• Journal of Korean Tunnelling and Underground Space Association
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• v.4 no.3
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• pp.217-227
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• 2002

Geostatistical methods were used for the groundwater flow analysis on the ${\bigcirc}{\bigcirc}$ tunnel area. Linear regression analysis shows that the topographic elevation and ground water level of this area have very high correlation. Groundwater-level contour maps produced by ordinary kriging and cokriging have little differences in mountain areas. But, comparing two maps on the basis of an elevation contour map, a groundwater-level contour map using cokring is more accurate. Analyzing the groundwater flow on two groundwater-level contour maps, the groundwater of study area flows from the north-west mountain areas to near valleys, and from the peak of the mountain to outside areas. In the design steps, the groundwater-level distribution is reasonably considered in the tunnel construction area by cokriging approach. And, geostatistics will provide quantitative information in the unknown groundwatrer-level area.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.255-260
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• 2003

공간자료에 대한 통계적 모형과 상관관계, 거리모형 등을 고려하여 크리깅방법에 의한 미 측정지역의 오존도를 예측한다. 서울시의 오존자료를 이용하여 예측한 결과 보통 크리깅방법이 효율적이다.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.335-345
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• 1999

시뮬레이션 실행 시간이 매우 오래 걸려서 보통 이용하는 비선형최고제곱법으로는 시뮬레이션의 입력 파라메터(또는 절대 상수)를 추정하기 힘든 경우의 추정 문제를 통계적인 메타모델을 이용하여 해결하는 방법에 대하여 기술하였다. 미리 답을 알고 있는 장난감 모형을 이용하여 절대 상수를 추정하기 위해 사용되는 세가지 통계적 메타모델들(전통적 희귀모형, 공간적 선형모형 그리고 projection pursuit 희귀모형)의 성능을 비교하였다. 그 결과 일양 크리깅(universal Kriging)에 의한 공간적 모형이 가장 우수하였고, 이를 실제 핵융합 시뮬레이션 자료에 적용하여 절대 상수를 추정하였다.

• Tunnel and Underground Space
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• v.15 no.2 s.55
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• pp.129-136
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• 2005

This paper describes the implementation of rock mass rating evaluation based on genetic algorithm(GA) and conditional simulation technique to estimate RMR in the area without sufficient borehole data RMR were estimated by GA and conditional simulation technique with reflecting distribution feature and spatial correlation. And RMR determined by GA were compared with the results from kriging. Through the analysis of the results from 30 simulations, the uncertainty of estimation could be quantified.

• Journal of the Korean earth science society
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• v.31 no.4
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• pp.301-312
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• 2010

Geochemical data have been regarded as one of the important environmental variables in the environmental management. Since they are often sampled at sparse locations, it is important not only to predict attribute values at unsampled locations, but also to assess the uncertainty attached to the prediction for further analysis. The main objective of this paper is to exemplify how indicator geostatistics can be effectively applied to geochemical data processing for providing decision-supporting information as well as spatial distribution of the geochemical data. A whole geostatistical analysis framework, which includes probabilistic uncertainty modeling, classification and risk analysis, was illustrated through a case study of cadmium mapping. A conditional cumulative distribution function (ccdf) was first modeled by indicator kriging, and then e-type estimates and conditional variance were computed for spatial distribution of cadmium and quantitative uncertainty measures, respectively. Two different classification criteria such as a probability thresholding and an attribute thresholding were applied to delineate contaminated and safe areas. Finally, additional sampling locations were extracted from the coefficient of variation that accounts for both the conditional variance and the difference between attribute values and thresholding values. It is suggested that the indicator geostatistical framework illustrated in this study be a useful tool for analyzing any environmental variables including geochemical data for decision-making in the presence of uncertainty.