Recognizing Porphyry Copper Mineralization Targets in Chahar-Gonbad Area of Kerman Province Using Extreme Learning Intelligent Method

Document Type : Research - Paper

Authors

1 M.Sc Student, Dept. of Mining Enginiering, Faculty of Technical & Engineering, Imam Khomeini International University, Qazvin, Iran

2 Assistant professor, Dept. of Mining Enginiering, Faculty of Technical & Engineering, Imam Khomeini International University, Qazvin, Iran

3 Associate Professor, Dept. of Mining Enginiering, Malayer University, Malayer, Iran

Abstract

Selection of training sites is an important and critical undertaking in the modeling procedure of mineral exploration targets using artificial intelligence approaches. This is because application of improper training algorithms results in exploration targeting models that carry bias and uncertainty. The present study aims to model exploration targets of porphyry copper mineralization in Chahar-Gonbad area, Kerman province, Iran, using artificial neural networks. In this regard, continuous weighted evidence maps of exploration criteria including proximity to intrusive contacts, fault density, multi-element geochemical signature and proximity to iron-oxide and argillic alterations were generated and applied as inputs to the neural network. Subsequently, 16 points with known mineral deposits and 16 points without mineralization were used to train the neural network through extreme learning algorithm. The ensuing exploration targeting model was compared with a model obtained by using geometric average integration method through prediction-area plot. The overall efficiency of the models are 0.34 and 0.27, respectively. Evaluation of the models demonstrated that the areas with high copper mineralization potential, marked as exploration targets, are in good conformity with known copper occurrences as well as with geological indicator features. Thus, the targets can be planned for further exploration programs.

Keywords

References

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History

  • Receive Date: 04 September 2020
  • Revise Date: 11 January 2021
  • Accept Date: 11 January 2021

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