دانشگاه بین المللی امام خمینی
معاونت پژوهشی و فناوری
نشریات دانشگاه در پرتال وزارت علوم، تحقیقات و فناوری

  اخبار و اعلانات

 آمار

تعداد نشریات20
تعداد شماره‌ها370
تعداد مقالات3,040
تعداد مشاهده مقاله4,094,021
تعداد دریافت فایل اصل مقاله2,731,661
محمد زاده, محمد جعفر, رجایی, محمد مهدی. (1403). شناسایی مناطق امیدبخش کانی‌زایی طلای زایلیک شمال غرب ایران با روش برهم‌نهی فازی اطلاعات. لسان مبین, 9(3), 1-22. doi: 10.30479/jmre.2023.18495.1631
محمد جعفر محمد زاده; محمد مهدی رجایی. "شناسایی مناطق امیدبخش کانی‌زایی طلای زایلیک شمال غرب ایران با روش برهم‌نهی فازی اطلاعات". لسان مبین, 9, 3, 1403, 1-22. doi: 10.30479/jmre.2023.18495.1631
محمد زاده, محمد جعفر, رجایی, محمد مهدی. (1403). 'شناسایی مناطق امیدبخش کانی‌زایی طلای زایلیک شمال غرب ایران با روش برهم‌نهی فازی اطلاعات', لسان مبین, 9(3), pp. 1-22. doi: 10.30479/jmre.2023.18495.1631
محمد زاده, محمد جعفر, رجایی, محمد مهدی. شناسایی مناطق امیدبخش کانی‌زایی طلای زایلیک شمال غرب ایران با روش برهم‌نهی فازی اطلاعات. لسان مبین, 1403; 9(3): 1-22. doi: 10.30479/jmre.2023.18495.1631

شناسایی مناطق امیدبخش کانی‌زایی طلای زایلیک شمال غرب ایران با روش برهم‌نهی فازی اطلاعات

نشریه مهندسی منابع معدنی
مقاله 1، دوره 9، شماره 3 - شماره پیاپی 33، شهریور 1403، صفحه 1-22    اصل مقاله (2.77 M)
نوع مقاله: علمی-پژوهشی
شناسه دیجیتال (DOI): 10.30479/jmre.2023.18495.1631
نویسندگان
محمد جعفر محمد زاده* 1؛ محمد مهدی رجایی2
1دانشیار، دانشکده مهندسی معدن، دانشگاه صنعتی سهند، تبریز
2دانشجوی دکتری ، دانشکده مهندسی معدن، دانشگاه صنعتی سهند، تبریز
تاریخ دریافت: 11 اسفند 1401،  تاریخ بازنگری: 21 خرداد 1402،  تاریخ پذیرش: 01 مرداد 1402 
چکیده
هدف از این پژوهش، استفاده هم‌زمان از عیار طلای به ‌دست‌ آمده از مدل‌سازی‌های ژئوشیمیایی و پارامتر‌های زمین‌شناسی، جهت شناسایی مناطق امیدبخش کانی‌زایی طلای اپی‌ترمال منطقه زایلیک در شمال‌غرب ایران است. شواهد زمین‌شناسی مورد استفاده در این منطقه، سنگ‌شناسی و دگرسانی‌های آرژیلیکی، پروپیلیتیکی، سیلیسی و اکسید آهن بوده و در مدل‌سازی‌های ژئوشیمیایی نیز از دو روش هوش مصنوعی 1) شبکه عصبی مصنوعی و 2) تلفیق آن با الگوریتم کرم شب‌تاب استفاده شد. شواهد زمین‌شناسی پس از کمّی شدن، به همراه مقادیر تخمین زده شده طلا در روش‌های هوش مصنوعی، برای وزن‌دهی به سیستم سلسله‌ مراتبی در نرم‌افزار Expert Choise وارد شدند. در این نرم‌افزار وزن‌دهی و تعیین درجه اهمیت نسبی پارامترهای زمین‌شناسی پس از مشورت با متخصصان زمین‌شناسی و اکتشاف صورت پذیرفته و روش‌های هوش مصنوعی نیز با استفاده از معیارهای کمّی مانند ضریب تعیین و تابع جذر میانگین مربعات خطا با یکدیگر مقایسه شدند که روش تلفیقی شبکه عصبی مصنوعی با الگوریتم کرم شب‌تاب، با توجه ‌به بیشتر بودن ضریب تعیین (R2=0.643) و کمتر بودن تابع خطا (RMSE=0.754)، نتایج بهتری را نشان داد، بنابراین از درجه اهمیت بیشتر، جهت تشخیص مناطق امید‌بخش کانی‌زایی برخوردار شد. در نهایت تمامی پارامترهای یاد شده در نرم‌افزار Arc GIS به وسیله روش برهم‌نهی فازی با یکدیگر تلفیق شده و مناطق بهینه اکتشافی در شمال و شمال‌شرق منطقه ثبت و ادامه اکتشاف ریشه کانی‌زایی طلا با توجه ‌به مدل معرفی شده در مناطق همجوار میسر شد.
کلیدواژه‌ها
