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

Aref Shirazi, Adel Shirazy, Seyed Omid Hosseini, Ardeshir Hezarkhani, Amin Beiranvand Pour

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

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References


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