کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4406989 1307336 2013 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات ژئوشیمی و پترولوژی
پیش نمایش صفحه اول مقاله
An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit
چکیده انگلیسی

Sungun porphyry copper deposit is in East Azarbaijan province, NW of Iran. There exist four hypogene alteration types in Sungun: potassic, propylitic, potassic–phyllic, and phyllic. Copper mineralization is essentially associated more with the potassic and less with the phyllic alterations and their separation is, therefore, quite important. This research has tried to separate these two alteration zones in Sungun porphyry copper deposit using the Support Vector Machine (SVM) method based on the fluid inclusion data, and seven variables including homogenization temperatures, salinity, pressure, depth, density and the Cu grade have been measured and calculated for each separate sample. To apply this method, use is made of the radial basis function (RBF) as the kernel function. The best values for λ and C (the most important SVM parameters) that perform well in the training and test data are 0.0001 and 1, respectively. If these values for λ and C are applied, the phyllic and potassic alteration zones in the training and test data will be separated with an accuracy of about 95% and 100%, respectively. This method can help geochemists in separating the alteration zones because classifying and separating samples microscopically is not only very hard, but also quite time and money consuming.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemie der Erde - Geochemistry - Volume 73, Issue 4, December 2013, Pages 545–554
نویسندگان
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