• Journal of Korea Water Resources Association
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• v.41 no.7
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• pp.681-692
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• 2008

This study investigated the application of the successive correction method(SCM), a simple data assimilation method, for synthesizing the radar and rain gauge data. First, the number of iteration and influence radius for the SCM application were decided based on their sensitivity analysis. Also, for the evaluation of synthetic rainfall, the distributed rainfall field using the dense rainfall gauge network was assumed to be the true one. The synthetic rainfall field based on the SCM was also compared quantitatively with the one based on the co-Kriging frequently used nowadays. As the results, the SCM, a simple and economical data assimilation method, was found to secure the accuracy and statistical characteristics of the co-Kriging application.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.275-280
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• 2003

공간 자료에서 이상점이 존재할 경우 변이도(Variogram)를 추정함에 있어 그 효과를 줄이기 위한 방법으로 로버스트(robust) 변이도를 이용한다. 그러나 이상점이 존재하는 자료분석에서 로버스트 변이도를 사용하기에 앞서 이상점을 수정한 자료를 사용하였을 경우 그 효율성 또한 좋다고 알려져 있다. 본 논문에서는 이상점이 존재하는 자료를 분석함에 있어 기존의 이상점 수정법 및 새로운 이상점 수정법의 효율성을 비교하였다.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.327-336
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• 2004

Many researchers have used the robust variogram to reduce the effect of outliers in spatial data analysis. Recently it is known that estimating the variogram after replacing outliers is more efficient. In this paper, we suggest a new data cleaner for geostatistic data analysis and compare the efficiency of outlier cleaners.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.4 s.74
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• pp.429-440
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• 2006

This paper comprises two specific objectives. The first is to examine the applicability of ordinary kriging interpolation(OK) to the p-adaptivity of the finite element method that is based on variogram modeling. The second objective Is to present the adaptive procedure by the hierarchical p-refinement in conjunction with a posteriori error estimator using the modified S.P.R. (superconvergent patch recovery) method. The ordinary kriging method that is one of weighted interpolation techniques is applied to obtain the estimated exact solution from the stress data at the Gauss points. The weight factor is determined by experimental and theoretical variograms for interpolation of stress data apart from the conventional interpolation methods that use an equal weight factor. In the p-refinement, the analytical domain has to be refined automatically to obtain an acceptable level of accuracy by increasing the p-level non-uniformly or selectively. To verify the performance of the modified S.P.R. method, the new error estimator based on limit value has been proposed. The validity of the proposed approach has been tested with the help of some benchmark problems of linear elastic fracture mechanics such as a centrally cracked panel, a single edged crack, and a double edged crack.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.4
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• pp.684-689
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• 2006

Rainfall is the most basic input data to analyze the hydrologic system. When we measure the rainfall data, the rainfall data can be missing due to various reasons. Therefore, various interpolation methods are available for compensating the missing data. However, the interpolation methods were used without considering their applicability and accuracy. This study compares the interpolation methods such as the arithmetic mean method, normal ratio method, modified normal ratio method, inverse distance method, linear programming, Kriging method to estimate the existing rainfall correction method.

• Economic and Environmental Geology
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• v.26 no.2
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• pp.207-215
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• 1993

In modem petroleum reservoir engineering, the characterization of reservoir heterogeneities is very important to accurately understand and predict reservoir production performance. Formation evaluation for the description of reservoir is generally conducted by performing the analysis of well logging, core testing, and well testing. However, the measured data points by well logging or core testing are in general very sparse and hence reservoir properties should be interpolated and extrapolated from measured points to uncharacterized areas. In assigning the data for the unknown points, simple averaging technique is not feasible as optimum estimation method since this method does not account the spatial relationship between the data points. The main goal of this work is to develop PC-version of multi-purpose geostatistical model in which several stages are systematically proceeded. In the development of model, the simulator employs a automatic selection of semivariogram function such as exponential or spherical model with the best values of $R^2$. The simulator also implements a special algorithm for the fitting of semivariogram function to experimental sernivariogram. The special algorithm such as trial and error scheme is devised since this method is much more reliable and stable than Gauss-Newton method. The simulator has been tested under stringent conditions and found to be stable. Finally, the validity and the applicability of the developed model have been studied against some existing actual field data.