عنوان مقاله [English]
In the present research, a probabilistic neural network based on the Bayesian probabilistic algorithm was employed to classify the grade of Ali-Abad copper deposit in Yazd. For this purpose, induced polarization (IP) and resistivity (Rs) geophysical data and rock type of exploration borehole cores as geological information corresponding to four geophysical profiles, DD-1, PD-2, PD-3 and PD-4 were used as input parameters as well as the copper grade of the boreholes as target parameter. To achieve the goal, 488, 528, 188, and 456 data were randomly collected from the sections related to DD-1, PD-2, PD-3 and PD-4 geophysical profiles so that 75% of total data were selected for training and 25% to test the probabilistic neural network. The performance of the proposed approach was evaluated by confusion matrix through the ratio of summation of data on the main diameter to the total test data, as well as determination of Commission and Omission errors. The results of the research show that the probabilistic neural network could estimate the test data for DD-1, PD-2, PD-3 and PD-4 profiles with accuracy of 60, 74, 60 and 83.3%, respectively which are reasonable considering the type of available data. In addition, the results were qualitatively evaluated through plotting isograde maps of four exploratory cross-sections over the geophysical profiles. This process was carried out using the assay data of exploration boreholes, gridding and the grid interpolation with the high accurate kriging estimation method, which was leaded to favorite results.