116 3.3 Principal Component Analysis (PCA) According to the selected elements, the analysis was performed on the data and the results were announced according to Table 3 . Table 3. Principal component analysis table. Variable PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 Cu 0.513 -0.371 -0.048 0.04 0.296 0.712 Co 0.46 -0.345 -0.247 0.461 0.143 -0.613 Ag 0.201 0.521 -0.811 -0.157 0.024 0.072 Sb 0.492 0.261 0.236 0.204 -0.763 0.103 Mo 0.318 0.603 0.462 0.053 0.553 -0.117 Zn 0.376 -0.201 0.1 -0.847 -0.062 -0.296 Eigen Value 2.3101 1.1922 0.8529 0.8148 0.4707 0.3592 Proportion 0.385 0.199 0.142 0.136 0.078 0.6 Cumulative 0.385 0.584 0.726 0.862 0.94 1 In the Table 3, according to the amount of special values and also the following diagram (Figure 4), the first three components that explain 73% of the behavior were selected . Fig 4. Scree plot in PCA The outlier data were announced by principal component analysis method with Mahalanobits interval as shown in Figure 5. Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 117 Fig 5. Outlier data detection based on Mahalanobis distance. Outlier data were replaced with appropriate values. Then the corrected data were used for statistical analysis (Levin 2011, Kim 2013, Kwak and Kim 2017, Alahgholi et al. 2018, Aali et al. 2022a, b, Shirazy et al. 2022a, b, c, d, Shirazi et al. 2022a, b). Based on the principal component analysis (PCA) method, the scores of different elements in each principal component were evaluated. Then the scores of the elements were mapped in each of the first, second and third principal components (PC1, PC2 and PC3). The map obtained from each of the first, second and third principal components are presented in Figures 6, 7 and 8, respectively. Fig 6. Contour map of principal component analysis method, First component (PC1) (Coordination in UTM, Zone: 39) Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 118 Fig 7. Contour map of principal component analysis method, second component (PC2) (Coordination in UTM, Zone: 39) Fig 8. Contour map of principal component analysis method, third component (PC3) (Coordination in UTM, Zone: 39) The studied elements were clustered using principal component analysis (PCA) method. For this purpose, the loads related to each of the elements in the first and second main components were plotted against each other. Elements close to each other were grouped together. Based on this clustering, 3 different group classes were obtained. Silver and molybdenum elements group, zinc, cobalt and copper elements group and antimony single element group (Figure 9). Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 119 Fig 9. Clustering by PCA method using components one and two. 3.4 Factor Analysis (FA) Factor analysis is a multivariate statistical method that establishes a special relationship between a large set of seemingly unrelated variables under a hypothetical model. In factor analysis, a large number of variables are expressed in terms of a small number of dimensions or structures; this structure is called a factor. In methods based on eigenvectors using eigenvalues and eigenvectors, directions with maximum variability are identified. In order to analyze the geochemical data of stream sediments, the rotary type factor analysis method with variomex rotation was used (Reimann et al. 2005, Shirazy et al. 2018a, b, c, Shirazy et al. 2019, Shirazi et al. 2021, Hedayat et al. 2022, Shirazy et al. 2022c). In Table 4, by selecting 3 factors and also the method of maximum likelihood, the numbers for each factor in each element are shown in Figures 10 to 12. Table 4. Factor analysis table. Variable Factor 1 Factor 2 Factor 3 Communality Cu 0.63 -0.35 0.398 0.678 Co 0.979 0.057 -0.25 0.962 Ag 0.106 -0.213 -0.177 0.088 Sb 0.405 -0.574 -0.179 0.526 Mo 0.104 -0.599 -0.344 0.488 Zn 0.239 -0.383 0.338 0.318 Variance 1.5979 1.0056 0.4555 3.0589 % Var 0.266 0.168 0.076 0.51 Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 120 Fig 10. Contour map of factor one (Coordination in UTM, Zone: 39) Fig 11. Contour map of factor two (Coordination in UTM, Zone: 39). Fig 12. Contour map of factor three (Coordination in UTM, Zone: 39) Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 121 According to the loads in the first two factors, Figure 13 can be drawn. Fig 13. Factor classification according to factor one and two. Or in the case of factors one and two, it is shown schematically in Figure 14. Fig 14. Factor classification according to factor one and two schematically. Based on the obtained results of factor analysis method, another grouping of the studied elements can be presented. This grouping includes cobalt, copper and zinc groups, silver and molybdenum groups and single element antimony group. 