Exploration
Hassanreza Ghasemi Tabar; Sajjad Talesh Hosseini; Andisheh Alimoradi; mahdi fathi; Maryam Sahafzadeh
Abstract
Estimating ore grades during the exploration phase is often time-consuming and costly due to the need for extensive drilling. Geophysical surveys, as the last indirect exploration method before drilling, offer valuable insights into subsurface mineralization. This study introduces a novel approach for ...
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Estimating ore grades during the exploration phase is often time-consuming and costly due to the need for extensive drilling. Geophysical surveys, as the last indirect exploration method before drilling, offer valuable insights into subsurface mineralization. This study introduces a novel approach for simulating “identical twins” of borehole copper grade values using geophysical attributes derived from the geoelectrical method in the Kahang porphyry copper deposit, central Iran. By treating the simulated values as digital twins of actual borehole grades, we employed four machine learning algorithms—Linear Regression (LR), Gradient Boosting (GB), Random Forest (RF), and Support Vector Machine (SVM)—to model the complex relationships between geophysical inputs and copper grades. After data preprocessing with Principal Component Analysis (PCA), a refined dataset was used to train, test, and validate each model. The results demonstrate that GB yielded the highest predictive accuracy, generating grade estimates closely aligned with actual values. This identical twin modeling approach highlights the potential of machine learning to enhance early-stage mineral exploration by reducing dependence on costly drilling.
Exploitation
Hassanreza Ghasemitabar; Andisheh Alimoradi; Hamidreza Hemati Ahooi; Mahdi Fathi
Abstract
Drilling of exploratory boreholes is one of the most important and costly steps in mineral exploration, which can provide us with accurate and appropriate information to continue the mining process. There are limitations on drilling the target boreholes, such as high costs, topographical problems in ...
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Drilling of exploratory boreholes is one of the most important and costly steps in mineral exploration, which can provide us with accurate and appropriate information to continue the mining process. There are limitations on drilling the target boreholes, such as high costs, topographical problems in installation of drilling rigs, restrictions caused by previous mining operation etc. The advances in artificial intelligence can help to solve these problems. In this research, we used python as one of the most pervasive and the most powerful programming languages in the field of data analysis and artificial intelligence. In this method mean shift algorithms have been used to cluster data, random forest to estimate clusters, and gradient boosting to estimate iron grade. Finally, in the studied area of Choghart in Central Iran, more than 91% accuracy was achieved in detection of ore blocks. Also, the results of the neural network indicate the mean square error (MSE) and mean absolute error (MAE) in the training data, respectively equal to 0.001 and 0.029, in the test data is 0.002 and 0.03, and in the validation boreholes, we reached a maximum of 0.06 and 0.2.
M. Fathi; A. Alimoradi; H.R. Hemati Ahooi
Abstract
Scientific uncertainties make the grade estimation very complicated and important in the metallic ore deposits. This paper introduces a new hybrid method for estimating the iron ore grade using a combination of two artificial intelligence methods; it is based on the single layer-extreme learning machine ...
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Scientific uncertainties make the grade estimation very complicated and important in the metallic ore deposits. This paper introduces a new hybrid method for estimating the iron ore grade using a combination of two artificial intelligence methods; it is based on the single layer-extreme learning machine and the particle swarm optimization approaches, and is designed based on the location of the boreholes, depth of the boreholes, and drill hole information from an orebody, and applied for the ore grade estimation on the basis of a block model. In this work, the two algorithms of optimization clustering and neural networks are used for the iron grade estimation in the Choghart iron ore north anomaly in the central Iran. The results of the training and testing the algorithms indicate a significant ability of the optimized neural network system in the ore grade estimation.
