基于Delaunay三角网的克里金并行算法优化

当采样点数据量较大时, 可以采用Delaunay三角剖分建立三角网来使用局部邻域采样点进行克里金插值. 但是该算法需要对每个插值点拟合半变异函数, 插值点规模大时造成巨大开销. 为此, 本文提出了一种以三角形为单位拟合半变异函数的克里金插值方法, 采用CPU-GPU负载均衡将部分计算优化, 充分考虑不均匀样本对克里金插值效果的影响. 结果表明, 本文算法能够保证不均匀样本集的插值效果, 提升了计算性能且能够保证较高的精度.

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.