عنوان مقاله [English]
In recent years, economic geology studies have become very popular method in mineral exploration studies. Modeling fluid inclusion data is one of the common studies in economic geology. In this research artificial neural networks method, as one of the machine learning algorithms, is used for three-dimensional modeling and application of the results of fluid inclusion analysis in Sungun porphyry copper deposit. For this purpose, fluid inclusion data is used for directly separation of related alteration zones with mineralization (Potassic, Phyllic and Potassic- Phyllic). Due to the relation that exists between alteration zones and mineralization areas, based on 173 fluid inclusion data the separation of alteration zones is modeled by artificial neural networks method in Sungun porphyry copper deposit. According to the validation studies, it can be concluded that precision of this model is appropriate (83%) and trained model could be used for separation of alteration zones in Sungun porphyry copper deposit.
Abbaszadeh, M., Hezarkhani, A. and Soltani-Mohammadi, S., 2013- An SVM based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit, Chem Erde-Geochem, vol. 73, p. 545- 554.
Abbaszadeh, M., Hezarkhani, A. and Soltani-Mohammadi, S., 2015- Classification of Alteration Zones Based on Whole- Rock Geochemical Data using Support Vector Machine. Journal of the Geological Society of India., vol. 85.
Aghazadeh, M., Z. Hou, Badrzadeh, Z. and Zhou, L., 2015- Temporal–spatial distribution and tectonic setting of porphyry copper deposits in Iran: Constraints from zircon U–Pb and molybdenite Re–Os geochronology, Ore Geology Reviews, vol. 70, p. 385- 406.
Asghari, O. and Hezarkhani, A., 2008- Applying discriminant analysis to separate the alteration zones within the Sungun porphyry copper deposit. Journal of Applied Sciences, vol. 24, p. 4472- 4486.
Bakhshandeh Amnieh, H., Siamaki, A. and Soltani, S., 2012- Design of blasting pattern in proportion to the peak particle velocity (PPV): Artificial neural networks approach, Safety Science, Vol. 50, p. 1913- 1916.
Bakker, R. J., 1999- Optimal Interpretation of Microthermometrical Data from Fluid Inclusions: Thermodynamic Modelling and Computer Programming. Ruprecht-Karls-University Heidelberg, Germany, 54 p.
Beane, R. E. and Bodnar, R. J., 1995- Hydrothermal fluids and hydrothermal alteration in porphyry copper deposits. In: Pierce, F.W., Bohm, J.G. (Eds.), Porphyry Copper Deposits of the American Cordillera: Tucson, AZAZ Geol. Soc. Dig., vol. 20. Arizona Geological Society, Arizona, United States, p. 83- 93 .
Beane, R. E. and Titley, S. R., 1981- Porphyry copper deposits, Part II: Hydrothermal alteration and mineralization: ECONOMIC GEOLOGY, 75TH ANNIV. p. 235- 269.
Calagari, A. A. and Hosseinzadeh, G., 2006- The mineralogy of copper-bearing skarn to the east of the Sungun-Chay river, East-Azarbaidjan, Iran, Journal of Asian Earth Sciences, Vol. 28, p. 423- 438.
Calagari, A. A., 1997- Geochemical, stable isotope, noble gas, and fluid inclusion studies of mineralization and alteration at Sungun porphyry copper deposit, East-Azarbaidjan, Iran: Implication for genesis. (Ph.D. thesis), Manchester University, Manchester, UK.
Calagari, A. A., 2003a- Concentration Variations of Major and Minor Elements Across Various Alteration Zones in Porphyry Copper Deposit at Sungun, East Azarbaidjan, Iran, Journal of Sciences Islamic Republic of Iran, Vol. 14, p. 27- 36.
Calagari, A. A., 2003b- Stable isotope (S, O, H and C) studies of the phyllic and potassic–phyllic alteration zones of the porphyry copper deposit at Sungun, East Azarbaidjan, Iran, Journal of Asian Earth Sciences, Vol. p. 767- 780.
Calagari, A. A., 2004a- Geology and fracture-related hypogene hydrothermal alteration and mineralization of porphyry copper deposit at Sungun, Iran, Journal of the Geological Society of India, Vol. 64, p. 595- 618.
Calagari, A. A., 2004b- Fluid inclusion studies in quartz veinlets in the porphyry copper deposit at Sungun, East-Azarbaidjan, Iran, Journal of Asian Earth Sciences, Vol. 23, p. 179- 189.
