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

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

• Geophysics and Geophysical Exploration
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• v.12 no.4
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• pp.309-315
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• 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 Korean Geotechnical Society
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• v.21 no.3
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• pp.5-17
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• 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.

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

• 한국지구물리탐사학회:학술대회논문집
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• 2005.05a
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• pp.59-64
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• 2005

Although experimental analysis for groundwater sample at wells located systematically are very effective to delineate seawater intrusion region at coastal area, this method is restricted in few wells and time. We have conducted electrical resistivity sounding at 30 points in the study areas to analyze the region of seawater intrusion and found the boundary between salt wedge and fresh water lens from the analysis results of geostatistical method using variogram for one-dimensional inversion results. The methodology adopted in this study would be useful for finding the seawater intrusion region and evaluating quantitatively.

• Geotechnical Engineering
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• v.13 no.1
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• pp.85-100
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• 1997

The heterogeneity of hydraulic conductivity of Metropolitan Waste Landfill is analized by using geostatistical methods and the contaminant transport analysis is performed by using heterogeneous hydraulic conductivity. The hydraulic conductivity data are obtained from laboratory pressurized permeability tests and the insitu, Slug test. Geostatistical methods used in this analysis are Ordinary Kriging and conditional simulation. It is concluded that the heterogeneities of hydraulic conductivity obtained from conditional simulation are greater than those from Ordinary Kriging analysis. The movement of the contaminant on the hydraulic conductivity with greater heterogeneity obtained from conditional simulation is faster than that observed in Ordinary Kriging analysis.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.69-87
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• 2014

This paper suggests a conditional simulation framework based on multiple data transformations for geostatistical simulation of compositional data. First, log-ratio transformation is applied to original compositional data in order to apply conventional statistical methodologies. As for the next transformations that follow, minimum/maximum autocorrelation factors (MAF) and indicator transformations are sequentially applied. MAF transformation is applied to generate independent new variables and as a result, an independent simulation of individual variables can be applied. Indicator transformation is also applied to non-parametric conditional cumulative distribution function modeling of variables that do not follow multi-Gaussian random function models. Finally, inverse transformations are applied in the reverse order of those transformations that are applied. A case study with surface sediment compositions in tidal flats is carried out to illustrate the applicability of the presented simulation framework. All simulation results satisfied the constraints of compositional data and reproduced well the statistical characteristics of the sample data. Through surface sediment classification based on multiple simulation results of compositions, the probabilistic evaluation of classification results was possible, an evaluation unavailable in a conventional kriging approach. Therefore, it is expected that the presented simulation framework can be effectively applied to geostatistical simulation of various compositional data.

• Journal of the Korean earth science society
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• v.31 no.7
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• pp.681-690
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• 2010

Inversion result of geophysical data given as a block type was geostatistically simulated with borehole observation given as a point type and was applied to the rock classifying map. The geophysical data generally involved secondary information for the target material and were obtained for overall region. In contrast, borehole data provided direct information for the target material, but tended to be effective only for a narrow range of region and were dealt as a point type. Integrated simulation or kriging interpolation of these two different kinds of information required the covariance for point-point, point-block and block-block. Using the Bssim module included in SGeMS software, integrated result of geophysical data and borehole data were obtained. The results were then compared with the method of geostatistical inversion proposed by authors. Downscaling method used in this study showed relatively more flexible than the geostatistical inversion.

• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.262-271
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• 2002

This study presents a practical procedure for the Bayesian inversion of geophysical data. We have applied geostatistical techniques for the acquisition of prior model information, then the Markov Chain Monte Carlo (MCMC) method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter.