تخمین عیار کانسار فسفات اسفوردی با روش رگرسیون بردار پشتیبان

نوع مقاله : علمی-پژوهشی

نویسنده

استادیار، گروه مهندسی معدن، دانشگاه کاشان، کاشان

چکیده

امروزه تخمین متغیر با استفاده از روش­های مبتنی بر هوش مصنوعی از جمله رویکردهای جدیدی است که فرآیند تصمیم­گیری موثر را در بسیاری از علوم میسر ساخته است. تخمین عیار نیز از مسایل مهم در ارزیابی ذخایر معدنی در علوم زمین به شمار می­رود. روش­های زمین آماری از جمله روش­های متداول تخمین متغیر در علوم زمین محسوب می­شوند. از آنجایی که این روش­ها در رابطه با داده­هایی که تعداد آن­ها محدود است و ماهیت پراکندگی و غیر­خطی بودن دارند تا حدودی دچار مشکل می­شوند، در این مطالعه از روش رگرسیون بردار پشتیبان به عنوان یکی از روش­های هوشمند در حیطه الگوریتم­های یادگیری ماشین برای تخمین عیار در کانسار فسفات اسفوردی استفاده شده است. دقت مدلسازی انجام گرفته با این روش بر اساس داده­های آزمایش در حدود 84 درصد شد که نشان­دهنده کارایی مدلسازی انجام شده است. بر اساس نتایج به دست آمده از مدلسازی انجام گرفته به روش رگرسیون بردار پشتیبان، اقدام به تخمین عیار در محدوده مدل بلوکی کانسار فسفات اسفوردی شد. نواحی معرفی شده به عنوان مناطق پرپتانسیل در مدل ­بلوکی تخمین زده شده به روش رگرسیون بردار پشتیبان می­تواند در ادامه فرآیند اکتشاف به عنوان محل گمانه­های تکمیلی مورد برداشت قرار گیرد. همچنین بر اساس نتایج به دست آمده از روش رگرسیون بردار پشتیبان در کانسار فسفات اسفوردی، مدل تناژ- عیار متوسط تهیه شد. به عنوان نمونه بر اساس این مدل به ازای عیار حد 6 درصد، تناژ ذخیره حدود 36/15 میلیون تن با عیار متوسط 59/13 درصد به دست آمد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Grade Estimation in Esfordi Phosphate Deposit Using Support Vector Regression Method

نویسنده [English]

  • M. Abbaszadeh
Assistant Professor, Dept. of Mining Engineering, University of Kashan, Iran
چکیده [English]

Nowadays, artificial intelligence methods have been broadly developed and applied for variable estimation to facilitate decision making in many fields. Grade estimation is an important issue in evaluating mineral deposits. Geostatistical methods are among the most commonly used approaches for variable estimation. Since these methods are somewhat defective in relation to limited numbers of dispersed nonlinear data, in this study, the support vector regression, a machine learning method, has been used for grade estimation in Esfordi phosphate deposit. The modeling accuracy was 84% according to the test data. Based on the results obtained from the modeling using the support vector regression method, grade estimation has been made within the block model in Esfordi phosphate deposit. The proposed potential areas in the block model can be taken as the the additional borehole sites in the further exploration stage. The tonnage-grade model was also prepared based on the results obtained by using the support vector regression modeling procedure. For example, based on this model, for a 6% cutoff grade, the reserve is about 15.36 million tons with an average grade of 13.59%.

کلیدواژه‌ها [English]

  • Machine learning algorithms
  • Support vector regression
  • Modeling
  • Estimation
  • Esfordi phosphate
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