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Afzal, Peyman, Farhadi, Sasan, Boveiri Konari, Mina, Shamseddin Meigooni, Mojtaba, Daneshvar Saein, Lili. (1401). Geochemical Anomaly Detection in the Irankuh District Using Hybrid Machine Learning Technique and Fractal Modeling. , 12(1), 191-199. doi: 10.22059/geope.2022.336072.648644
Peyman Afzal; Sasan Farhadi; Mina Boveiri Konari; Mojtaba Shamseddin Meigooni; Lili Daneshvar Saein. "Geochemical Anomaly Detection in the Irankuh District Using Hybrid Machine Learning Technique and Fractal Modeling". , 12, 1, 1401, 191-199. doi: 10.22059/geope.2022.336072.648644
Afzal, Peyman, Farhadi, Sasan, Boveiri Konari, Mina, Shamseddin Meigooni, Mojtaba, Daneshvar Saein, Lili. (1401). 'Geochemical Anomaly Detection in the Irankuh District Using Hybrid Machine Learning Technique and Fractal Modeling', , 12(1), pp. 191-199. doi: 10.22059/geope.2022.336072.648644
Afzal, Peyman, Farhadi, Sasan, Boveiri Konari, Mina, Shamseddin Meigooni, Mojtaba, Daneshvar Saein, Lili. Geochemical Anomaly Detection in the Irankuh District Using Hybrid Machine Learning Technique and Fractal Modeling. , 1401; 12(1): 191-199. doi: 10.22059/geope.2022.336072.648644

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

Geopersia
دوره 12، شماره 1 - شماره پیاپی 22287825، فروردین 2022، صفحه 191-199    اصل مقاله (2.21 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/geope.2022.336072.648644
نویسندگان
Peyman Afzal1؛ Sasan Farhadi* 2؛ Mina Boveiri Konari3؛ Mojtaba Shamseddin Meigooni4؛ Lili Daneshvar Saein5
1Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
2Department of Structural, Geotechnical and Building Engineering, Polytechnic University of Turin, Italy
3Department of Economic Geology, Tarbiat Modares University, Tehran, Iran
4Department of Earth Sciences, Science and Research Branch, Azad University, Tehran, Iran
5Department of Geology, Payame Noor University, Tehran, Iran
چکیده
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.
کلیدواژه‌ها
Hybrid Machine learning؛ Geochemical anomaly detection؛ K-Nearest Neighbor Regression (KNNR)؛ Random Forest Regression (RFR)؛ Fractal Modeling
عنوان مقاله [English]
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نویسندگان [English]
مجتبی شمس الدین میگونی4؛
4ایران، تهران، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، گروه علوم زمین
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تعداد مشاهده مقاله: 1,034
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