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Regional Geochemical Exploration for Cu-Au Deposit Based on Self-Organizing Map (SOM) in Valezir Area, Meshginshahr, NW of Iran | ||
| نشریه مهندسی منابع معدنی | ||
| مقاله 2، دوره 6، شماره 1 - شماره پیاپی 19، فروردین 1400، صفحه 11-25 اصل مقاله (11.59 M) | ||
| نوع مقاله: علمی-پژوهشی | ||
| شناسه دیجیتال (DOI): 10.30479/jmre.2020.12474.1370 | ||
| نویسندگان | ||
| A. Nasseri* 1؛ P. Mohebbi2؛ Kh. Allahyari3؛ A. Davatgar4 | ||
| 1Assistant Professor, Dept. of Mining Engineering, Ahar Branch, Islamic .Azad University, Ahar, Iran | ||
| 2Assistant Professor, Dept. of Mining Engineering, Sahand University of Technology, Tabriz, Iran. | ||
| 3M.Sc Student, Dept. of Mining Engineering, Sahand University of Technology, Tabriz, Iran. | ||
| 4M.Sc, Dept. of Mining Engineering, Sahand University of Technology, Tabriz, Iran. | ||
| تاریخ دریافت: 28 دی 1398، تاریخ بازنگری: 07 تیر 1399، تاریخ پذیرش: 12 اسفند 1398 | ||
| چکیده | ||
| Since most of the geochemical data analysis procedures require preliminary assumptions that lead to constraint and errors in the true nature of the data causing reduction in the effectiveness of the adopted methods. Therefore, a model-based clustering method as self-organizing map (SOM) were employed with the aim of recognizing Cu-Au mineralized zones by establishing an optimized exploration tool in Valezir area, NW of Iran. SOM as a dimension reduction method was introduced to recognize geochemical distribution patterns of Cu-Au with higher certainty while preserving the originality of the data. Subsequent to data preprocessing and testing different SOM architectures, an appropriate structure with a pattern containing six clusters was selected. Accordingly, the related elements distribution model was extracted and interpretation of the geochemical system represents two significant sets of elements in clusters (i.e. 1st, 2nd and 6th clusters) to anticipate the mechanism of distribution: 1- Copper and pertaining trace elements formation from intermediate to acidic hydrothermal solutions, which are localized in the northern part of the area and emplaced in the quartz monzo-dioritd intrusive body. 2- Au Anomalies and its associated elements As, Hg and Bi depicted in 2nd cluster. The Au anomalies follow geochemical pattern with Bi, Sb, As, and W that are mostly elongated from NW to SW of the area. It seems relatively the low enrichment of gold has occurred during the intrusion of the igneous body into older volcanic units that caused extensive alterations, remobilization and localization of Au and related elements. To assess the SOM results, a comparative study was carried out with the results obtained from hierarchical clustering analysis (HCA). The results illustrated higher performance by SOM approach in characterizing geochemical system and detecting the elements paragenetic sequence in the area for locating the exploration targets. | ||
| کلیدواژه‌ها | ||
| Geochemical system modeling؛ Pattern recognition؛ Self-Organizing Maps (SOMs)؛ Valezir area؛ NW of Iran | ||
| عنوان مقاله [English] | ||
| Regional Geochemical Exploration for Cu-Au Deposit Based on Self-Organizing Map (SOM) in Valezir Area, Meshginshahr, NW of Iran | ||
| نویسندگان [English] | ||
| A. Nasseri1؛ P. Mohebbi2؛ Kh. Allahyari3؛ A. Davatgar4 | ||
| 1Assistant Professor, Dept. of Mining Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran. | ||
| 2Assistant Professor, Dept. of Mining Engineering, Sahand University of Technology, Tabriz, Iran. | ||
| 3M.Sc Student, Dept. of Mining Engineering, Sahand University of Technology, Tabriz, Iran. | ||
| 4M.Sc, Dept. of Mining Engineering, Sahand University of Technology, Tabriz, Iran. | ||
