Document Type : Original Research Paper


Department of Mining and Metallurgy Engineering, Amirkabir University of technology, Tehran, Iran


In this paper, we aim to achieve two specific objectives. The first one is to examine the applicability of wavelet neural network (WNN) technique in ore grade estimation, which is based on integration between wavelet theory and Artificial Neural Network (ANN). Different wavelets are applied as activation functions to estimate Cu grade of borehole data in the hypogene zone of porphyry ore deposit, Shahr-e-Babak district, SE Iran. WNN parameters such as dilation and translation are fixed and only the weights of the network are optimized during its learning process. The efficacy of this type of network in function learning and estimation is compared with Ordinary Kriging (OK). Secondly, we aim to delineate the potassic and phyllic alteration regions in the hypogene zone of Cu porphyry deposit based on the estimation obtained of WNN and OK methods, and utilize Concentration–Volume (C–V) fractal model. In this regard, at first C–V log–log plots are generated based on the results of OK and WNN. The plots then are used to determine the Cu threshold values of the alteration zones. To investigate the correlation between geological model and C-V fractal results, the log ratio matrix is applied. The results showed that, Cu values less than 1.1% from WNN have more overlapped voxels with phyllic alteration zone by overall accuracy (OA) of 0.74. Spatial correlation between the potassic alteration zones resulted from 3D geological modeling and high concentration zones in C-V fractal model showed that the alteration zone has Cu values between 1.1% and 2.2% with OA of 0.72 and finally have an appropriate overlap with Cu values greater than 2.2% with OA of 0.7. Generally, the results showed that the WNN (Morlet activation function) with OA greater than OK can be can be a suitable and robust tool for quantitative modeling of alteration zones, instead of qualitative methods.


[1]. Lowell, J.D. and Guilbert, J.M. (1970). Lateral and vertical alteration-mineralization zoning in porphyry ore deposits. Economic Geology 65, 373-408.
[2]. Hezarkhani, A. and Williams-Jones, A.E. (1998). Controls of alteration and mineralization in the Sungun porphyry copper deposit, Iran; evidence from fluid inclusions and stable isotopes. Economic Geology 93, 651-670.
[3]. Asghari, O., Hezarkhani, A. and Soltani, F. (2009). The comparison of alteration zones in the Sungun porphyry copper deposit, Iran (based on fluid inclusion studies). Acta Geologica Polonica 59, 93-109.
[4]. Soltani, F., Afzal, P. and Asghari, O. (2014). Delineation of alteration zones based on Sequential Gaussian Simulation and concentration–volume fractal modeling in the hypogene zone of Sungun copper deposit, NW Iran. Journal of Geochemical Exploration 140, 64-76.
[5]. Schwartz, G.M. (1947). Hydrothermal alteration in the" porphyry copper" deposits. Economic Geology 42, 319-352.
[6]. Beane, R. (1982). Hydrothermal alteration in silicate rocks. University of Arizona Press, Tucson, pp. 117-137.
[7]. Roedder, E. (1971). Fluid inclusion studies on the porphyry-type ore deposits at Bingham, Utah, Butte, Montana, and Climax, Colorado. Economic Geology 66, 98-118.
[8]. Nash, J.T. (1976). Fluid-inclusion petrology data from porphyry copper deposits and applications to exploration: a summary of new and published descriptions of fluid inclusions from 36 porphyry copper deposits and discussion of possible applications to exploration for copper deposits. US Govt. Print. Off.
[9]. Sillitoe, R. 1997. Characteristics and controls of the largest porphyry copper‐gold and epithermal gold deposits in the circum‐Pacific region. Australian Journal of Earth Sciences 44, 373-388.
[10]. Ulrich, T., Günther, D. and Heinrich, C.A. (2002). The evolution of a porphyry Cu-Au deposit, based on LA-ICP-MS analysis of fluid inclusions: Bajo de la Alumbrera, Argentina. Economic Geology 97, 1889-1920.
[11]. Asghari, O. and Hezarkhani, A. (2008). Appling discriminant analysis to separate the alteration zones within the Sungun porphyry copper deposit. Journal of Applied Sciences 24, 4472-4486.
[12]. Berger, B.R., Ayuso, R.A., Wynn, J.C. and Seal, R.R. (2008). Preliminary model of porphyry copper deposits. US geological survey open-file report 1321, 55.
