Abstract
Estimation of elemental distribution with minimum estimation errors is crucial in mineral exploration. This study aims to estimate Cu, Mo, Ag, and Au distribution in a local scale. To do this, the application of an ordinary kriging–artificial neural network (OK-ANN) model is proposed for elemental distribution based on lithogeochemical data in Kahang porphyry deposit, Central Iran. In addition, ordinary kriging (OK) method is applied and the result will be compared with the suggested method. Following this, the proposed estimation model has less error in comparison with the OK. To ensure the validity of proposed OK-ANN, the results were correlated with lithological, faults, and alteration maps in the studied region using a logratio matrix. As a result, main parts of Ag and Au enrichment are associated with faults and argillic alteration with high values of overall accuracies (OAs) in the western part of the deposit. Furthermore, Cu values estimated by the OK-ANN are connected with potassic alteration zone in the central part of deposit. In addition, the Cu-, Mo-, Au-, and Ag-enriched anomalous parts are associated with the existing faults, major alterations, and Eocene sub-volcanic rocks as well as porphyritic quartz diorites, monzodiorite–monzogranite, and dacitic units. Correlation coefficient between raw data and the results obtained by the proposed estimator is more than 84%, meaning that the proposed technique can be utilized for estimation of geochemical data in porphyry deposits.
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The authors would like to thank the National Iranian Copper Industries Co (NICICO) for authorizing the use of the Kahang exploration dataset in order to conduct this research.
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Yasrebi, A.B., Hezarkhani, A., Afzal, P. et al. Application of an ordinary kriging–artificial neural network for elemental distribution in Kahang porphyry deposit, Central Iran. Arab J Geosci 13, 748 (2020). https://doi.org/10.1007/s12517-020-05607-0
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DOI: https://doi.org/10.1007/s12517-020-05607-0