شبکه عصبی مصنوعی؛ الگوریتم کرم شب‌تاب؛ سلسله‌مراتبی؛ برهم‌نهی فازی؛ طلای زایلیک
عنوان مقاله [English]
Identifying the Promising Areas of Zailik Gold Mineralization in the Northwest of Iran Using Fuzzy Overlay of Information Method
نویسندگان [English]
M.J. Mohammadzadeh1؛ M.M. Rajaei2
1Associate Professor, Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran
2Ph.D student, Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran
چکیده [English]
This research aims to simultaneously use geochemical modeling and geological parameters for gold grade estimation to identify promising zones of epithermal gold mineralization in the Zailik region, northwest of Iran. For this purpose, the employed geological evidence includes lithology and alterations like silicification, iron oxides, phyllic, and propylitic. For  geochemical modeling two methods were utulized: 1) artificial neural network (ANN),  2) integrating ANN with the Firefly algorithm. Geological evidence after quantification, along with the estimated amounts of gold in artificial intelligence methods, was entered into the hierarchical system in Expert Choice software for weighting. In this method, the weighting and determination of the degree of relative importance of geological parameters were attempted after consulting geological and exploration experts. Subsequently, artificial intelligence methods were also compared with each other using quantitative criteria such as the coefficient of determination and the root mean square error function. The results showed that the combined method of artificial neural networks with the Firefly algorithm provides better results due to the higher coefficient of determination (R2=0.643) and lower error function (RMSE=0.754). Therefore, it has a higher degree of importance to identify promising areas for mineralization. Finally, all the above parameters were combined with each other in the Arc GIS software using the fuzzy overlay method, and the optimal exploration targets were detected in the north and northeast of the region, enabling to continue the exploration targets along the root of gold mineralization in the neighboring areas according to the introduced model.
کلیدواژه‌ها [English]
Artificial neural network, Firefly algorithm, AHP, Fuzzy overlay, Zailik gold
سایر فایل های مرتبط با مقاله
مراجع
  1. Parsa, M., Carranza, E. J. M., and Ahmadi, B. (2022). “Deep GMDH Neural Networks for Predictive Mapping of Mineral Prospectivity in Terrains Hosting Few but Large Mineral Deposits”. Natural Resources Research, 31(1): 37-50.
  2. Ehteram, M., Khozani, Z. S., Soltani-Mohammadi, S., and Abbaszadeh, M. (2023). “The Necessity of Grade Estimation”. In: Estimating Ore Grade Using Evolutionary Machine Learning Models, Singapore: Springer Nature Singapore, 1-6.
  3. Dumakor-Dupey, N. K., and Arya, S. (2021). “Machine Learning—A Review of Applications in Mineral Resource Estimation”. Energies, 14(14): 4079.