Canet, C., Franco, S. I., Prol-Ledesma, R. M., González-Partida, E. and Villanueva-Estrada, R. E., 2011- A model of boiling for fluid inclusion studies: application to the Bolaños Ag-Au-Pb-Zn epithermal deposit, Western Mexico, Journal of Geochemical Exploration, vol. 110, p. 118- 125.
Cardon, H. R. A. and Hoogstraten, R. V., 1995- Key Issues for Successful Neural Network Applications: An Application in Geology, Artificial Neural Network: An Introduction to ANN Theory and Practice, Braspenning, P.J., Thuijsman, F., and Weijters, A.J.M.M., p. 235- 245.
Cheng, B. and Titterington, D. M., 1994- Neural networks: A review from a statistical perspective, Statistical Science, vol. 9(1), pp. 2- 54.
Dutta, S., 2006- Predictive performance of machine learning algorithms for ore reserve estimation in sparse and imprecise data, PhD thesis, University of Alaska Fairbanks, p.189.
Foody, G. M., 1996- Relating the land-cover composition of mixed pixels to artificial neural network classification output, Photogrammetric Engineering and Remote Sensing,Vol. 62, p. 491- 499.
Hassani Pak, A. A., 2001, Mining Sampling (Exploration, Explotation & Mineral Processing), Tehran University Press, p. 523.
Haykin, S., 1999- Neural Networks – A Comprehensive Foundation. Prentice Hall, New Jersey.
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, vol. 93, p. 651- 670.
Hezarkhani, A., 1997- Physicochemical controls on alteration and copper mineralization in the Sungun porphyry copper system, Iran. (PhD), University of McGill, Montreal, Quebec, Canada.
Hezarkhani, A., 2006a- Hydrothermal evolutions at the Sar-Cheshmeh porphyry Cu–Mo deposit, Iran: evidence from fluid inclusions, Journal of Asian Earth Sciences, vol. 28, p. 408- 422.
Hezarkhani, A., 2006b- Mineralogy and fluid inclusion investigations in the Reagan Porphyry System, Iran, the Path to an uneconomic porphyry copper deposit, Journal of Asian Earth Sciences, vol. 27,p. 598- 612.
Hezarkhani, A., 2008- Hydrothermal Evolution in Miduk Porphyry Copper System (Kerman, Iran): Based on the Fluid Inclusion Investigation, Journal of IGR, Stanford, USA., vol. 50, p. 665- 684.
Hezarkhani, A., 2009- Hydrothermal fluid geochemistry at the Chah-Firuzeh porphyry copper deposit, Iran: Evidence from fluid inclusions, Journal of Geochemical Exploration, vol. 101, p. 254- 264.
Hezarkhani, A., Tahmasebi, T. and Asghari, O., 2010- Separating the Sungun copper deposit alteration zones by applying artificial neural network, journal of geosciences, vol.20, No.77, p. 41- 46.
Kotake, N., Suzuki, K., Asahi, S. and Kanda, Y., 2002- Experimental study on the grinding rate constant of solid materials in a ball mill, Powder Technol,Vol. 122, p. 101- 108.
Landtwing, M. R., Pettke, T., Halter, W. E., Heinrich. C. A., Redmond, P. B., T., E. M. and Kunze. K., 2005- Copper deposition during quartz dissolution by cooling magmatic-hydrothermal fluids: The Bingham porphyry. Earth and Planetary Science Letters, vol. 235, p. 229- 243.
Lee, C. and Sterling, R., 1992- Identifying probable failure modes for underground openings using a neural network, International Journal of Rock Mechanics and Mining Science and Geomechanics Abstracts, vol. 29(1), p. 49- 67.
Lescuyer, J. L., Riou, R., Babakhani, A., Alavi Tehrani, N., Nogol, M. A., Dido, J. and Gemain, Y. M., 1978- Geological map of the Ahar area: Geological Survey Of Iran.
Linderman, M., Liu, J., QI, J., An, L., Ouyang, Z., Yang, J. and Tan, Y., 2004- Using Artificial Neural Networks to Map the Spatial Distribution of Understorey Bamboo from Remote Sensing Data, Int. J. Remote Sensing, vol. 25, p. 1685- 1700.
