Radial Basis Functions (RBFs) are used in resource estimation, particularly for spatial interpolation and implicit modeling in geological and mining applications. They offer an alternative when traditional methods like ordinary kriging are not feasible due to challenges in variogram calculation. RBFs are also used in implicit modeling, a technique that uses RBFs to define the boundaries of geological models. How RBFs are used in Resource Estimation: Spatial Interpolation: RBFs are employed to estimate values at unknown locations based on known data points, like drillhole data. This is crucial for creating continuous surfaces or volumes for resource estimation. Implicit Modeling: RBFs are used to define the shape and boundaries of geological formations. This approach offers a flexible way to model complex geological structures, especially when traditional methods are difficult to apply. Grade Estimation: In mining, RBFs can be used to estimate ore grades at different locations within a deposit, aiding in reserve estimation and mine planning. Overcoming Limitations of Traditional Methods: RBFs can be a good alternative when traditional methods like ordinary kriging are challenging, such as when experimental variograms cannot be reliably calculated. Key Concepts: Radial Basis Functions: These are functions that depend on the distance from a central point. Implicit Modeling: This approach uses RBFs to define the geometry of a 3D model by representing surfaces and volumes as level sets of a distance function. Interpolation: Estimating values at unknown locations based on known data points. Advantages of using RBFs in Resource Estimation: Flexibility: RBFs can model complex shapes and surfaces. Simplicity: RBF networks have a simpler structure compared to other neural network models. Speed: RBF networks can have a faster training process than some other methods. Robustness: RBFs can provide reasonable estimates even with limited data or when dealing with skewed distributions.