کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4457551 1620927 2013 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Objective based geochemical anomaly detection—Application of discriminant function analysis in anomaly delineation in the Kuh Panj porphyry Cu mineralization (Iran)
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات زمین شناسی اقتصادی
پیش نمایش صفحه اول مقاله
Objective based geochemical anomaly detection—Application of discriminant function analysis in anomaly delineation in the Kuh Panj porphyry Cu mineralization (Iran)
چکیده انگلیسی


• Objective based geochemical anomaly detection is proposed for real anomaly identification.
• Anomaly and background are defined based on presence or absence of mineralization in drill cores.
• Linear discriminant analysis has successfully delineated the mineralized zones in the Kuh Panj porphyry copper deposit.
• Linear discriminator has classified the samples more effectively than quadratic discriminator.

A common objective method for anomaly detection in geochemical exploration is target delineation by discriminant function analysis. Discriminant analysis (DA) is a multivariate statistical technique that classifies each observation into a specific group based on observed predictor variables and predefined groups. In the present study a new approach is considered for geochemical anomaly identification employing DA and “real” pre-defined “anomaly” and “background” data set. The anomalous and background samples are identified based on presence or absence of mineralization in depth; so, this method is introduced as “objective approach”. In order to classify surface geochemical samples into anomaly and background, assays of core drillings in the Kuh Panj porphyry Cu mineralization are used. They are classed as anomaly if the presence of mineralization is proven and are labeled as background if the absence of mineralization is confirmed in cores. Stepwise Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are utilized to achieve discrimination functions. For the test data used to generate the models, both LDA and QDA methods have led to perfect classification however cross validation has shown 84% and 74% total correct classification for LDA and QDA respectively. Outcomes of this research have demonstrated that LDA can effectively be employed as an objective method for geochemical anomaly identification if available information from geology and geochemistry of target area are employed and utilized. It is also shown that the definition of anomalism in geochemical exploration can be improved remarkably by this approach.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Geochemical Exploration - Volume 130, July 2013, Pages 65–73
نویسندگان
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