In geophysics, horizon gridding is much more than just filling gaps between picked points. The choice of interpolation method can strongly impact structural interpretation, reservoir geometry, and volumetric estimations. Some of the most commonly used interpolation methods include: - Nearest Neighbor — fast but often too blocky - Inverse Distance Weighting (IDW) — simple and stable for dense datasets - Minimum Curvature — widely used for smooth geological surfaces - Kriging — geostatistical approach with uncertainty modeling - Radial Basis Functions (RBF) — excellent for smooth continuous horizons - Triangulation / Delaunay — preserves local structures and faults - Machine Learning methods — increasingly used for complex stratigraphic patterns There is no universal "best" interpolation method. The optimal choice depends on: data density, fault complexity, structural style, noise level, and geological objectives. A good grid should honor both the data and the geology.