  4. Ahmadi, R., and Sadat Koodehi, S. M. (2018). “Classification and reserve estimation of Robat Arregije Pb-Zn deposit, Khomein Township, Markazi Province, using geostatistical methods”. New Findings in Applied Geology, 12(24): 39-53.
  5. Bastante, F., Ordóñez, C., Taboada, J., and Matías, J. (2008). “Comparison of indicator kriging, conditional indicator simulation and multiple-point statistics used to model slate deposits”. Engineering Geology, 98(1-2): 50-59.
  6. Pardo-Igúzquiza, E., Dowd, P. A., Baltuille, J., and Chica-Olmo, M. (2013). “Geostatistical modelling of a coal seam for resource risk assessment”. International Journal of Coal Geology, 112: 134-140.
  7. Wu, X., and Zhou, Y. (1993). “Reserve estimation using neural network techniques”. Computers & Geosciences, 19(4): 567-575.
  8. Jafrasteh, B., Fathianpour, N., and Suárez, A. (2018). “Comparison of machine learning methods for copper ore grade estimation”. Computational Geosciences, 22: 1371-1388.
  9. Chudasama, B. (2022). “Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations”. MethodsX, 9: 101629.
  10. Ziaii, M., Doulati Ardejani, F., Ziaei, M., and Soleymani, A. A. (2012). “Neuro-fuzzy modeling based genetic algorithms for identification of geochemical anomalies in mining geochemistry”. Applied Geochemistry, 27(3): 663-676.
  11. Tenorio, V. O., Bandopadhyay, S., Misra, D., Naidu, S., and Kelley, J. (2015). “Support vector machines applied for resource estimation of underwater glacier-type platinum deposits”. Application of Computers and Operations Research in the Mineral Industry, 889-902.
  12. Mahmoudabadi, H., Izadi, M., and Menhaj, M. B. (2009). “A hybrid method for grade estimation using genetic algorithm and neural networks”. Computational Geosciences, 13: 91-101.
  13. Moeini, H., and Torab, F. M. (2017). “Comparing compositional multivariate outliers with autoencoder networks in anomaly detection at Hamich exploration area, east of Iran”. Journal of Geochemical Exploration, 180: 15-23.
  14. Soltani-Mohammadi, S., Hoseinian, F. S., Abbaszadeh, M., and Khodadadzadeh, M. (2022). “Grade estimation using a hybrid method of back-propagation artificial neural network and particle swarm optimization with integrated samples coordinate and local variability”. Computers & Geosciences, 159: 104981.
  15. Sun, T., Li, H., Wu, K., Chen, F., Zhu, Z., and Hu, Z. (2020). “Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China”. Minerals, 10(2): 102.
  16. Ghezelbash, R., Maghsoudi, A., and Carranza, E. J. M. (2020). “Sensitivity analysis of prospectivity modeling to evidence maps: Enhancing success of targeting for epithermal gold, Takab district, NW Iran”. Ore Geology Reviews, 120: 103394.
  17. Bazdar, H., Fattahi, H., and Ghadimi, F. (2015). “Hybrid ANN with Invasive Weed Optimization Algorithm, a New Technique for Prediction of Gold and Silver in Zarshuran Gold Deposit, Iran”. Journal of Tethys, 3(3): 273-286.
  18. Fattahi, H., and Ghadimi, F. (2016). “A hybrid artificial neural network with particle swarm optimization for estimation of heavy metals of rainwater in the industrial region-a case study”. Journal of Tethys, 4(2): 154-168.
  19. Tahmasebi, P., and Hezarkhani, A. (2012). “A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation”. Computers & Geosciences, 42: 18-27.
  20. Ghezelbash, R., Maghsoudi, A., and Carranza, E. J. M. (2020). “Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm”. Computers & Geosciences, 134: 104335.
