• Journal of Soil and Groundwater Environment
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• v.17 no.6
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• pp.69-81
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• 2012

Diverse geostatistical tools such as kriging have been used to estimate the volume and spatial coverage of contaminated soil needed for remediation. However, many approaches frequently yield estimation errors, due to inherent geostatistical uncertainties. Such errors may yield over- or under-estimation of the amounts of polluted soils, which cause an over-estimation of remediation cost as well as an incomplete clean-up of a contaminated land. Therefore, it is very important to use a better estimation tool considering uncertainties arising from incomplete field investigation (i.e., contamination survey) and mathematical spatial estimation. In the current work, as better estimation tools we propose stochastic simulation approaches which allow the remediation volume to be assessed more accurately along with uncertainty estimation. To test the efficiency of proposed methods, heavy metals (esp., Pb) contaminated soil of a shooting range area was selected. In addition, we suggest a quantitative method to delineate the confident interval of estimated volume (and spatial extent) of polluted soil based on the spatial aspect of uncertainty. The methods proposed in this work can improve a better decision making on soil remediation.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1047-1051
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• 2004

The design storm is a crucial element in urban drainage design and hydrological modeling. The total rainfall depth of a design storm is usually estimated by hydrological frequency analysis using historic rainfall records. The different geostatistical approaches (ordinary kriging, universal kriging) have been used as estimators and their results are compared and discussed. Variogram parameters, the sill, nugget effect and influence range, are analysis. Kriging method was applied for developing contour maps of design storm depths In bocheong stream basin. Effect to utilize weather radar data and grid-based basin model on the spatial variation characteristics of storm requires further study.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.495-504
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• 2015

The Gaussian geostatistical model has been widely used for modeling spatial data. However, this model suffers from a severe difficulty in computation because inference requires to invert a large covariance matrix in evaluating log-likelihood. In addressing this computational challenge, three strategies have been employed: likelihood approximation, lower dimensional space approximation, and Markov random field approximation. In this paper, we reviewed statistical approaches attacking the computational challenge. As an illustration, we also applied integrated nested Laplace approximation (INLA) technology, one of Markov approximation approach, to real data to provide an example of its use in practice dealing with large spatial data.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.29-40
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• 2014

A DC resistivity survey was performed to detect anomalies beneath concrete pavement. A set of high conductive media and planar electrodes were used to lessen the effect's a high contact resistance of concrete. Results of the resistivity survey were analyzed and compared with those of other geophysical surveys such as Ground Penetration Radar (GPR), Impulse Response (IR), and Multi-channel Analysis of Surface Waves (MASW), which were carried out in the same location. The results of resistivity survey showed a high resistive distribution in the section of sink and pavement where a pattern of reinforcement was observed through the GPR survey. Also, a comparison of results between the IR and resistivity surveys indicated that the high resistivity was produced by the high dynamic stiffness in the reinforced section. The co-Kriging of both the results of DC resistivity and MASW surveys at the same location showed that an integrated geostatistical analysis is able to give more accurate description on the anomalous subsurface region than can a separate analysis of each geophysical approach. This study suggests that the integrated geostatistical approaches were used for a decision-making process based on the geophysical surveys.

• Geotechnical Engineering
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• v.9 no.3
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• pp.61-66
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• 1993

In this paper, the incorporation of qualitative(or soft) data, such as outputs of geophysical tests or construction experience which has so far been cumulated, was discussed for rock classsification. Geostatistics wart used for this research since the parameters for the design of tunnels are spatially correlated. In particular, indicator kriging technique, which is one of non -parametric approaches, was used. As a selection criteria for an optimal classification, the cost of errors was adopted and the binary classes were only considered for rock classification. In future, incorporating an appreciable amount of available qualitative data will be necessary in tunnelling projects in which quantitative data are scarce. In this respect, this research is of great significance.

• Geotechnical Engineering
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• v.11 no.2
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• pp.71-78
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• 1995

In this paper, a study was performed on classifying a rock mass into multiple classes as in rock mass classification systems, such as RMR system and Q system etc. In a situation with only limited quantitative data available, it was sought to employ a way of incorporating qualitative data in a systematical and reasonable manner. It is based on the realm of Geostatistics. In particular, indicator kriging technique, which is one of non-parametric approaches, was used. As a selection criterion for an optimal classification, the cost of errors was adopted. As a result, the binary rock classification method developed before was extended and generalized for multiple rock classification with its total number of classes unlimited.