rock type

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.‎