| چکیده [English] | ||
| Since most of the geochemical data analysis procedures require preliminary assumptions that lead to constraint and errors in the true nature of the data causing reduction in the effectiveness of the adopted methods. Therefore, a model-based clustering method as self-organizing map (SOM) were employed with the aim of recognizing Cu-Au mineralized zones by establishing an optimized exploration tool in Valezir area, NW of Iran. SOM as a dimension reduction method was introduced to recognize geochemical distribution patterns of Cu-Au with higher certainty while preserving the originality of the data. Subsequent to data preprocessing and testing different SOM architectures, an appropriate structure with a pattern containing six clusters was selected. Accordingly, the related elements distribution model was extracted and interpretation of the geochemical system represents two significant sets of elements in clusters (i.e. 1st, 2nd and 6th clusters) to anticipate the mechanism of distribution: 1- Copper and pertaining trace elements formation from intermediate to acidic hydrothermal solutions, which are localized in the northern part of the area and emplaced in the quartz monzo-dioritd intrusive body. 2- Au Anomalies and its associated elements As, Hg and Bi depicted in 2nd cluster. The Au anomalies follow geochemical pattern with Bi, Sb, As, and W that are mostly elongated from NW to SW of the area. It seems relatively the low enrichment of gold has occurred during the intrusion of the igneous body into older volcanic units that caused extensive alterations, remobilization and localization of Au and related elements. To assess the SOM results, a comparative study was carried out with the results obtained from hierarchical clustering analysis (HCA). The results illustrated higher performance by SOM approach in characterizing geochemical system and detecting the elements paragenetic sequence in the area for locating the exploration targets. | ||
| کلیدواژه‌ها [English] | ||
| Geochemical system modeling, Pattern recognition, Self-Organizing Maps (SOMs), Valezir area, NW of Iran | ||
| مراجع | ||
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[1] Butt, C. (2005). “Geochemical dispersion, process and exploration models”. Regolith Expression of Australian Ore Systems, 81-106. [2] Grunsky, E. C. (2010). “The interpretation of geochemical survey data”. Geochemistry: Exploration, Environment, Analysis, 10: 27-74. [3] Brehme, M., Bauer, K., Nukman, M., and Regenspurg, S. (2017). “Self-organizing maps in geothermal exploration–A new approach for understanding geochemical processes and fluid evolution”. Journal of Volcanology and Geothermal Research, 336: 19-32. [4] Friedman, N., Geiger, D., and Goldszmidt, M. (1997). “Bayesian Network Classifiers”. Machine Learning, 29: 131-163. [5] Porwal, A., Carranza, E., and Hale, M. (2003). “Artificial neural networks for mineral-potential mapping: a case study from Aravalli Province, Western India”. Natural Resources Research, 12: 155-171. [6] Templ, M., Filzmoser, P., and Reimann, C. (2008). “Cluster analysis applied to regional geochemical data: problems and possibilities”. Applied Geochemistry, 23(8): 2198-2213. [7] Sharma, S., and Sharma, S. (1996). “Applied multivariate techniques”. New York: John Wiley, pp. 512. [8] Reimann, C., Filzmoser, P., and Garrett, R. G. (2002). “Factor analysis applied to regional geochemical data: problems and possibilities”. Applied Geochemistry, 17(3): 185-206. [9] Purwar, S., Jablonowski, C. J., and Nguyen, Q. P. (2011). “Development optimization using reservoir response surfaces: Methods for integrating facility and operational option”. Natural