  21. Chen, Y., and An, A. (2016). “Application of ant colony algorithm to geochemical anomaly detection”. Journal of Geochemical Exploration, 164: 75-85.
  22. Gu, Y., Bao, Z., Song, X., Patil, S., and Ling, K. (2019). “Complex lithology prediction using probabilistic neural network improved by continuous restricted Boltzmann machine and particle swarm optimization”. Journal of Petroleum Science and Engineering, 179: 966-978.
  23. Roshanravan, B., Aghajani, H., Yousefi, M., and Kreuzer, O. (2019). “Particle swarm optimization algorithm for neuro-fuzzy prospectivity analysis using continuously weighted spatial exploration data”. Natural Resources Research, 28(2): 309-325.
  24. Ghezelbash, R., Daviran, M., Maghsoudi, A., Ghaeminejad, H., and Niknezhad, M. (2023). “Incorporating the genetic and firefly optimization algorithms into K-means clustering method for detection of porphyry and skarn Cu-related geochemical footprints in Baft district, Kerman, Iran”. Applied Geochemistry, 148: 105538.
  25. Fister, I., Fister Jr, I., Yang, X.-S., and Brest, J. (2013). “A comprehensive review of firefly algorithms”. Swarm and Evolutionary Computation, 13: 34-46.
  26. Das, S., Maity, S., Qu, B.-Y., and Suganthan, P. N. (2011). “Real-parameter evolutionary multimodal optimization—A survey of the state-of-the-art”. Swarm and Evolutionary Computation, 1(2): 71-88.
  27. Yang, X.-S. (2010). “Nature-inspired metaheuristic algorithms”. Luniver Press, pp. 148.
  28. Zhang, Y., and Wu, L. (2012). “A novel method for rigid image registration based on firefly algorithm”. International Journal of Research and Reviews in Soft and Intelligent Computing (IJRRSIC), 2(2): 141-146.
  29. Dutta, R., Ganguli, R., and Mani, V. (2011). “Exploring isospectral spring–mass systems with firefly algorithm”. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 467(2135): 3222-3240.
  30. Apostolopoulos, T., and Vlachos, A. (2011). “Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem”. International Journal of Combinatorics, 2011: 523806.
  31. Horng, M.-H., and Liou, R.-J. (2011). “Multilevel minimum cross entropy threshold selection based on the firefly algorithm”. Expert Systems with Applications, 38(12): 14805-14811.
  32. Basu, B., and Mahanti, G. K. (2011). “Fire fly and artificial bees colony algorithm for synthesis of scanned and broadside linear array antenna”. Progress in Electromagnetics Research, 32: 169-190.
  33. Zaman, M. A., and Abdul Matin, M. (2012). “Nonuniformly spaced linear antenna array design using firefly algorithm”. International Journal of Microwave Science and Technology, 2012: 1-8.
  34. Giannakouris, G., Vassiliadis, V., and Dounias, G. (2010). “Experimental study on a hybrid nature-inspired algorithm for financial portfolio optimization”. In: Hellenic Conference on Artificial Intelligence, Springer, 101-111.
  35. Yang, X.-S., Deb, S., and Fong, S. (2011). “Accelerated particle swarm optimization and support vector machine for business optimization and applications”. In: Networked DigitalTechnologies (NDT2011), Communications in Computer and Information Science, Springer, 136: 53-66.
  36. Gholizadeh, S., and Barati, H. (2012). “A comprative study of three metaheuristics for optimum design of trusses”. International Journal of Optimization in Civil Engineering, 3: 423-441.
  37. Jakimovski, B., Meyer, B., and Maehle, E. (2010). “Firefly flashing synchronization as inspiration for self-synchronization of walking robot gait patterns using a decentralized robot control architecture”. In: International Conference on Architecture of Computing Systems, Springer, 61-72.
