Grade Estimation in Esfordi Phosphate Deposit Using Support Vector Regression Method

Nowadays, artificial intelligence methods have been broadly developed and applied for variable estimation to facilitate decision making in many fields. Grade estimation is an important issue in evaluating mineral deposits. Geostatistical methods are among the most commonly used approaches for variable estimation. Since these methods are somewhat defective in relation to limited numbers of dispersed nonlinear data, in this study, the support vector regression, a machine learning method, has been used for grade estimation in Esfordi phosphate deposit. The modeling accuracy was 84% according to the test data. Based on the results obtained from the modeling using the support vector regression method, grade estimation has been made within the block model in Esfordi phosphate deposit. The proposed potential areas in the block model can be taken as the the additional borehole sites in the further exploration stage. The tonnage-grade model was also prepared based on the results obtained by using the support vector regression modeling procedure. For example, based on this model, for a 6% cutoff grade, the reserve is about 15.36 million tons with an average grade of 13.59%.