Resources Research, 20(1): 1-9. [10] Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., and Williamson, R. C., (2001). “Estimating the support of a high-dimensional distribution”. Neural Computation, 13: 1443-1471. [11] Zuo, R., and Carranza, E. J. M. (2011). “Support vector machine: a tool for mapping mineral prospectivity”. Computers & Geosciences, 37: 1967-1975. [12] David, M., and Woussen, G. (1973). “Correspondence analysis, a new tool for geologists”. Proceeding of Mining Příbram Symposium, 1: 41-65. [13] Cazes, P. (1970). “Application de l’analyse des données au traitement de problèmes géologiques”. Th`ese de 3`eme cycle, Facult´e des Sciences de Paris. [14] Lindqvist, L., Lundholm, I., Nisca, D., Esbensen, K., and Wold, S. (1987). “Multivariate geochemical modelling and integration with petrophysical data”. Journal of Geochemical Exploration, 29(1-3): 279-294. [15] Javid, F., Mohammadzadeh, M. J., and Nasseri, A. (2015). “Optimization of Geochemical Patterns Based on Multivariate Methods in the Duzduzan Area, E-Azerbaijan”. Proceedings of the 24th international Mining congress of Turey(IMCET), APRIL, Antalya/TURKEY, 360-369. [16] Nasseri, A., Mohammadzadeh, M. J., and Raeisi, S. H. T. (2015). “Fracture enhancement based on artificial ants and fuzzy c-means clustering (FCMC) in Dezful Embayment of Iran”. Journal of Geophysics and Engineering, 12: 227-241. [17] Kohonen, T., Kaski, S., and Lappalainen, H. (1997). “Self-organized formation of various invariant feature fiters in the adaptive-subspace SOM”. Neural Computation, 9: 1321-1344. [18] Kohonen, T. (2001). “Self-Organizing Maps”. Springer series in Information Sciences, New York, Springer-Verlag, 30: 501. [19] Schatzmann, J., and Ghanem, M. (2003). “Using self-organizing maps to visualize clusters and trends in multidimensional datasets”. Department of Computing Data Mining Group, Imperial College, London, 27-32 [20] Vesanto, J., and Alhoniemi, E. (2000). “Clustering of the self-organizing map”. Neural Networks, IEEE Transactions on, 11(3): 586-600. [21] Kaski, S. (1997). “Data exploration using self-organizing maps. Paper read at Acta Polytechnica Scandinavica: Mathematics”. Computing and Management in Engineering Series, 82: 20-30. [22] Pastukhov, A. A., and Prokofiev, A. A. (2016). “Kohonen self-organizing map application to representative sample formation in the training of the multilayer perceptron”. Petersburg Polytechnical University Journal: Physics and Mathematics, 2: 134-143. [23] Bação, F., Lobo, V., and Painho, M. (2005). “The self-organizing map, the Geo-SOM, and relevant variants for geosciences”. Computers & Geosciences, 31(2): 155-163. [24] Kalteh, A. M., Hjorth, P., and Berndtsson, R. (2008). “Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application”. Environmental Modelling & Software, 23(7): 835-845. [25] Kalteh, A. M., and Berndtsson, R. (2007). “Interpolating monthly precipitation by self-organizing map (SOM) and multilayer perceptron (MLP)”. Hydrological Sciences Journal, 52(2): 305-317. [26] Liu, Y., and Weisberg, R. H. (2011). “A review of self-organizing map applications in meteorology and oceanography. Self-Organizing Maps”. Applications and Novel Algorithm Design, 253-272. [27] Nourani, V., Baghanam, A. H., Adamowski, J., and Gebremichael, M. (2013). “Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling”. Journal of Hydrology, 476: 228-243. [28] Hsu, K. l., Gupta, H. V., Gao, X., Sorooshian, S., and Imam, B. (2002). “Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis”. Water Resources Research, 38(12): 1-17. [29] Chon, T.-S. (2011). “Self-organizing maps applied to ecological sciences”. Ecological Informatics, 6(1): 50-61. [30] Park, Y.