[13]. Afzal, P., Alghalandis, Y.F., Moarefvand, P., Omran, N.R. and Haroni, H.A. (2012). Application of power-spectrum–volume fractal method for detecting hypogene, supergene enrichment, leached and barren zones in Kahang Cu porphyry deposit, Central Iran. Journal of Geochemical Exploration 112, 131-138.
[14]. Afzal, P., Madani, N., Shahbeik, S. and Yasrebi, A.B. (2015). Multi-Gaussian kriging: a practice to enhance delineation of mineralized zones by Concentration–Volume fractal model in Dardevey iron ore deposit, SE Iran. Journal of Geochemical Exploration 158, 10-21.
[15]. Emery, X. (2008). Uncertainty modeling and spatial prediction by multi-Gaussian kriging: accounting for an unknown mean value. Computers & Geosciences 34, 1431-1442.
[16]. Chiles, J.-P. and Delfiner, P. (2009). Geostatistics: modeling spatial uncertainty. John Wiley & Sons.
[17]. Rossi, M.E. and Deutsch, C.V. 2013. Mineral resource estimation. Springer Science & Business Media.
[18]. Cheng, Q., Xu, Y. and Grunsky, E. (2000). Integrated spatial and spectrum method for geochemical anomaly separation. Natural Resources Research 9. 43-52.
[19]. Zuo, R., Cheng, Q., Agterberg, F.P. and Xia, Q. (2009). Evaluation of the uncertainty in estimation of metal resources of skarn tin in Southern China. Ore Geology Reviews 35. 415-422.
[20]. Li, X.-l., Li, L.-h., Zhang, B.-l. and Guo, Q.-j. (2013). Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation. Neurocomputing 118: 179-190.
[21]. Wang, H. and Zuo, R. (2015). A comparative study of trend surface analysis and spectrum–area multifractal model to identify geochemical anomalies. Journal of Geochemical Exploration 155: 84-90.
[22]. Soltani, F.,  Moarefvand, P., Alinia, F. and Afzal, F. (2020). Detection of main rock type for Rare Earth Elements (REEs) mineralization using staged factor and fractal analysis in Gazestan iron-apatite deposit, central iran. Geopersia 10 (1): 89-99.
[23]. Mirzaie, M., Afzal, P. Adib, A., Rahimi, E. and Mohammadi, G. (2020). Detection of zones based on ore and gangue using fractal and multivariate analysis in Chah Gaz iron ore deposit, central iran. Journal of Mining and Environment 11 (2): 453-466.
[24]. Yasrebi, A.B. and Hezarkhani, A. (2019). Resources classification using fractal modelling in eastern Kahang Cu-Mo porphyry deposit, central iran. Iranian Journal of Earth Sciences 11 (1): 56-67.
[25]. Daneshvar Saein, L. (2017). Delineation of enriched zones of Mo, Cu and Re by concentration-volume fractal model in Nowchun Mo-Cu porphyry deposit, SE Iran. Iranian Journal of Earth Sciences 9 (1): 64-72.
[26]. Afzal, P. and Zahedi, P. (2018). determination of phosphorous and sulfur zonation using fractal modeling in jalal-abad iron ore, se iran. International Multidisciplinary Scientific GeoConference: SGEM 18 (1.3). 247-254.
[27]. Dutta, S., Misra, D., Ganguli, R., Samanta, B. and Bandopadhyay, S. (2006). A hybrid ensemble model of kriging and neural network for ore grade estimation. International Journal of Surface Mining, Reclamation and Environment 20. 33-45.
[28]. Samanta, B. and Bandopadhyay, S. (2009). Construction of a radial basis function network using an evolutionary algorithm for grade estimation in a placer gold deposit. Computers & Geosciences 35. 1592-1602.
[29]. Samanta, B. (2010). Radial basis function network for ore grade estimation. Natural resources research 19. 91-102.
[30]. Tahmasebi, P., Hezarkhani, A. (2012). A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Computers & geosciences 42. 18-27.
[31]. Reis, A., Sousa, A. and Fonseca, E.C. (2003). Application of geostatistical methods in gold geochemical anomalies identification (Montemor-O-Novo, Portugal). Journal of Geochemical Exploration 77. 45-63.
