Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran

Prediction of geochemical concentration values is essential in mineral exploration as it plays a principal role in the economic section. In this paper, four regression machine learning (ML) algorithms, such as K neighbor regressor (KNN), support vector regressor (SVR), gradient boosting regressor (GBR), and random forest regressor (RFR), have been trained to build our proposed hybrid ML (HML) model. Three metric measurements, including the correlation coefficient, mean absolute error (MAE), and means squared error (MSE), have been selected for model prediction performance. The final prediction of Pb and Zn grades is achieved using the HML model as they outperformed other algorithms by inheriting the advantages of individual regression models. Although the introduced regression algorithms can solve problems as single, non-complex, and robust regression models, the hybrid techniques can be used for the ore grade estimation with better performance. The required data are gathered from in situ soil. The objective of the recent study is to use the ML model’s prediction to classify Pb and Zn anomalies by concentration-area fractal modeling in the study area. Based on this fractal model results, there are five geochemical populations for both cases. These elements’ main anomalous regions were correlated with mining activities and core drilling data. The results indicate that our method is promising for predicting the ore elemental distribution

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

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 to classify 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 grades in similar investigations

Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran

Prediction of geochemical concentration values is essential in mineral exploration as it plays a principal role in the economic section. In this paper, four regression machine learning (ML) algorithms, such as K neighbor regressor (KNN), support vector regressor (SVR), gradient boosting regressor (GBR), and random forest regressor (RFR), have been trained to build our proposed hybrid ML (HML) model. Three metric measurements, including the correlation coefficient, mean absolute error (MAE), and means squared error (MSE), have been selected for model prediction performance. The final prediction of Pb and Zn grades is achieved using the HML model as they outperformed other algorithms by inheriting the advantages of individual regression models. Although the introduced regression algorithms can solve problems as single, non-complex, and robust regression models, the hybrid techniques can be used for the ore grade estimation with better performance. The required data are gathered from in situ soil. The objective of the recent study is to use the ML model’s prediction to classify Pb and Zn anomalies by concentration-area fractal modeling in the study area. Based on this fractal model results, there are five geochemical populations for both cases. These elements’ main anomalous regions were correlated with mining activities and core drilling data. The results indicate that our method is promising for predicting the ore elemental distribution