-S., Tison, J., Lek, S., Giraudel, J.-L., Coste, M., and Delmas, F. (2006). “Application of a self-organizing map to select representative species in multivariate analysis: a case study determining diatom distribution patterns across France”. Ecological Informatics, 1(3): 247-257. [31] Peeters, L., Bação, F., Lobo, V., and Dassargues, A. (2007). “Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen’s self-organizing map”. Hydrology and Earth System Sciences Discussions, 11(4): 1309-1321. [32] Lin, G. F., and Chen, L. H. (2005). “Time series forecasting by combining the radial basis function network and the self-organizing map”. Hydrological Processes, 19(10): 1925-1937. [33] Strecker, U., and Uden, R. (2002). “Data mining of 3D poststack seismic attribute volumes using Kohonen self-organizing maps”. The Leading Edge, 21(10): 1032-1037. [34] Baldwin, J. L., Bateman, R. M., and Wheatley, C. L. (1990). “Application of a neural network to the problem of mineral identification from well logs”. The Log Analyst, 31(05): 279-293. [35] Nasseri, A., and Mohammadzadeh, M. J. (2017). “Evaluating distribution pattern of petrophysical properties and their monitoring under a hybrid intelligent based method in southwest oil field of Iran” Arabian Journal of Geosciences, 10(9): 8-15. [36] Herbst, M., Gupta, H., and Casper, M. (2009). “Mapping model behavior using self-organizing maps”. Hydrology and Earth System Sciences, 13(3): 395-409. [37] Ley, R., Casper, M., Hellebrand, H., and Merz, R. (2011). “Catchment classification by runoff behaviour with self-organizing maps (SOM)”. Hydrology and Earth System Sciences, 15(9): 2947-2962. [38] Céréghino, R., Giraudel, J., and Compin, A. (2001). “Spatial analysis of stream invertebrates distribution in the Adour-Garonne drainage basin (France), using Kohonen self organizing maps”. Ecological Modelling, 146(1-3): 167-180. [39] Lee, S., and Lathrop, R. G. (2006). “Subpixel analysis of Landsat ETM/sup+/using self-organizing map (SOM) neural networks for urban land cover characterization”. IEEE Transactions on Geoscience and Remote Sensing, 44(6): 1642-1654. [40] Martínez, P., Gualtieri, J., Aguilar, P., Pérez, R., Linaje, M., Preciado, J., and Plaza, A. (2001).“Hyperspectral image classification using a self-organizing map”. Summaries of the X JPL Airborne Earth Science Workshop. [41] Matteoli, S., Diani, M., and Corsini, G. (2010). “A tutorial overview of anomaly detection in hyperspectral images”. IEEE Aerospace and Electronic Systems Magazine, 25(7): 5-28. [42] Neagoe, V.-E., and Ropot, A.-D. (2002). “Concurrent self-organizing maps for pattern classification”. Proceedings First IEEE International Conference on Cognitive Informatics, IEEE, 304-312. [43] Patil, J. K., and Kumar, R. (2011). “Advances in image processing for detection of plant diseases”. Journal of Advanced Bioinformatics Applications and Research, 2(2): 135-141. [44] Tasdemir, K., and Merényi, E. (2009). “Exploiting data topology in visualization and clustering of self-organizing maps”. IEEE Transactions on Neural Networks, 20(4): 549-562. [45] Toivanen, P. J., Ansamäki, J., Parkkinen, J., and Mielikäinen, J. (2003). “Edge detection in multispectral images using the self-organizing map”. Pattern Recognition Letters, 24(16): 2987-2994. [46] Villmann, T., Merényi, E., and Hammer, B. (2003). “Neural maps in remote sensing image analysis”. Neural Networks, 16(3-4): 389-403. [47] Carneiro, C. d. C., Fraser, S. J., Crósta, A. P., Silva, A. M., and Barros, C. E. d. M. (2012). “Semiautomated geologic mapping using self-organizing maps and airborne geophysics in the Brazilian Amazon”. Geophysics, 77: K17-K24. [48] Fraser, S., and Dickson, B. (2007). “A new method for data integration and integrated data interpretation: self-organizing maps”. Proceedings of Exploration, 7: 907-910. [49] Fraser, S., and Dickson, B. (2006). “Data mining geoscientific data sets using self organizing maps”. Mastering the Data Explosion in the Earth and Environmental Sciences, Extended Abstracts, 5-7. [50] Marroquín, I. D., Brault, J.