Geochemical Behavior Investigation Based on K-means and Artificial Neural Network Prediction for Copper, in Kivi region, Ardabil province, IRAN

Shirazy, Adel; Ziaii, Mansour; Hezarkhani, Ardeshir

doi:10.22034/ijme.2020.37388

Geochemical Behavior Investigation Based on K-means and Artificial Neural Network Prediction for Copper, in Kivi region, Ardabil province, IRAN

Document Type : research - paper

Authors

Adel Shirazy ¹

Mansour Ziaii ²

Ardeshir Hezarkhani ³

¹ Mining exploration, Faculty of Mining Petroleum and Geophysics, Shahrood University of Technology

² Associate Professor, Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Semnan, Iran.

³ Professor, Department of Mining & Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran.

10.22034/ijme.2020.37388

Abstract

Kivi region is located in Ardabil province of Iran. This research is on kivi geochemical sheet (on scale 1:100000) which is investigated by geological survey & mineral explorations of Iran (GSI) using stream sediment analyzes. This region consists of sedimentary, igneous and metamorphic rock units. The oldest existing sedimentary unit, the pre-Cretaceous rocks and the newest, is related to Quaternary and the present. Due to the ability of metal mineralization, especially the copper element in this region, it is important to study it carefully. Accordingly, finding information about the relation and behavior of the elements of gold, silver and molybdenum to the copper element in this region is important. The purpose of this study is to determine the behavior of geochemical halos in region. In this study with the aim of geochemical behavior investigating the mentioned elements K-means method was used. This method is based on clustering methods that minimize the total Euclidean intervals of each sample from the center of the groups to which it is assigned. In this research, the clustering quality function ( p(k) ) and the desirability of sample in the desired cluster ( S (i) ) were usedto determine the optimum numberof clusters. Then, taking into account clusters centers and results, equations were provided to predict the amount of copper with a special look at the method. After elemental behavioral studies, an artificial neural network test using general regression and backward propagation of errors was conducted to estimate the amount of copper. The accuracy value (R) of the estimation in the experimental data in the artificial neural network of general regression and backward propagation of errors was 0.77 and 0.74, respectively. Finally, it was determined that the general regression artificial neural network method has an advantage inThe optimal estimation of copper element in the studyarea.

Keywords

Kivi Region

Copper

Artificial Neural Network (ANN)

K-means Clustering

Geochemical Behavior Investigation

20.1001.1.17357616.1398.14.45.8.5

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