• Tunnel and Underground Space
• /
• v.13 no.2
• /
• pp.87-99
• /
• 2003

Geostatistics is defined as the theory of modeling of regionalized variables and is an efficient and elegant methodology for estimation and uncertainty evaluation from limited spatial sample data. In this study, we have made a theoretical comparison between kriging estimation and geostatistical simulation methods. Kriging methods do not preserve the histogram of original data nor their spatial structure, and also provide only an incomplete measure of uncertainty when compared to the simulation methods. A practical procedure of geostatistical simulation is suggested in this study and the technique is demonstrated through an application, in which it was used to identify the spatial distribution of RMR as well as to evaluate the spatial uncertainty. It is concluded that the geostatistical simulation is the appropriate method to quantify the spatial uncertainty of geotechnical variables such as RMA. Therefore, the results from the simulation can be used as useful information for designer's considerations in decision-making under various geological conditions as well as the related terms of contract.

• 한국지구과학회:학술대회논문집
• /
• 2006.02a
• /
• pp.213-218
• /
• 2006

이 논문에서는 지구통계학적 시뮬레이션을 이용하여 자료 표현에서의 불확실성이 최종적인 공간통합에 미치는 영향을 정량적으로 분석하고자 하였다. 광물자원 탐사를 위한 공간통합 사례연구를 통해 시뮬레이션 결과에 따라 예측 능력의 차이가 나타남을 확인 할 수 있었으며, 결론적으로 지구통계학적 시뮬레이션이 공간 자료의 불확실성 모델링에 효율적으로 이용될 수 있을 것으로 판단된다.

• 한국지구물리탐사학회:학술대회논문집
• /
• 2008.10a
• /
• pp.63-67
• /
• 2008

Geostatistical inverse approach using geophysical data was applied to indirectly make the RMR classification at points apart from boreholes. The geostatistical appoach was usually used to find optimized estimation which supports two or more different physical properties at unsampled points. However, in this study, an approach to solve inverse problem was proposed. The primary variable, RMR values obtained at known boreholes, is geostatistically simulated with many realization at pre-defined grid point according to the variogram model. The simulated values are sequentially compared with the physical property resulted from geophysical survey at an arbitrary grid point, and the most similar one is chosen. This process means that the spatial distribution of primary variable, RMR, is conformed well to the original pattern of the borehole observation, and ensure to fit the geophysical survey result to reflect the correlation between different physical properties.

• Geophysics and Geophysical Exploration
• /
• v.7 no.2
• /
• pp.121-129
• /
• 2004

The geostatistical approach was applied to integrate MT (Magneto-telluric) resistivity data and borehole information for the spatial RMR (Rock Mass Rating) evaluation. Generally, resistivity of the subsurface is believed to be positively related to the RMR, thus the resistivity and borehole RMR information was combined in a geostatistical approach. To relate the two different sets of data, we take the MT resistivity data as secondary information and estimate the RMR mean values at unsampled points by identification of the resistivity to the borehole data. Two types of approach are performed for the estimation of RMR mean values. Then the residuals of the RMR values around the borehole sites are geostatistically modeled to infer the spatial structure of difference between real RMR values and estimated mean values. Finally, this geostatistical estimation is added to the previous means. The result applied to a real situation shows prominent improvements to reflect the subsurface structure and spatial resolution of RMR information.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
• /
• 2004.03a
• /
• pp.463-468
• /
• 2004

이 연구에서는 원격탐사 자료를 이용하여 얻어진 분류 결과로부터 분류 정확도의 공간적 불확실성을 추정하고자 하였다. 기존 분류결과로부터 얻어지는 토지 피복별 확률값을 지구통계학적 시뮬레이션 기법을 이용하여 참조자료의 공간적 분포와 통합하였다. 다중센서 화상 통합에 적용한 결과, 각 토지피복 항목별로 공간적인 정확도 분포를 얻을 수 있을 수 있었으며 이러한 자료는 분류결과를 해석하는데 유용하게 사용될 수 있을 것으로 기대된다.