-J., and Hart, B. S. (2008). “A visual data-mining methodology for seismic facies analysis: Part 1—Testing and comparison with other unsupervised clustering methods”. Geophysics, 74: P1-P11. [51] Cracknell, M., Reading, A. M., and De Caritat, P. (2015). “Multiple influences on regolith characteristics from continental-scale geophysical and mineralogical remote sensing data using Self-Organizing Maps”. Remote Sensing of Environment, 165: 86-99. [52] Cracknell, M., Reading, A., and McNeill, A. (2014). “Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer–Mt Charter region, Tasmania, using Random Forests™ and Self-Organising Maps”. Australian Journal of Earth Sciences, 61: 287-304. [53] Sun, X., Deng, J., Gong, Q., Wang, Q., Yang, L., and Zhao, Z. (2009). “Kohonen neural network and factor analysis based approach to geochemical data pattern recognition”. Journal of Geochemical Exploration 103: 6-16. [54] Aghanabati, A. (2004). “Geology of Iran”. Geological Survey of Iran, pp. 586. (In Persian) [55] Lescuyer, J. L., and Riou, R. (1976). “Geologie de la region de Mianeh ( Azerbaijan)”. Contribution al’ etude da Volcanism tertiare de L’Iran, Thesis, Grenoble University Grenoble, France, pp. 232. [56] Didon, J., and Gemaime,Y. M. (1976). “Le Sabalan Volkan plioquartermair del Azerbaijan oriental (Iran), Etude qeologique et petrographique del edfic et de son environmental regional thesis docteur du 3c cycle”. Unviversity Grenoble, France, pp. 304. [57] Geological Survey and Mineral Exploration of Iran, (2008). Geochemical exploration report of Meshginshahr (2)1:25000 sheet, Kan Azin engineering company, pp. 300. [58] Riou, R., Dupuy, C., and Dostal, J. (1981). “Geochemistry of coexisting alkaline and calc-alkaline volcanic rocks from northern Azerbaijan (NW Iran)”. Journal of Volcanology and Geothermal Research, 11(2-4): 253-275. [59] Vesanto, J., Himberg, J., Alhoniemi, E., and Parhankangas, J. (2000). SOM toolbox for Matlab 5: Citeseer. [60] Romesburg, C. (2004). “Cluster analysis for researchers”. Lulu. com. [61] Ultsch, A., and Herrmann, L. (2005). “The architecture of emergent self-organizing maps to reduce projection errors”. Esann, 1-6. [62] Klose, C. D. (2006). “Self-organizing maps for geoscientific data analysis: geological interpretation of multidimensional geophysical data”. Computational Geosciences, 10: 265-277. [63] Nasseri, A., Mohammadzadeh, M. J., Mohebbi, P., and Javani, P. (2015). “Negative Geochemical anomalies and their importance in regional exploration, Gharachaman-Duzduzan”. Geosciences Scientific Quarterly Journal, 24(94): 383- 392. [64] Žibret, G., and Šajn, R. (2010). “Hunting for geochemical associations of elements: factor analysis and self-organising maps”. Mathematical Geosciences, 42: 681-703. [65] Carranza, E. J. M. (2008). “Geochemical anomaly and mineral prospectivity mapping in GIS”. Handbook of Exploration and Environmental Geochemistry, Elsevier, Amsterdam, 11: 78-114. [66] Mohammadzadeh, M. J., Nasseri, A., and Mahmoudian, O. (2010). “Comparative studies of Lithological Unit Discrimination Method and Fuzzy C-Mean Clustering (FCMC) on eliminating Syngenetic Component of Stream Sediments in Regional Geochemical Exploration of Gharehchaman area, E- Azerbaijan”. Iranian Journal of Mining Engineering (IRJME), 4(8):51-58.
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