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首页> 外文期刊>Journal of Earth Science & Climatic Change >Influence of the Variogram Model on an Interpolative Survey Using Kriging Technique
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Influence of the Variogram Model on an Interpolative Survey Using Kriging Technique

机译：变异函数模型对使用Kriging技术进行的插值测量的影响

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Geostatistics is an efficient and effective method to continuously assess the content, the spatio-temporal distribution and the correlation of a discretely sampled deposit. It begins with an exploratory analysis that evaluates the consistency and distribution of data through histograms and QQ plots, and then a structural analysis that evaluates data correlation and dependency through variogram and finally a predictive analysis using kriging. This predicting method is used in various geographical investigations: meteorology, demography, hydrology, orography, economy, and pollution, etc. Even when using related software, it is generally of the duty of the user to manually select the suitable variogram model. The main objectives of this paper were to highlight how the choice of a variogram model can affect the results of an interpolating predictive analysis and to show how a best-fitted model can be selected. The results, illustrated with an example, show that the choice of the variogram model inevitably influences the results of a kriging at both endpoints and amplitude of the range of the estimated values. However, the direction of variation of the interpolated values is independent of the variogram model: different variogram models (with the same characteristics) produce different thematic maps but, the areas of minimum and maximum values remain unchanged. Fortunately, the computation of some cross validation tests such as mean error (ME), mean square error (MSE), root mean square error (RMSE), average standard error (ASE) and root mean square standardized error (RMSSE) can help to ascertain the performance of the developed models.
机译：地统计学是连续评估离散采样矿床的含量，时空分布和相关性的有效方法。首先是通过直方图和QQ图评估数据的一致性和分布的探索性分析，然后是通过方差图评估数据相关性和依赖性的结构分析，最后是使用克里金法的预测分析。这种预测方法可用于各种地理调查：气象，人口统计学，水文学，地形学，经济和污染等。即使使用相关软件，用户也通常有责任手动选择合适的变异函数模型。本文的主要目的是强调变异函数模型的选择如何影响插值预测分析的结果，并说明如何选择最合适的模型。通过示例说明的结果表明，变异函数模型的选择不可避免地会影响两个端点的克里金法结果以及估计值范围的振幅。但是，内插值的变化方向与变异函数模型无关：不同的变异函数模型（具有相同的特征）会生成不同的专题图，但是最小值和最大值的区域保持不变。幸运的是，一些交叉验证测试的计算（例如均值误差（ME），均方误差（MSE），均方根误差（RMSE），平均标准误差（ASE）和均方根标准误差（RMSSE））可以帮助确定开发模型的性能。

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