  38. Nayak, J., Naik, B., Pelusi, D., and Krishna, A. V. (2020). “A Comprehensive Review and Performance Analysis of Firefly Algorithm for Artificial Neural Networks”. In: Nature-Inspired Computation in Data Mining and Machine Learning, X.-S. Yang and X.-S. He (Eds.), Cham: Springer International Publishing, 137-159.
  39. Lin, N., Chen, Y., Liu, H., and Liu, H. (2021). “A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity”. Minerals, 11(2): 159.
  40. Nabavi, M. (1984). “An introduction to the geology of Iran, Geological Survey of Iran”. Tehran University Publications, Tehran.
  41. Calagari, A., Siahcheshm, K., and Sohrabi, G. (2019). “Geochemical study of alteration zones around Au-bearing silicic veins at Zailic, East of Ahar, East-Azarbaidjan Province”. Iranian Journal of Crystallography and Mineralogy, 27(2): 347-360.
  42. Poli, R., Kennedy, J., and Blackwell, T. (2007). “Particle swarm optimization”. Swarm intelligence, 1(1): 33-57.
  43. TSai, P.-W., Pan, J.-S., Liao, B.-Y., and Chu, S.-C. (2009). “Enhanced artificial bee colony optimization”. International Journal of Innovative Computing, Information and Control, 5(12): 5081-5092.
  44. Yousefi, A., and Amirshahi, B. (2015). “A hybrid meta-heuristic algorithm based on ABC and Firefly algorithms”. Journal of Advances in Computer Engineering and Technology, 1(4): 53-58.
  45. Khaze, S. R., Maleki, I., Hojjatkhah, S., and Bagherinia, A. (2013). “Evaluation the efficiency of artificial bee colony and the firefly algorithm in solving the continuous optimization problem”. International Journal on Computational Sciences & Applications (IJCSA), 3(4): 23-35.
  46. Wang, G.-G., Guo, L., Duan, H., and Wang, H. (2014). “A new improved firefly algorithm for global numerical optimization”. Journal of Computational and Theoretical Nanoscience, 11(2): 477-485.
  47. Shamshirband, S., Esmaeilbeiki, F., Zarehaghi, D., Neyshabouri, M., Samadianfard, S., Ghorbani, M. A., Mosavi, A., Nabipour, N., and Chau, K.-W. (2020). “Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths”. Engineering Applications of Computational Fluid Mechanics, 14(1): 939-953.
  48. Mohammadzadeh, M., Nasseri, A., Mahboubiaghdam, M., and Jahangiri, M. (2021). “Mineral prospectivity mapping of Cu-Au by integrating AHP technique with ARAS and WASPAS models in the Sonajil area, E-Azerbaijan”. Zeitschrift der Deutschen Gesellschaft für Geowissenschaften: 171-186.
  49. Ghezelbash, R., and Maghsoudi, A. (2018). “Comparison of U-spatial statistics and C–A fractal models for delineating anomaly patterns of porphyry-type Cu geochemical signatures in the Varzaghan district, NW Iran”. Comptes Rendus Geoscience, 350(4): 180-191.
  50. Saaty, R. W. (1987). “The analytic hierarchy process—what it is and how it is used”. Mathematical Modelling, 9(3): 161-176.
  51. Carranza, E. J. M. (2008). “Geochemical anomaly and mineral prospectivity mapping in GIS”. Elsevier.
  52. Riahi, S., Bahroudi, A., Abedi, M., Aslani, S., and Lentz, D. R. (2022). “Evidential data integration to produce porphyry Cu prospectivity map, using a combination of knowledge and data‐driven methods”. Geophysical Prospecting, 70(2): 421-437.
  53. Boadi, B., Sunder Raju, P. V., and Wemegah, D. D. (2022). “Analysing multi-index overlay and fuzzy logic models for lode-gold prospectivity mapping in the Ahafo gold district – Southwestern Ghana”. Ore Geology Reviews, 148: 105059.
آمار
تعداد مشاهده مقاله: 187
تعداد دریافت فایل اصل مقاله: 44


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب