شناسایی نواحی امیدبخش کانی سازی مس پورفیری در ناحیه چهارگنبد استان کرمان با استفاده از روش هوشمند یادگیری سریع

نوع مقاله : علمی-پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه مهندسی معدن، دانشکده فنی و مهندسی، دانشگاه بین المللی امام خمینی (ره)، قزوین

2 استادیار، گروه مهندسی معدن، دانشکده فنی و مهندسی، دانشگاه بین المللی امام خمینی (ره)، قزوین

3 دانشیار، گروه مهندسی معدن، دانشگاه ملایر، ملایر

چکیده

در مدلسازی پتانسیل‌های معدنی و شناسایی اهداف اکتشافی با استفاده از روش‌های هوش ‌مصنوعی، نحوه استفاده از نمونه‌های آموزشی و انتخاب الگوریتم مناسب یک مساله مهم است، زیرا عدم استفاده از الگوریتم آموزش مناسب موجب بروز خطاهای سیستماتیک در مدل خروجی می‌شود. هدف از مطالعه حاضر، تهیه نقشه پتانسیل ‌معدنی کانی‌سازی مس ‌پورفیری در منطقه چهارگنبد استان کرمان با استفاده از شبکه ‌عصبی ‌مصنوعی است. در این راستا نقشه‌های شاهد وزن‌دار پیوسته برای معیارهای توده‌ نفوذی، گسل، ژئوشیمی و آلتراسیون‌ها تولید و به عنوان ورودی شبکه‌ عصبی انتخاب شدند. برای آموزش شبکه از 16 نقطه شناخته‌ شده دارای ذخیره و 16 نقطه فاقد کانی‌سازی و همچنین از الگوریتم ماشین ‌یادگیری‌ سریع استفاده ‌شد. در نهایت مدل ‌پتانسیل ‌معدنی تولید شده با استفاده از شبکه ‌عصبی ‌مصنوعی با نتایج مدلسازی با روش تلفیق میانگین‌ هندسی، با استفاده از نمودار نرخ پیش‌بینی مساحت بهبودیافته، مورد مقایسه قرار گرفت و برای مدل‌های یاد شده، به ترتیب نرخ پیش‌بینی 34/0 و 27/0 به دست آمد. ارزیابی مدل‌ها اثبات کرد که اهداف شناسایی شده و مناطق دارای پتانسیل بالای کانی‌سازی مس، انطباق خوبی با اندیس‌ها و کانسارهای مس شناخته ‌شده و همچنین با سایر شواهد اکتشافی دارند، بنابراین می‌توانند برای طراحی ادامه عملیات اکتشاف مورد توجه قرار گیرند.

کلیدواژه‌ها

عنوان مقاله [English]

Recognizing Porphyry Copper Mineralization Targets in Chahar-Gonbad Area of Kerman Province Using Extreme Learning Intelligent Method

نویسندگان [English]

  • M. Ghadiyanloo 1
  • A. Alimoradi 2
  • M. Yousefi 3
1 M.Sc Student, Dept. of Mining Enginiering, Faculty of Technical & Engineering, Imam Khomeini International University, Qazvin, Iran
2 Assistant professor, Dept. of Mining Enginiering, Faculty of Technical & Engineering, Imam Khomeini International University, Qazvin, Iran
3 Associate Professor, Dept. of Mining Enginiering, Malayer University, Malayer, Iran
چکیده [English]

Selection of training sites is an important and critical undertaking in the modeling procedure of mineral exploration targets using artificial intelligence approaches. This is because application of improper training algorithms results in exploration targeting models that carry bias and uncertainty. The present study aims to model exploration targets of porphyry copper mineralization in Chahar-Gonbad area, Kerman province, Iran, using artificial neural networks. In this regard, continuous weighted evidence maps of exploration criteria including proximity to intrusive contacts, fault density, multi-element geochemical signature and proximity to iron-oxide and argillic alterations were generated and applied as inputs to the neural network. Subsequently, 16 points with known mineral deposits and 16 points without mineralization were used to train the neural network through extreme learning algorithm. The ensuing exploration targeting model was compared with a model obtained by using geometric average integration method through prediction-area plot. The overall efficiency of the models are 0.34 and 0.27, respectively. Evaluation of the models demonstrated that the areas with high copper mineralization potential, marked as exploration targets, are in good conformity with known copper occurrences as well as with geological indicator features. Thus, the targets can be planned for further exploration programs.

کلیدواژه‌ها [English]

  • Continuous weighting method
  • Logistic function
  • Chahar-Gonbad area
  • Artificial neural networks
  • Exploration targets

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  • تاریخ دریافت: 14 شهریور 1399
  • تاریخ بازنگری: 22 دی 1399
  • تاریخ پذیرش: 22 دی 1399

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