Scientific Quarterly Journal of Iranian Association of Engineering Geology

Scientific Quarterly Journal of Iranian Association of Engineering Geology

Application of probabilistic neural network method for classification of ‎Yazd, Ali-Abad copper deposit ‎

Document Type : Original Article

Authors
Reza Ahmadi 1
Masoud Lashgari Ahangarani 2
1 Faculty member of Mining Engineering Department, Engineering of Earth Science College, Arak University of Technology
2 Mining Engineering Department,, Arak University of Technology, Arak, Iran
Abstract
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.‎
Keywords
Yazd Ali-Abad copper deposit
Bayesian probabilistic neural network
Resistivity and ‎Induced polarization
Rock type
Grade
Adeli, H., Panakkat, A., 2009. A probabilistic neural network for earthquake magnitude prediction, Neural networks, 22(7), 1018-1024.
Bishop, C.M., 2006. Pattern recognition and machine learning information: science and statistics, Springer, p. 758.
Huang, X.D., Wang, C.Y., Fan, X.M., Zhang, J.L., Yang, C., Wang, Z.D., 2018. Oil source recognition technology using concentration-synchronous-matrix-fluorescence spectroscopy combined with 2D wavelet packet and probabilistic neural network, Science of The Total Environment, 616, 632-638.
Khoyee, N., Ghorbani, M., Tajbakhsh, P., 2000. Copper deposits in Iran, Geological survey and mineral exploration of Iran. p. 421 (In Persian).
Kusy, M., Zajdel, R., 2014. Probabilistic neural network training procedure based on Q (0)-learning algorithm in medical data classification, Applied Intelligence, 41(3), 837-854.
Leite, E.P., Souza Filho, C.R., 2009. Probabilistic neural networks applied to mineral potential mapping for platinum group elements in the Serra Leste region, Carajás Mineral Province, Brazil, Computers & Geosciences, 35(3), 675-687.
Li, P., 2011. Structural damage localization using probabilistic neural networks, Mathematical and Computer Modelling, 54(3-4), 965-969.
Mao, K.Z., Tan, K.C., Ser, W., 2000. Probabilistic neural-network structure determination for pattern classification, IEEE Transactions on neural networks, 11(4), 1009-1016.
Murphy, K.P., 2012. Machine learning: a probabilistic perspective adaptive computation and machine learning, MIT press, p. 1104.
Saman Kav, Consulting engineers Co., 2006. Final report of exploration geophysics by induced polarization (IP/Rs) method in the region of Ali-Abad copper deposit, Yazd province (In Persian). 
Specht, D.F., 1990. Probabilistic neural networks, Neural networks, 3(1), 109-118.
www.rockware.com/Rockworks16
 
Scientific Quarterly Journal of Iranian Association of Engineering Geology
Volume 14, Issue 3
Winter 2022
Pages 65-76

  • Receive Date 10 September 2020
  • Revise Date 19 December 2020
  • Accept Date 25 January 2021