• Journal of the Korean Geotechnical Society
• /
• v.21 no.3
• /
• pp.5-17
• /
• 2005

Spatial uncertainty of Busan marine clay ground, which commonly occurs during site investigation testing, data analysis and transformation modeling, has been described. In this paper geotechnical uncertainty of shear strength indicator $N_k$ has been quantified in both horizontal direction and vertical direction using geostatistical Kriging method. Most of soil data used are from 25 boring tests, 75 laboratory tests, 124 field vane tests and 25 cone penetration tests (CPT). CPT-$N_k$ data for undrained shear strength determination, which are the most important properties in geotechnical design stages, have been analysed. Comparison between cone factor from conventional CPT-based method and that of geostatistical method shows that geostatistical Kriging method is an ideal tool to quantify the spatial variability of uncertainty from self-correlation of soil property of interest, and can be recommended to identify the spatial distribution of consolidation .md shear strength of soils at any sites concerned.

• Journal of Advanced Marine Engineering and Technology
• /
• v.35 no.8
• /
• pp.1132-1140
• /
• 2011

When the offshore wind power plant is planned to construct, it is important for the wind farm site to figure out the distribution of the weak soil layers that might cause subsidence by the impact of the external moment from the wind plant's load and an oscillating wind load. Coring test is the optimistic method to figure out weak soil layers, but this method have some problem such as condition of the in-situ or economical limitation. In order to make up for the weak points in coring test, the researches using the geostatistics methods is actually done. In this study, setting a fixed coastal area that offshore wind plants construct firstly and Estimation of distribution on the thickness of the weak soil layer through the geostatistic method is conducted. The weak soil layer is sorted by result of the Standard penetration test, geostatistic method is used to ordinary kring and sequential gaussian simulation and compared to both method's result. As a results of study, we found that both methods show similar estimations of deep weak soil layer and we could evaluate quantitatively the uncertainty of the result.

• Proceedings of the Korean Society of Marine Engineers Conference
• /
• 2011.06a
• /
• pp.240-240
• /
• 2011

• Geophysics and Geophysical Exploration
• /
• v.12 no.4
• /
• pp.309-315
• /
• 2009

Integrated interpretation of multi-geophysical data has been continuously used in terms that it has provided more confident information than the result from single-geophysical data. Especially, geostatistical integration has its own superiority that it is possible to deal with spatial characteristics as well as physical properties of survey data and the process of integration is clear. This paper further extends the previous work of geostatistical inversion for integrated interpretation. In this paper, we propose a new way of dealing with the case that the multi-geophysical data do not share the measurement range. According to the geostatistical kriging, the closer between the measurement points, the smaller kriging variance we get, and vice versa. We used this spatial properties as a weighting value to the process of geostatistical inversion for the geophysical data integration. An objective way to integrate different kinds of geophysical data measured at different ranges is provided with this algorithm.

• Journal of The Geomorphological Association of Korea
• /
• v.20 no.4
• /
• pp.85-99
• /
• 2013

The objective of this paper is to analyze the spatial distribution of errors in the DEM generated using waterlines from multi-temporal remote sensing data and to assess flood vulnerability. Unlike conventional research in which only global statistics of errors have been generated, this paper tries to quantitatively analyze the spatial distribution of errors from a probabilistic viewpoint using geostatistical simulation. The initial DEM in Baramarae tidal flats was generated by corrected tidal level values and waterlines extracted from multi-temporal Landsat data in 2010s. When compared with the ground measurement height data, overall the waterline-based DEM underestimated the actual heights and local variations of the errors were observed. By applying sequential Gaussian simulation based on spatial autocorrelation of DEM errors, multiple alternative error distributions were generated. After correcting errors in the initial DEM with simulated error distributions, probabilities for flood vulnerability were estimated under the sea level rise scenarios of IPCC SERS. The error analysis methodology based on geostatistical simulation could model both uncertainties of the error assessment and error propagation problems in a probabilistic framework. Therefore, it is expected that the error analysis methodology applied in this paper will be effectively used for the probabilistic assessment of errors included in various thematic maps as well as the error assessment of waterline-based DEMs in tidal flats.