Kamran Mostafaei - Department of Mining, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran.
Shahoo Maleki - Faculty of Mining Engineering, Amirkabir University of Technology, Tehran, Iran.
Behshad Jodeiri Shokri - School of Engineering, University of Southern Queensland, Springfield Campus, Springfield, Australia.
Mahyar Yousefi - Associate professor, Faculty of Engineering, Malayer University, Malayer, Iran .

This paper uses support vector machine (SVM), back propagation neural network (BPNN), and Multivariate Regression Analysis (MLA) methods to predict the gold in the Dalli deposit situated in the central province of Iran. After analyzing the data, the dataset was prepared. Subsequently, through comprehensive statistical analyses, Au was chosen as the output element for modelling, while Cu, Al, Ca, Fe, Ti, and Zn were considered input parameters. Then, the dataset was divided into two groups: training and testing datasets. For this purpose, ۷۰% of the datasets were randomly entered into the data process, while the remaining data were assigned for the testing stage. The correlation coefficients for SVM, BPNN, and MLA were ۹۴%, ۷۵%, and ۶۸%, respectively. A comparison of these coefficients revealed that all used methods successfully predicted the actual grade of Au. However, the SVM was more reliable and accurate than other methods. Considering the sensitivity of the gold data and the small number in the exploratory database, the results of this research are used to prepare the main layer in the mineral prospectivity mapping (MPM) of gold in ۲ and ۳D.

Gold grade estimation, Support vector machine, Back propagation neural network, Dalli deposit, Iran