بررسی رفتار ژئوشیمیایی عنصر مس به روش کا-میانگین و پیش‏بینی آن توسط شبکه عصبی مصنوعی در منطقه کیوی، استان اردبیل

نوع مقاله: علمی - پژوهشی

نویسندگان

1 اکتشاف معدن، دانشکده مهندسی معدن نفت و ژئوفیزیک، دانشگاه صنعتی شاهرود

2 استاد یار- دانشگاه مهندسی معدن- دانشگاه صنعتی شاهرود

3 استاد - عضو هیئت علمی دانشگاه صنعتی امیرکبیر

چکیده

این پژوهش بر روی برگه یک‏صدهزارم ژئوشیمیایی کیوی انجام شد که توسط سازمان زمین‏شناسی و اکتشافات معدنی ایران با استفاده از آنالیز شیمیایی نمونه های رسوبات آبراهه‏ای تهیه شده است. منطقه کیوی در استان اردبیل قرار دارد. این ناحیه شامل سه واحد سنگی رسوبی، آذرین و دگرگونی می‏باشد. قدیمی ترین واحد رسوبی موجود، سنگ‏های قبل از کرتاسه و جدیدترین آن، مربوط به کواترنر و عهد حاضر است. با توجه به استعداد کانی‏سازی فلزی، بالاخص عنصر مس در این منطقه، بررسی دقیق آن با اهمیت است. بر این اساس، یافتن اطلاعاتی در مورد ارتباط و رفتار عناصر طلا، نقره و مولیبدن نسبت به عنصر مس در این منطقه اهمیت می‏یابد؛ هدف از این بررسی، رفتارسنجی هاله‏های ژئوشیمیایی در منطقه می باشد. در پژوهش حاضر با هدف رفتارسنجی عناصر نام برده، از روش مشهور و مفید کا میانگین استفاده شد. این روش از روش‏های خوشه‏بندی است که بر کمینه کردن مجموع فواصل اقلیدسی هر یک از نمونه‏ها از مرکز دسته‏هایی که به آن تخصیص می‏یابد، استوار می‏باشد. در این پژوهش از تابع کیفیت خوشه‏بندی و میزان مطلوبیت نمونه در خوشه مورد نظر (S(i)) برای تشخیص تعداد خوشه بهینه استفاده شد، سپس با در نظرگرفتن مراکز خوشه ها و نتایج‏ حاصل، معادلاتی به منظور پیش‏بینی مقدار عنصر مس ارائه شد. ا پس از بررسی های رفتاری عناصر، آزمایش شبکه عصبی مصنوعی برای تخمین میزان مس با استفاده از روش‏های رگرسیون عمومی و پس انتشار خطا انجام شد. مقدار صحت (R) تخمین در داده ‏های آزمایشی در شبکه عصبی مصنوعی رگرسیون عمومی و پس انتشار خطا به ترتیب 0.77 و 0.74 گزارش شد. در انتها مشخص شدکه روش شبکه عصبی مصنوعی رگرسیون عمومی در تخمین بهینه عنصر مس در منطقه مورد مطالعه دارای ارجحیت است.

کلیدواژه‌ها


عنوان مقاله [English]

Geochemical Behavior Investigation Based on K-means and Artificial Neural Network Prediction for Copper, in Kivi region, Ardabil province, IRAN

نویسندگان [English]

  • Adel Shirazy 1
  • Mansour Ziaii 2
  • Ardeshir Hezarkhani 3
1 Mining exploration, Faculty of Mining Petroleum and Geophysics, Shahrood University of Technology
2 Associate Professor, Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Semnan, Iran.
3 Professor, Department of Mining & Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran.
چکیده [English]

Kivi region is located in Ardabil province of Iran. This research is on kivi geochemical sheet (on scale 1:100000) which is investigated by geological survey & mineral explorations of Iran (GSI) using stream sediment analyzes. This region consists of sedimentary, igneous and metamorphic rock units. The oldest existing sedimentary unit, the pre-Cretaceous rocks and the newest, is related to Quaternary and the present. Due to the ability of metal mineralization, especially the copper element in this region, it is important to study it carefully. Accordingly, finding information about the relation and behavior of the elements of gold, silver and molybdenum to the copper element in this region is important. The purpose of this study is to determine the behavior of geochemical halos in region. In this study with the aim of geochemical behavior investigating the mentioned elements K-means method was used. This method is based on clustering methods that minimize the total Euclidean intervals of each sample from the center of the groups to which it is assigned. In this research, the clustering quality function ( p(k) ) and the desirability of sample in the desired cluster ( S (i) ) were usedto determine the optimum numberof clusters. Then, taking into account clusters centers and results, equations were provided to predict the amount of copper with a special look at the method. After elemental behavioral studies, an artificial neural network test using general regression and backward propagation of errors was conducted to estimate the amount of copper. The accuracy value (R) of the estimation in the experimental data in the artificial neural network of general regression and backward propagation of errors was 0.77 and 0.74, respectively. Finally, it was determined that the general regression artificial neural network method has an advantage inThe optimal estimation of copper element in the studyarea.

کلیدواژه‌ها [English]

  • Kivi Region
  • copper
  • artificial neural network (ANN)
  • K-Means Clustering
  • Geochemical Behavior Investigation
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