Exploration
Hamed Norouzi; Aliakbar Daya
Abstract
Estimating mineral reserves in exploration or extraction projects is a critical and challenging process. It must be conducted precisely, regardless of the mining scale and mineral type. With the growing significance of mineral resources in economic and industrial development, the importance of adopting ...
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Estimating mineral reserves in exploration or extraction projects is a critical and challenging process. It must be conducted precisely, regardless of the mining scale and mineral type. With the growing significance of mineral resources in economic and industrial development, the importance of adopting advanced technologies in mineral assessment has also surged. Modern spatial grade modeling techniques can play a pivotal role in decision-making processes. This study aims to compare the performance and capabilities of two popular machine learning methods, including Gaussian Process Regression (GPR) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) in spatial grade modeling of copper at the Chehel Kureh Copper deposit. The dataset comprises 42 drill holes with an average copper grade of 0.18%. Each core sample data point includes seven variables: three spatial coordinates (X, Y, and Depth), lead grade, zinc grade, lithology and copper grade, which serves as the target variable. The Gaussian Process Regression (GPR) and Multilayer Perceptron (MLP-ANN) neural network were employed for copper grade estimation. To make a better assessment, the hyperparameters of both models were optimized using the Bayesian Optimization algorithm. The results showed that the Gaussian Process Regression outperformed MLP-ANN, achieving an RMSE of 0.04 and a coefficient of determination (R²) of 0.89 compared to an RMSE of 0.05 and a coefficient of determination (R²) of for MLP-ANN, suggest the superiority of the Gaussian Process Regression method in estimating copper grade spatial variability.
