Document Type : Original Research Paper
Authors
1 GIS Group, Faculty of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran, Iran
2 GIS Group, Faculty of Geodesy and Geomatics, K. N. Toosi University of Technology, Tehran, Iran.
3 GIS Group, Geological Survey of Iran, Tehran, Iran.
Abstract
Drilling in mine deposits has proven to be complicated, costly and time consuming process, hence it has identified the determining of optimum boreholes as a crucial issue in detailed studies. Because of some complexity in formation of mineral deposits, decreasing in risk and expenses of drilling may be continued by considering the wrapped condition of mineral deposits formation, followed by the integration of effective mineralization factors. By considering that in traditional methods of combination of mineralization’s factors like overlay and index overlay, is based on expert’s knowledge and expert knowledge is related to data accuracy, therefore, the accuracy of these methods could be remarkably decreased by large amount of data and noise. In order to solve these problems, utilization of flexible methods and powerful tools in data processing is obviously needed, especially in case of noise presence. Artificial Neural Networks are appropriate tools in large amount of data management and pattern recognition of noisy data, because of nonlinear, parallel and flexible architecture. So ANNs has been used in determining of proper position of boreholes. Neural Networks have various structures regarding their activation function and number of hidden layer and neurons in each layer. Consequently it is necessary to examine the performance of all these structures in determining the optimum position of boreholes.This paper represents a study on utilization GIS and four different Neural Networks namely: Multilayer peceptrons, Radial Basis Function, Generalized Neural Network and Probability Neural Network, for determining the position of boreholes of porphyry copper exploration in Chahfirouzeh region using cross correlation method. First, the mineralization factors are explained based on conceptual model of porphyry copper and predictor maps are produced, then, the training vectors are derived. After that, the networks are trained by geology, geochemistry and geophysics data layers. At the end, performances of the networks are compared. Implementation of Artificial Neural Networks reveals that two Neural Networks, GRNN and RBF, have the highest accuracy (approximately 80 to 83 %). Eventually, a potential map is produced by the best method.
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