Geochemical Anomaly Detection in the Irankuh District Using Hybrid Machine Learning Technique and Fractal Modeling

Document Type : Research Paper

Authors

1 Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Structural, Geotechnical and Building Engineering, Polytechnic University of Turin, Italy

3 Department of Economic Geology, Tarbiat Modares University, Tehran, Iran

4 Department of Earth Sciences, Science and Research Branch, Azad University, Tehran, Iran

5 Department of Geology, Payame Noor University, Tehran, Iran

Abstract

Prediction of elemental concentrations is essential in mineral exploration as it plays a vital role in detailed exploration. New machine learning (ML) methods such as hybrid models are robust approaches infrequently used concerning other methods in this field; therefore, they have not been examined properly. In this study, a hybrid machine learning (HML) method was proposed based on combining K-Nearest Neighbor Regression (KNNR) and Random Forest Regression (RFR) to predict Pb and Zn grades in the Irankuh district, Sanandaj-Sirjan Zone.. The aim of the proposed study is to employ the hybrid model as a new method for grade distribution. The KNNR-RFR hybrid model results have been applied for the Pb and Zn anomalies classification. The hybrid (KNNR-RFR) method has shown more accurate prediction outputs based on the correlation coefficients than the single regression models with 0.66 and 0.54 correlation coefficients for Pb and Zn, respectively. The KNN-RF results were used for the classification of Pb and Zn anomalies in the study area. The concentration-area fractal model separated the main anomalous areas for these elements. The Pb and Zn main anomalies were correlated with mining activities and core drilling data. The current study demonstrates that the hybrid model has a substantial potential for the ore elemental distribution prediction. The presented model expresses a promising result and can predict ore grade in similar investigations.

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Article Title [Persian]

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Author [Persian]

  • مجتبی شمس الدین میگونی 4
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4 ایران، تهران، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، گروه علوم زمین
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History

  • Receive Date: 23 December 2021
  • Revise Date: 07 March 2022
  • Accept Date: 14 March 2022

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