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首页> 外文期刊>Journal of neurosurgical sciences >Geostatistical prediction of a local geometric geoid - kriging and cokriging with the use of EGM2008 geopotential model
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Geostatistical prediction of a local geometric geoid - kriging and cokriging with the use of EGM2008 geopotential model

机译:利用EGM2008地理调节模型,局部几何水泥 - Kriging和Cokriging的地质统计预测

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摘要

We investigate prediction abilities of different variants of kriging and different combinations of data in a local geometric (GNSS/leveling based) geoid modeling. In order to generate local geoid models, we have used GNSS/leveling data and EGM2008 geopotential model. EGM2008 has been used twofold. Firstly, it was used as a basic long wave-length trend to be removed from geoid undulation data to generate a residual field of geoid heights modeled later by kriging (remove-restore technique). Secondly, EGM2008-based undulations were used as a secondary variable in a cokriging prediction procedure (as pseudo-observations). Besides the use of EGM2008, the kriging-based local geometric geoid models were generated only on the basis of raw undulations data. Kriging itself was used in two variants, i.e. ordinary kriging and universal kriging for univariate and bivariate cases (cokriging). The quality of kriging-based prediction for all its variants and all data combinations have been investigated on one fixed validation dataset consisting of 86 points and three training data sets characterized by a different density of sampling. Results of this study indicate that incorporation of EGM08 as a long wave-length trend in kriging prediction procedure outperforms cokriging strategy based on incorporation of EGM08 as a secondary spatially correlated variable.
机译:我们调查克里格汀不同变体的预测能力以及在局部几何(基于GNSS / Locking)的大地形建模中的不同数据组合。为了生成当地的大地区模型,我们使用了GNSS / Locking数据和EGM2008地理调节模型。 EGM2008已被使用双重。首先,它被用作从大地大麻波状数据中除去的基本长波长趋势,以产生以后通过Kriging建模的大型木制高度的残留场(移除恢复技术)。其次,基于EGM2008的起伏作为Cokriging预测过程中的次要变量(作为伪观察)。除了使用EGM2008之外,仅基于原始起伏数据产生的基于Kriging的本地几何水大线程模型。 Kriging本身用于两种变体,即普通的Kriging和Universal Kriging用于单变量和生物案件(Cokriging)。已经研究了所有变体的基于克里格的预测的质量和所有数据组合的一个固定验证数据集,其中包括86个点和三个训练数据集,其特征在于不同的采样密度。该研究的结果表明,将EGM08掺入Kriging预测程序中的长波长趋势,基于将EGM08的掺入作为次要空间相关变量来优异地优于Cokriging策略。

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