Comparison of radial basis function and sequential Gaussian simulation: A review on application in mineral resource estimation

Accurate mineral resource estimation is crucial for optimising mining operations, reducing financial risks, and ensuring efficient resource utilisation. Various spatial modelling techniques have been developed to estimate ore grades and geological continuity, among which radial basis function interpolation and sequential Gaussian simulation are widely used. This review paper provides a comparative analysis of these two methods, examining their theoretical foundations, applications, strengths, and limitations in mineral resource estimation. Radial basis function is a deterministic interpolation method that models spatial relationships based on radial distance functions, making it effective for capturing smooth geological trends. Although kriging techniques have historically dominated the mining sector, new developments have brought to light their shortcomings, especially when handling sparse datasets and grade uncertainty. The paper examined case examples that demonstrate the usefulness of radial basis function and sequential Gaussian simulation in estimating mineral resources, with an emphasis on how they can model grade distributions, measure uncertainty, and maximise financial results. There is a substantial study gap dealing with the comparison of both methods; despite their respective advantages, a head-to-head comparison of radial basis function and sequential Gaussian simulation is still not well-studied. Their relative advantages, computational effectiveness, and compatibility with other geostatistical methods should all be assessed in future research. Guidelines for selecting estimating techniques based on geological conditions and project objectives are given to practitioners. The review emphasises how geostatistical techniques must be continuously improved to improve resource estimation precision and facilitate well-informed mining sector decision-making.