Optimization of interpolation parameters for 1;50 000 regular distribution gravity data based on radial basis function

In order to select the optimized interpolation parameters of 1;50 000 regular distribution gravity data to provide quantitative interpolation basis for data gridding. We take the gravity data of the theoretical model as an example, use the radial basis function method to optimize the interpolation parameters, such as the interpolation kernel function and the search neighborhood, and using the standard deviationindex to evaluate the interpolation results corresponding to different parameters. The results indicate that the natural cubic spline kernel function corresponds to the highest interpolation accuracy, when the R² parameter is in the first interval (0~1), the interpolation is stable and accurate. The interpolation accuracy is highest when the search neighborhood is elliptical, and the preferred interpolation parameters are as follows: the search radius R₁=3 km, R₂=4.5 km, the number of sectors to search is 4, the search angle is 32°, the anisotropy ratio is 0.667, the anisotropy angle is 32°, the maximum number of data to use from all sectors is 80, the maximum number of data to use from each sector is 20, the minimum number of data in all sectors (node is blanked if fewer) is 8, the node is blanked if more than 3 sectors are empty.