Classification of ASTER Data by Neural Network to Mapping Alterations Related to Copper and Iron Mineralization in Birjand
Publish place: Journal of Mining and Environment، Vol: 15، Issue: 2
Publish Year: 1403
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JMAE-15-2_015
تاریخ نمایه سازی: 8 بهمن 1402
Abstract:
The studied area located in eastern Iran shows a high potential for various mineralizations, especially copper due to its tectonic activity. Remote sensing data can effectively distinguish these areas because of the sparse vegetation. Therefore, in this study, the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) multi-spectral data was used to recognize argillic, sericite, propylitic, and iron oxide alterations associated with copper mineralization. For this purpose, two categories (porphyry copper-iron and advanced argillic-iron) related alterations were considered to perform the classification of a ۲۶۱۷ square kilometer area using a neural network classification algorithm. To evaluate the accuracy of the classifier, the confusion matrix was computed, which provides overall accuracy and the kappa coefficient factors for assessing classification accuracy. As a result, ۶۴.۱۷% and ۸۳.۵% of overall accuracy, and ۰.۶۰۲ and ۰.۸۰۷ of the kappa coefficient were achieved for the advanced argillic alterations and porphyry copper categories, respectively. Ultimately, the validation of the classifications was carried out using the normalized score (NS) equation, employing quantitative criteria. Notably, the advanced argillic class emerged with the top normalized score of ۲.۲۵ out of ۴, signifying a ۵۶% alignment with the geological characteristics of the region. Consequently, this outcome has led to the identification of favorable areas in the central and northeastern parts of the studied area.
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Authors
Jabar Habashi
Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran
Majid Oskouei
Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran
Hadi Jamshid Moghadam
Head of the R&D department of Foladgostar Kowsar Investment Group, Iran
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