Estimation of Iron concentration by using a support vector machineand an artificial neural network - the case study of the Choghart deposit southeast of Yazd, Yazd, Iran

Document Type : Research Paper


1 Department of Mining and Metallurgy Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

2 Department of mining, Imam Khomeini International Qazvin University, Qazvin, Iran


Estimation of the metal value in the metallic deposits is one of the important factors to evaluate the deposits in exploration studies and
mineral processing. Therefore, one accurate estimator is essential to obtain a fine insight into the accumulation of the ore body. There
are geostatistical methods for the estimation of the concentration of iron which performance of these models is complexity of analysis.
The support vector machine (SVM) is by far one of the most robust artificial intelligence techniques used successfully for predictions
and estimations of deposits because of its ability to generalize. Keeping this is view, the aim of this article is to use the SVM and back
propagation neural networks (BPNN) to estimate the concentration of the iron element in the Choghartdeposit, in Iran. Comparing the
obtained results with those of the validation process demonstrates that the SVM method is faster than the BPNN method and is more
precise for the estimation of the iron concentration in the Choghartmine. The results of this study show that artificial intelligence–
based models can evaluate the iron concentration with an acceptable accuracy.


Article Title [Persian]

تخمین غلظت آهن با استفاده از ماشین برداری پشتیبان و شبکه عصبی مصنوعی- مطالعه موردی، معدن آهن چغارت شمال شرقی یزد، یزد، ایران

Authors [Persian]

  • شاهو ملکی 1
  • حمیدرضا رمضی 2
  • سیروان مرادی 2
1 دانشکده معدن و متالوژی، دانشگاه صنعتی امیرکبیر، تهران، ایران
2 دانشکده معدن، دانشگاه بین المللی امام خمینی قزوین، قزوین، ایران.
Abstract [Persian]

تعیین مقدار ذخایر فلزی یکی از موضوعات بسیار مهم در مطالعات ارزیابی ذخیره برای اکتشاف مواد معدنی دارای صرفه اقتصادی می‌باشد. بنابراین، برای درک مناسب انباشتگی و پیچیدگی‌های مواد معدنی وجود یک تخمین‌گر مناسب و دقیق ضروری به نظر می‌رسد. روش‌های زمین آماری یکی از متداول‌ترین روش‌هایی است که معمولاً برای تخمین و مدل سازی ذخایر فلزی مانند آهن بکار می‌روند که مقادیر و مدل‌های حاصل از آن، دارای پیچیدگی‌ها و مشکلات بسیار زیادی می‌باشد. امروزه با پیدایش روش‌های کاربردی هوش مصنوعی مانند ماشین برداری پشتیبان با توجه به قابلیت تعمیم بالا آن و سادگی در پیش‌بینی و تخمین مدل‌‌سازی می‌توان به عنوان یکی از بهترین و موفق‌ترین روش‌های هوش مصنوعی در تخمین ذخایر معدنی فلزی بکار گرفته شود. ازینرو، در این تحقیق از روش ماشین برداری پشتیبان و شبکه عصبی برگشتی برای تخمین ذخیره آهن در معدن چغارت استفاده گردیده است. مقایسه نتایج بدست آمده برای تعیین غلظت این ذخیره نشان می‌دهد که ماشین برداری پشتیبان نسبت به شبکه عصبی برگشتی سریع‌تر و دقیق‌تر می‌باشد و همچنین مدل‌سازی‌های حاصل از این روش می‌تواند برای ارزیابی و تخمین غلظت ذخایر آهن با خصوصیات مشابه، با دقت قابل قبولی بکار گرفته شود.

Keywords [Persian]

  • ذخیره آهن
  • ذخایر معدنی
  • ماشین برداری پشتیبان
  • شبکه عصبی برگشتی
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Volume 4, Issue 2 - Serial Number 2
summer & Autumn
October 2014
Pages 201-212
  • Receive Date: 12 April 2014
  • Revise Date: 18 January 2015
  • Accept Date: 08 December 2014
  • First Publish Date: 08 December 2014