<![CDATA[The selection of an interpolation method that complies with the availability of data, to map the grade distribution of mineral commodities, is an important issue in every stage of exploration in the mining industry. A reliable method can produce accurate predictions of the grade distribution of deposits so that it can be used to properly evaluate the economic potential of a mineral deposit. The objective of this research was to compare the performance of four deterministic interpolation methods, including Global Polynomial Interpolation (GPI), Radial Basis Function (RBF), Inverse Distance Weighting (IDW), and Local Polynomial Interpolation (LPI), to map the distribution of Ni, Fe, and MgO. The evaluation of the interpolation results was carried out using the cross-validation technique through the statistical parameters Mean Error (ME), Root Mean Square Error (RMSE), and Mean Relative Error (MRE). The results of the comparison show that the performance of the RBF method is the most accurate as indicated by the lowest RMSE and MRE values, or the ME value that is closest to zero. It can be concluded that the RBF interpolation technique is the best method for predicting the spatial distribution of Ni, Fe, and MgO grades in this study area.]]>
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