Lowell, J. D. and Guilbert, J. M., 1970- Lateral and vertical alteration mineralization zoning in porphyry ore deposits. Economic Geology, vol. 65, p. 373- 408.
Mehrpartou, M., 1993- Contributions to the geology, geochemistry, ore genesis and fluid inclusion investigations on Sungun Cu-Mo porphyry deposit, (North-West of Iran), PhD Thesis, Hamburg University, Hamburg, Germany, 1- 245.
Menhaj, M. B., 2000- Fundamentals of Artificial Neural Networks, Tehran, Iran, Amirkabir University Press.
Miller, D. M., Kaminsky, E. J., and , and Rana, S., 1995- Neural Network Classification of Remote Sensing Data, Computers and Geosciences, vol. 21, p.377- 386.
Moritz, R., 2006- Fluid salinities obtained by infrared microthermometry of opaque minerals: Implications for ore deposit modeling - A note of caution, Journal of Geochemical Exploration, vol. 89, p. 284- 287.
Nayak, P. C., Rao, Y. R. S. and Sudheer, K. P., 2006- Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach, Water Resources Management, vol. 20, p.77- 90.
Rizzo, D. M. and Dougherty, D. E., 1994- Characterization of aquifer properties using artificial neural networks: Neural kriging, Water Resources Research, vol. 30, p. 483- 497.
Rusk, B. G., Reed, M. H., Dilles, J. H., Klemm, L. M. and Heinrich, C. A., 2004- Compositions of magmatic hydrothermal fluids determined LAICP- MS of fluid inclusions from the porphyry copper-molybdenum deposit at Butte, MT: Chemical Geology, vol. 210, p. 173- 199.
Shahin, M. A., Jaksa, M. B. and Maier, H. R., 2008- State of the art of artificial neural networks in geotechnical engineering, Electronic Journal of Geotechnical Engineering, vol. Special Volume Bouquet.
Simmonds, V., Moazzen, M. and Mathur, R., 2017- Constraining the timing of porphyry mineralization in northwest Iran in relation to Lesser Caucasus and Central Iran; Re –Os age data for Sungun porphyry Cu–Mo deposit, International Geology Review, vol. 59, p. 1561- 1574.
Singh, V., Banerjee, P. K., Tripathy, S. K., Saxena, V. K. and Venugopal, R., 2013- Artificial Neural Network Modeling of Ball Mill Grinding Process, Powder Metallurgy and Mining, vol. 2.
Soltani, S., Bakhshandeh Amnieh, H. and Bahadori, M., 2012- Investigating Ground Vibration to Calculate the Permissible Charge Weight for Blasting Operations of Gotvand-Olya Dam Underground Structures, Archieves of Mining Science, vol. 56, p. 701- 710.
Sutherland, B. A., and Cathro, R. J., 1976- A perspective of porphyry deposits: Porphyry deposits of the Canadian Cordillera, special vol, 15, p. 7- 15.
Tahmasebi, P. and Hezarkhani, A., 2009- Application of Discriminant and Principal Components Analysis for Alteration Separation; Sungun Copper Porphyry Deposit, East Azerbaijan, Iran, Australian Journal of Basic and Applied Sciences, vol. 6, p. 564- 576.
Thiery, R., 2006- Thermodynamic modelling of aqueous CH4-bearing fluid inclusions trapped in hydrocarbon-rich environments, Chemical Geology, vol. 227, p. 154- 164.
Wang, Y. G. and Li, H., P., 2010- Remote sensing image classification based on artificial neural network: A case study of Honghe Wetlands National Nature Reserve, International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE), p. 17- 20.
Zhang, D., Xu, G., Zhang, W., and Golding, S. D., 2007- High salinity fluid inclusions in the Yinshan polymetallic deposit from the Le–De metallogenic belt in Jiangxi Province, China: Their origin and implications for ore genesis. Ore Geology Reviews, vol. 31, p. 247- 260.
Zhang, G. P., Patuwo, B. E. and Hu, M. Y., 1998- Forecasting with artificial neural networks: The state of the art,. International Journal of Forecasting, vol. 1, p. 35- 62.
Zhao, K. and Chen, S., 2011- Study on artificial neural network method for ground subsidence prediction of metal mine, The Second International Conference on Mining Engineering and Metallurgical Technology, Procedia Earth and Planetary Science, vol. 2, p. 177- 182.