fractal modeling of the principal component data

Determining mineralization factors and paragenesis elements, anomaly identification and geochemical potential mapping are important issues that are carried out using multivariate statistical methods. In this study, in order to identify the mineralization processes in the Tanurcheh region on geochemical data, from the new integrated method of "fractal modeling of principal component data" based on principal component analysis (PCA) methods. and fractal concentration-area (C-A) have been used. For this purpose, first, for data preparation, by using the multivariate Mahalanobis method. to determine the role of rejecting multivariate outlier data in improving the results, outlier samples were identified and removed from the data, and the PCA method was used it was done on raw geochemical data and modified data separately. The results showed that the PCA method on raw data cannot to determine the quality of paragenesis elements, but in the case of removing outlier samples with Mahalanobis method, the results of PCA method improved. At this stage, iron, arsenic, phosphorus, lead, strontium, molybdenum, copper and gold elements were found as paragenesis elements in The first principal component was identified and this multi-element factor of mineralization was used to determine anomalous areas. In order to determine and separate the geochemical populations in the mineralization factor, the methods of standard deviation-mean and concentration-area fractal method were used. The data of the principal components were implemented and the distribution map of geochemical populations was drawn for different cases and compared with each other. The final results of the investigation of the mineralization process in the region with the mentioned methods showed that the multi-element geochemical anomaly map obtained from the fractal modeling of the principal components data showed significant agreement with field observations and mineralization outcrops.