Optimization of Kriging Parallel Algorithm Based on Delaunay Triangulation Network
Under a large data amount of sampling points,Delaunay triangulation can be adopted to establish a triangulation network and then employ local neighborhood sampling points for Kriging interpolation.However,this algorithm requires fitting a semi-variogram to each interpolation point,which incurs significant overhead in the condition of a large interpolation point scale.Therefore,this study proposes a Kriging interpolation method that fits the semi-variogram on a triangular basis.Additionally,it utilizes CPU-GPU load balancing to optimize some calculations and fully considers the influence of non-uniform samples on the Kriging interpolation effect.The results show that the proposed algorithm can ensure the interpolation effect of non-uniform sample sets,improve computational performance,and ensure high accuracy.
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万方数据
维普
