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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
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