GEOCHEMICAL BEHAVIOR INVESTIGATION BASED ON K-MEANS AND ARTIFICIAL NEURAL NETWORK PREDICTION FOR TITANIUM AND ZINC, KIVI REGION, IRAN

• Main Authors: Adel Shirazy, Mansour Ziaii, Ardeshir Hezarkhani, Timofey V. Timkin, Valery G. Voroshilov
• Format: Article
• Language: Russian
• Published: Tomsk Polytechnic University 2021-03-01
• Series: Известия Томского политехнического университета: Инжиниринг георесурсов
• Subjects: titanium; zinc; kivi region; k-means clustering method; artificial neural network; estimation of the elements grade
• Online Access: http://izvestiya.tpu.ru/archive/article/view/3106/2428

The relevance. These are the first studies in the Kivi region. Due to the presence of titanium and zinc in the area, these studies are necessary. Artificial Neural Network and K-means methods for element behavior measurement are new methods in mineral exploration. The main aim of the research is to identify Ti and Zn geochemical behavior for prediction Ti by ANN and K-means methods. Object: Kivi 1:100000 geochemical map in Ardabil province, Iran. Methods. The samples taken from bottom sediments of the Kiwi region, which were analyzed by the ICP-MS method, served as the initial data. Then, the behavior of these elements in relation to each other and their geographical coordinates was analyzed by the K-means clustering method. The amount of titanium was also predicted with the artificial neural network (ANN- GRNN). Results. The Ti and Zn elements relationship was determined using this K-means method taking into account the latitude and longitude of the samples to estimate the grade and more accurate estimation of the appearance and extent of the geochemical halos in the studied area. According to the results obtained during processing of these elements, a regression equation was drawn up to estimate the titanium content based on three parameters: Zn content, the length and width of the sampling points, the correlation coefficient. According to the K-means cluster centers and artificial neural network, the Ti element grade was predicted and the correlation coefficient was reported 0,51. Both methods produce the desired results, but the artificial neural network method has more accurate data. Schematic maps of the initial and predicted Ti content were constructed. The results of the study can be used in the course of geological exploration to forecast and identify new promising areas.