An approach based on Machine Learning Algorithms, Geostatistical Technique, and GIS analysis to estimate phosphate ore grade at the Abu Tartur Mine, Western Desert, Egypt

Purpose. This paper aims to estimate phosphate ore grade in the Abu Tartur area, Western Desert, Egypt, using four Machine Learning Algorithms (MLA), Geostatistical Techniques (variogram and kriging models), and GIS-analysis. Methods. Four machine-learning techniques include Optimizable Decision Tree (ODT), Optimizable Support Vector Machine (OSVM), Optimizable Gaussian Process Regression (OGPR), Artificial Neural Network (ANN) are applied in this paper. The constructed variogram and kriging models, as well as GIS-analysis, provide a clear understanding of all the elements distributed in the Abu Tartur phosphate ore and are very useful at the planning and mining stages. Findings. Phosphate content has been estimated with high accuracy based on the results of four machine-learning techniques. The most efficient technique for estimating phosphate content is optimizable (OGPR), which gives correlation coefficients (R) of 0.933 and 0.927 with Mean Absolute Errors (MAE) of 0.983 and 0.933 for the training and validation data, respectively. In addition, geostatistical and GIS methods have shown that percentage of P2O5, thickness, and Fe% are suitable for phosphate mining processes, except for small pockets that require little attention at the mining stage. Originality. This research attempts to develop a quick estimation of phosphate ore grade and to provide a clear understanding about the distribution of different constituents within the ore body using different techniques. Practical implications. Grade estimation is commonly reduced to a function approximation. Artificial intelligence (AI) techniques, and in particular the chosen type of AI techniques, can provide, a valid methodology for estimating grade, and the proposed models can be applied to any other data in the range used in this research.