中文说明：
包括基本的克里金(Kriging)插值法实现代码，仅实现基本方法部分，不包含扩展克里金方法
克里金使用普通克里格插值在z和y位置的测量变量z，在未采样位置席，Yi。该函数需要包含变异函数所有必要信息的变量vstruct。vstruct是函数变差函数拟合的第四个输出参数。这是一个基本的，但易于使用的函数来执行简单的克里格插值。我称之为初级的，因为它总是包括所有的观察，以估计在未采样的位置的值。当样本位置不在自相关范围内，但需要k近邻搜索算法或类似算法时，这可能不是必需的。因此，这些算法最适用于相对较少的观测值（100-500）。对于更多的观察，我建议使用GSTAT。请注意，如果有两个或多个观测值，克里格法将失败
English Description：
It includes the basic Kriging interpolation method implementation code, only the basic method part, not the extended Kriging method < br / > Kriging uses ordinary Kriging to interpolate the measurement variables Z at z and Y positions, and Yi at the non sampling position. This function needs variable vsstruct which contains all necessary information of variogram. Vsstruct is the fourth output parameter of function variation function fitting. This is a basic but easy to use function to perform simple Kriging interpolation. I call it primary because it always includes all observations to estimate the value at the unsampled location. This may not be necessary when the sample location is not in the autocorrelation range, but k-nearest neighbor search algorithm or similar algorithm is needed. Therefore, these algorithms are most suitable for relatively few observations (100-500). For more observation, I recommend using GST at. Note that Kriging will fail if there are two or more observations