[32]. Subbey, S., Christie, M. and Sambridge, M. (2004). Prediction under uncertainty in reservoir modeling. Journal of Petroleum Science and Engineering 44. 143-153.
[33]. Berberian, M. and King, G. (1981). Towards a paleogeography and tectonic evolution of Iran. Canadian journal of earth sciences 18. 210-265.
[34]. Hezarkhani, A. (2008). Hydrothermal evolution of the Miduk porphyry copper system, Kerman, Iran: a fluid inclusion investigation. International Geology Review 50. 665-684.
[35]. Dai, F., Zhou, Q., Lv, Z., Wang, X. and Liu, G. (2014). Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecological Indicators 45. 184-194.
[36]. Isaaks, E., Srivastava, R. (1989). Applied geostatistics: An introduction. Applied geoestatistics: An introduction to.
[37]. Matheron, G. (1962). Traité de géostatistique appliquée. 1 (1962). Editions Technip.
[38]. Pokhrel, R.M., Kuwano, J. and Tachibana, S. (2013). A kriging method of interpolation used to map liquefaction potential over alluvial ground. Engineering geology 152. 26-37.
[39]. Vann, J. and Guibal, D. (1998). Beyond Ordinary Kriging–An overview of non-linear estimation, Proceedings of a one day symposium: Beyond Ordinary Kriging.
[40]. Hu, H. and Shu, H. (2015). An improved coarse-grained parallel algorithm for computational acceleration of ordinary Kriging interpolation. Computers & Geosciences 78. 44-52.
[41]. Daugman, J.G. (1988). Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression. IEEE Transactions on Acoustics, Speech, and Signal Processing 36. 1169-1179.
[42]. Alexandridis, A.K. and Zapranis, A.D. (2013). Wavelet neural networks: A practical guide. Neural Networks 42. 1-27.
[43]. Saljooghi, B.S. and Hezarkhani, A. (2014). Comparison of WAVENET and ANN for predicting the porosity obtained from well log data. Journal of Petroleum Science and Engineering 123. 172-182.
[44]. Saljooghi, B.S. and Hezarkhani, A. (2015). A new approach to improve permeability prediction of petroleum reservoirs using neural network adaptive wavelet (wavenet). Journal of Petroleum Science and Engineering 133. 851-861.
[45]. Zhang, Q. and Benveniste, A. (1992). Wavelet networks. IEEE transactions on Neural Networks 3. 889-898.
[46]. Cousineau, D. and Chartier, S. (2010). Outliers detection and treatment: a review. International Journal of Psychological Research 3.
[47]. Costa, J.F. (2003). Reducing the impact of outliers in ore reserves estimation. Mathematical geology 35. 323-345.
[48]. Schwertman, N.C., Owens, M.A. and Adnan, R. (2004). A simple more general boxplot method for identifying outliers. Computational statistics & data analysis 47. 165-174.
[49]. Daya, A.A. (2015). Comparative study of C–A, C–P, and N–S fractal methods for separating geochemical anomalies from background: A case study of Kamoshgaran region, northwest of Iran. Journal of Geochemical Exploration 150. 52-63.
[50]. Chaturvedi, D., Satsangi, P. and Kalra, P. (1996). Effect of different mappings and normalization of neural network models, Proceedings of the National Power Systems Conference. Indian Institute of Technology, pp. 377-386.
[51]. Sola, J., Sevilla, J. (1997). Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Transactions on Nuclear Science 44. 1464-1468.
[52]. Demuth, H. and Beale, M. (1992). Neural Network Toolbox. For Use with MATLAB. The MathWorks Inc 2000.
[53]. Kasiviswanathan, K., He, J., Sudheer, K. and Tay, J.-H. (2016). Potential application of wavelet neural network ensemble to forecast streamflow for flood management. Journal of Hydrology 536. 161-173.
[54]. Crochemore, L., Perrin, C., Andréassian, V., Ehret, U., Seibert, S.P., Grimaldi, S., Gupta, H., Paturel, J.-E. (2015). Comparing expert judgement and numerical criteria for hydrograph evaluation. Hydrological Sciences Journal 60. 402-423.
[55]. Sreekanth, P., Geethanjali, N., Sreedevi, P., Ahmed, S., Kumar, N.R. and Jayanthi, P.K. (2009). Forecasting groundwater level using artificial neural networks. Current science. 933-939.