CAJ | 학술논문

K riging插值方法的前提是数据符合或基本符合正态分布的假设，或通过对非正态数据进行正态变换，使其满足要求。但对于拖尾的负偏态分布数据，正态变换方法无法奏效，若直接对数据进行Kriging插值，精度较低。文中在对样本数据深入分析的基础上，借助G IS平台，从不同透视角度对数据进行趋势分析，利用二次多项式模型对数据进行全局趋势拟合，并对随机的短程变异进行Kriging插值，选取不同的变异函数模型进行拟合比较，最终确定最优的模型参数，达到较为理想的插值结果。
K riging삽치방법적전제시수거부합혹기본부합정태분포적가설，혹통과대비정태수거진행정태변환，사기만족요구。단대우타미적부편태분포수거，정태변환방법무법주효，약직접대수거진행Kriging삽치，정도교저。문중재대양본수거심입분석적기출상，차조G IS평태，종불동투시각도대수거진행추세분석，이용이차다항식모형대수거진행전국추세의합，병대수궤적단정변이진행Kriging삽치，선취불동적변이함수모형진행의합비교，최종학정최우적모형삼수，체도교위이상적삽치결과。
The premise of Kriging interpolation method is to meet or in line with the normal distribution of data ,or normally transformed to meet the requirements .But for the serious negative trailing skewed data , normality transformation method can not work .If the Kriging interpolation method is used directly on the data ,the accuracy is low .Based on the sample data in-depth analysis .GIS platform is used from a different perspective angle data for trend analysis . T he quadratic polynomial model is used to fit the data trends globally ,and conduct Kriging interpolation for random short variation .In order to achieve the satisfactory interpolation results ,different variogram models are selected and compared ,and ultimately the optimal model parameters are determined .