8183

7581

كارشناسي ارشد

اكتشاف معدن

اصفهان: دانشگاه صنعتي اصفهان، دانشكده معدن

1391

نه،95ص.: مصور،جدول،نمودار

ص.ع.به فارسي و انگليسي

احمدرضا مختاري، محمدرضا ايران نژادي

نادر فتحيان پور

5/9/92

حسن طباطبايي، حميد هاشم الحسيني

مهندسي معدن

ID7581

به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي

Three dimensional delineation of high grade zones copper in Darezereshk and Aliabad copper deposits using geophysical data and statistical mathematical classification methods Mohammad karimi Mohammad karimi@mi iut ac ir Date of Submission 2013 09 21 Department of Mining Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Persian Supervisor Ahmadreza Mokhtari ar mokhtari@cc iut ac ir Mohammadreza Irannezhadi mohiran@cc iut ac ir Abstract Since the most of copper deposits in Iran are porphyry in type optimizations in exploration of such deposits are of great importance In exploration of this type of deposits geophysical and geological perceptions provide useful information for researchers to determine the location of mineralization separation of high grade zones from background and determination of optimal location of exploration boreholes Due to the high complexity interpreting and analyzing of raw data is very difficult thus using multivariate statistical analysis and supervised classification methods can significantly reduce the risk level of decision making With the help of geophysical data including induced polarization resistivity magnetic measurement and geological data including lithology and alteration in this research separation of high grade zones from low grade zones in porphyry copper deposits is acrried out The three supervised classification methods include Support Vector Machine SVM linear Discriminate Analysis LDA and Quadratic Discriminate Analysis QDA were used to separate the predefined zones In this thesis information and data of Aliabad and Darehzereshk porphyry copper deposits have been analyzed In Aliabad SVM method with the accuracy of 80 compared to LDA method with the accuracy of 65 5 and QDA with 60 accuracy provided better performance in classification of data In Darehzereshk deposit SVM displayed 81 accuracy in comparison to LDA and QDA methods showing 60 7 and 59 5 accuracy respectively Keywords Aliabad copper deposit Darehzereshk copper deposit Multivariate Supervised Classification Discriminant Analysis Support Vector Machine PDF created with pdfFactory trial version www pdffactory com

احمدرضا مختاري، محمدرضا ايران نژادي

نادر فتحيان پور

حسن طباطبايي، حميد هاشم الحسيني