5571

5205

كارشناسي ارشد

اكتشاف معدن

اصفهان: دانشگاه صنعتي اصفهان، دانشكده معدن

1389

[نه]،85ص.: مصور،جدول،نمودار

ص.ع.به فارسي و انگليسي

حسن طباطبائي، هوشنگ اسدي هاروني

نادر فتحيان پور

4/10/89

احمدرضا مختاري، همايون صفايي

مهندسي معدن

ID5205

به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي

87 Potential mapping of copper mineralization in Kerman metalogenic belt using integrated analysis of exploration data in a GIS environment Mohammad Hadi Aminahmadi mineexploration@yahoo com 24 Sep 2010 Department of Mining Engineering Isfahan University of Technology Isfahan 84155 199 Iran Degree M Sc Language FarsiSupervisors Seyed Hasan Tabatabaei Hooshang Asadi HarooniAbstract Due to presence of inherent uncertainties in geosciences data caused by various unknownand even known geological phenomenon applying simple Boolean logics to infer fromsuch data would eventually lead to significant estimation errors One way out of thisdifficulty is to employ knowledge based methods such as Fuzzy logic inference modelswhich handles such uncertainties through considering gradual nature of properties ofqualitative parameters under investigation Fuzzy logic models are considered asknowledge based techniques and when they exploit the advantages of data driventechniques such as neural network form a very powerful class of inference model calledneurofuzzy Such integration of Fuzzy inference power of knowledge and robustness ofneural network models is particularly useful in dealing with estimating and forecasting ingeosciences problems where we are faced with huge amount of data and genetic models Kerman metalogenic belt makes south part of Orumiye Dokhtar metalogeny provinceand is known as the richest copper containing belt of Iran More than 200 deposites andmineral indexes are known in this belt which most of them are porphyries The geology remote sensing and airborn geophysics of the area has been studied In geology studies layers separating have been done In remote sensing studies frequency of Iron oxides andhydroxide ions beside lineations have been studied In term of geophysics reduce to polecorrection has been done on airborn magnetic data being achieved analytical signal Theresults were 6 data layers After that gathering information has been done by fuzzymethod and neural network In all methods 100 training points has been used and thenmodeling has been done the results showed that 88 9 of known deposits and mineralindexes in area with suitable potential are known by neural network and 80 2 ofdeposits and mineral indexes are known by fuzzy method In additional both of them aresimilar by comparing various methods can be concluded that neural network because ofits compatibility with geological structures are more coincided with truth and are moreuseful potential mapping in GIS Key words Porphyry Copper potential mapping Orumiye Dokhtar Zone Fuzzy logic Neural Network Kerman

حسن طباطبائي، هوشنگ اسدي هاروني

نادر فتحيان پور

احمدرضا مختاري، همايون صفايي