3.5 Hierarchical Cluster Analysis (HCA) In data mining and statistics, "hierarchical clustering" is a method that performs the act of categorizing and grouping observations and data in a hierarchical manner. The point that distinguishes this method from other clustering methods is the sequence and top- down (or bottom-up) view that exists in this technique (Khakmardan et al. 2020a; Khayer et al. 2021; Madani et al. 2021; Nazerian, Catania, et al. 2022; Nazerian, Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 122 Shirazy, et al. 2022). For clustering, the hierarchical distance clustering method was used and the results were presented according to Table 5 and the dendrogram in Figure 15. Table 5: Hierarchical clustering parameters. Agglomeration Schedule Stage Cluster Combined Coefficients Stage Cluster First Appears Next Stage Cluster 1 Cluster 2 Cluster 1 Cluster 2 1 3 5 74.721 0 0 2 2 3 4 136.962 1 0 4 3 1 2 18884.820 0 0 4 4 1 3 57148.272 3 2 5 5 1 6 177736.097 4 0 0 Fig 15. Final hierarchical clustering dendrogram. Geochemical elements can be grouped according to the results of hierarchical analysis. In this grouping, silver, molybdenum and antimony elements are in one group, copper and cobalt elements in one group and zinc element as a single element in a separate group. 3.6 K-Means Clustering Analysis As it was observed, according to the results of statistical analysis, copper and cobalt elements are inherently geochemically related to each other. Therefore, the geochemical behavior of these two elements in relation to each other (as an exploratory key in the study region) (Shirazy et al. 2019), is important. In order to investigate the geochemical behavior of Cu and Co elements, K-Means clustering method was used Khayer et al. 2020, Shirazy et al. 2022c). * * * * * H I E R A R C H I C A L C L U S T E R A N A L Y S I S * * * * * * Dendrogram using Average Linkage (Between Groups) Rescaled Distance Cluster Combine C A S E 0 5 10 15 20 25 Label Num +---------+---------+---------+---------+---------+ Ag 3 ─┐ Mo 5 ─┼───────────────┐ Sb 4 ─┘ ├───────────────────────────────┐ Cu 1 ─────┬───────────┘ │ Co 2 ─────┘ │ Zn 6 ─────────────────────────────────────────────────┘ Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 123 Classification of 3 classes, with an average value of 0.5237 Classification of 4 classes, with an average value of 0.5556 Classification of 5 classes, with an average value of 0.5468 Classification of 6 classes, with an average value of 0.4677 Classification of 7 classes, with an average value of 0.4471 Classification of 8 classes, with an average value of 0.4975 Fig 16. Profile images obtained from the classification of Cu and Co elements along with the mean values of the utility function. Initially, the data set was categorized into different classes using the K-Means algorithm. Then the amount of utility function for each classification was calculated. As shown in Figure 17, the image of the silhouettes is presented along with the mean value of the utility function. Based on the results of geochemical data classification of copper and cobalt elements, classification of 4 classes with the highest value of the utility function was selected as the optimal clustering (Ahmadi et al. 2022, Shirazy and Hezarkhani 2018, Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 124 Shirazy et al. 2022; Shirazy et al. 2021). The graph of the values of the utility function for classifying 3 classes to 20 classes is presented in Figure 17. Fig 17. The graph of the values of the utility function (silhouette) for classifying 3 classes to 20 classes. Fig 18. Geochemical behavior of copper (Cu) vs. cobalt (Co) elements. Cu and Co concentrations were calculated in the centers of 4-class classification based on K-Means algorithm. Using regression method, the values of the centers of the clusters were plotted. The diagram shown in Figure 18 is actually the geochemical behavior of copper and cobalt elements in samples of stream sediments in the Siojan region. As can be seen in the geochemical behavior diagram of the elements copper and cobalt, the concentrations of these two elements are directly related to each other. Thus, with increasing Cu concentration, Co concentration also increases. This increase in value is calculated as a quadratic curve. The third-degree equation of geochemical behavior of copper and cobalt concentrations is: Cu = 0.272Co3 - 13.345Co2 + 218.47Co - 1163.9 4 Conclusions Siojan exploration region is located in South Khorasan province of Iran with the mineralization potential of copper element. Copper mineralization has occurred in this area. Therefore, it was used as a target for studies to identify geochemical anomalies. Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 125 120 geochemical samples of stream sediments were collected according to the designed sampling map. After preparing the samples, they were analyzed by ICP-MS method. From each sample, 44 elements were obtained as a result of analysis . Data were prepared using statistical methods. Then, in order to identify the relationships of geochemical elements in the region, the methods of principal component analysis, factor analysis and hierarchical analysis were used. The grouping performed on the geochemical elements by the methods of principal component analysis and factor analysis found similar results. Based on these results, three groupings of geochemical elements of the region were presented. Cu, Co and Zn group, Ag and Mo group and single element Sb group. According to the same result, the methods of principal component analysis and factor analysis in the grouping of geochemical elements in the region, in order to increase the accuracy of research and identify elements related to the element, hierarchical analysis was used. Based on this method, geochemical elements showed different groupings. This grouping also had similarities with the grouping obtained from the results of other methods. According to this grouping, the elements Ag, Mo and Sb in one group, Cu and Co elements in one group and Zn as a single element in a separate group. Due to the fact that the target element in this study was copper, based on the results of statistical studies, the genetic family of this element in Siojan region is cobalt. Although the element zinc was also included in the two groups, which was part of the copper group family by the method of principal component analysis and factor analysis, but it cannot be mentioned as an intrinsic relationship in the region. Another reason is the inherent and permanent correlation between zinc and lead, which was not achieved in statistical analysis . Geochemical behavioral studies of Cu and Co elements based on K-Means algorithm show that these two elements have direct behavior with each other. Behavioral changes in the concentration of elements relative to each other were calculated as a quadratic equation. Using this equation, the concentration of each element can be calculated based on the other element. The calculated equation is Cu = 0.272Co3 - 13.345Co2 + 218.47Co - 1163.9. Acknowledgements We appreciate the Department of Mining and Metallurgy Engineering Amirkabir University of Technology (Tehran Polytechnic). The Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu (UMT) and Research Institute for Sustainable Environment, Universiti Teknologi Malaysia are also acknowledged for providing facilities during editing, rewriting, re-vising, and re-organizing the manuscript. Great appreciation should go to anonymous journal reviewers/ editors for their constructive comments on the primary version of the manuscript. Aref Shirazi et al. Stream sediment analysis from Siojan Prospecting Area, Iran Ruhuna Journal of Science Vol 13 (2): 110-128, December 2022 126 References Aali AA, Shirazi A, Shirazy A, Khakmardan S, Maghsoudi A, Hezarkhani A. 2022a. Geophysical Study to Identify Iron Mineralization Anomalies Using Terrestrial Magnetometry in the Chak-Chak Exploration Area, Iran. Türkiye Jeoloji Bülteni 65(2): 159-170. Aali AA, Shirazy A, Shirazi A., Pour AB, Hezarkhani A, Maghsoudi , Hashim M, Khakmardan S. 2022b. 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