• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.321-338
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• 2009

The objective of this paper is to compare spatial prediction capabilities of univariate kriging algorithms for generating GIS-based thematic maps from ground survey data with asymmetric distributions. Four univariate kriging algorithms including traditional ordinary kriging, three non-linear transform-based kriging algorithms such as log-normal kriging, multi-Gaussian kriging and indicator kriging are applied for spatial interpolation of geochemical As and Pb elements. Cross validation based on a leave-one-out approach is applied and then prediction errors are computed. The impact of the sampling density of the ground survey data on the prediction errors are also investigated. Through the case study, indicator kriging showed the smallest prediction errors and superior prediction capabilities of very low and very high values. Other non-linear transform based kriging algorithms yielded better prediction capabilities than traditional ordinary kriging. Log-normal kriging which has been widely applied, however, produced biased estimation results (overall, overestimation). It is expected that such quantitative comparison results would be effectively used for the selection of an optimal kriging algorithm for spatial interpolation of ground survey data with asymmetric distributions.

• Economic and Environmental Geology
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• v.30 no.5
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• pp.493-498
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• 1997

The purpose of this study is to develop geostatistical model for evaluating the abundance of deep-sea manganese nodule. The abundance data used in this study were obtained from the KODOS (Korea Deep Ocean Study) area. The variation of nodule abundance was very high within short distance, while sampling methods was very limited. As the distribution of nodule abundance showed non-gaussian, indicator simulation method was used instead of conditional simulation method and/or ordinary kriging. The abundance data were encoded into a series of indicators with 6 cutoff values. They were used to estimate the conditional probability distribution function (cpdf) of the nodule abundance at any unsampled location. The standardized indicator variogram models were obtained according to variogram analysis. This SIS method had the advantage over other traditional techniques such as the turning bands method and ordinary kriging. The estimating values by indicator conditional simulation near high abundance area were more detailed than by ordinary kriging and indicator kriging. They also showed better spatial characteristics of distribution of nodule abundance.

• Journal of Korean Tunnelling and Underground Space Association
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• v.6 no.2
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• pp.101-111
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• 2004

In order to guarantee the stability of a tunnel and its optimum design, it is very important to obtain enough ground investigation data. In realty, however, it is not the case due to the limitation of measuring spatially distributed data and economical reasons. Especially, there are regions where drilling is impossible due to civil appeal and mountainous topology, and it is also difficult to estimate rock mass classes quantitatively with only geophysical exploration data. In this study, therefore, 3 dimensional multiple indicator kriging (3D-MI kriging), which can incorporate geophysical exploration data and drill core data off a tunnel center line, is proposed to cope with such problems. To this end, two dimensional mutiple indicator kriging, which is one of the geostatistical techniques, is extended for three dimensional analysis. Also, the proposed 3D-MI kriging was applied to determine the rock mass classes by RMR system for the design of a Kyungbu express rail way tunnel.

• Economic and Environmental Geology
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• v.29 no.5
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• pp.575-587
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• 1996

The purpose of this study is to develop geostatistical methods for selection of prospective areas of polymetallic nodule deposits in KODOS (Korea Deep Ocean Study) area of the North-East Pacific Ocean. In this study $110{\times}165$ grid system was used, and each node represents the center of an estimated block of $1km{\times}1km$. The ordinary kriging was applied to SeaBeam2000 data in order to evaluate the bathymetry. A structural analysis (variogram) of the bathymetry data was carried out for constructing digital terrain model (DTM) and the maximum slopes of the bathymetry were calculated by DTM data. The above method can be used to solve the problem that is resulted from the lack of theory of a change of support model for the maximum slope of the bathymetry. The ordinary kriging and the indicator kriging were used to evaluate the nodule abundance, and the different two kriging methods were compared to evaluate the accuracy for the estimation of the nodule abundance. It has been shown that indicator kriging was better estimation tool than the ordinary kriging. The overlay map is presented for the selection of potentially minable sites by combining the two indicator maps of the nodule abundance and the maximum slope of bathymetry. This overlay map could be utilized to establish follow-up survey and to investigate the potentially minable sites in the KODOS area.

• Geotechnical Engineering
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• v.11 no.3
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• pp.17-26
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• 1995

Embracing a suggestion by Sullivan(1984) and minimizing the sum of the estimation variances at all thresholds, a rigorous solution to order relatioll problems in multiple indi gator kriging is formulated. By utilizing the particular structure of the resulting optimization problem, a solution algorithm is developed that requires little computational effort beyond the initial indicator kriging. Thus, this proposed solution is computationally efficient, mathematically consistent, and based upon an explicit statistical foundation-unlike many of the ad hoc solutions currently in use.

• Tunnel and Underground Space
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• v.19 no.5
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• pp.407-420
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• 2009

In this study geostatistical technique using indicator kriging was performed to evaluate the optimal rock mass classification by integrating the various geophysical information such as borehole data and geophysical data. To get the optimal kriging result, it is necessary to devise the suitable technique to integrate the hard (borehole) and soft (geophysical) data effectively. Also, the model parameters of the variogram must be determined as a priori procedure. Iterative non-linear inversion method was implemented to determine the model parameters of theoretical variogram. To verify the algorithm, behaviour of object function and precision of convergence were investigated, revealing that gradient of the range is extremely small. This algorithm for the field data was applied to a mountainous area planned for a large-scale tunneling construction. As for a soft data, resistivity information from AMT survey is incorporated with RMR information from borehole data, a sort of hard data. Finally, RMR profiles were constructed and attempted to be interpreted at the tunnel elevation and the upper 1D level.

• Proceedings of the Korea Water Resources Association Conference
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• 1992.07a
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• pp.256-263
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• 1992

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.350-350
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• 2017

저수지의 설계홍수량 산정 시 인근의 기상관측 자료를 활용하고 있으나 인근에 기상관측 자료가 없거나 저수지 배후 유역이 큰 경우에는 단일 기상관측 자료를 이용하기에는 한계가 있다. 따라서 실무적으로 지점별 기상관측소의 자료를 이용하여 설계홍수량을 산정할 때에는 각 관측소 자료를 이용하여 확률강우량을 산정하고 Thiessen 가중평균을 한 후 면적우량환산계수 (ARF)를 곱하여 사용하고 있는데, Thiessen 방법의 경우 방법이 간단하지만 지형 고도 효과는 무시되고 우량계의 지배면적에 의한 우량계의 분포 상태만을 고려하게 된다. 그러므로 설계홍수량 산정시 사용되는 Thiessen 방법은 공간적 불확실성을 내포하고 있고, 특히 소규모 저수지의 설계홍수량을 산정하는 경우에는 저수지 유역의 국소적인 특징을 나타내기 어렵다. 본 연구에서는 설계홍수량 산정 시 저수지 위치에 해당하는 확률강우량의 공간적 불확실성을 평가하기 위하여 SIS(Sequential Indicator Simulation) 방법을 이용하였다. SIS 방법은 Kriging 기법과 마찬가지로 베리오그램으로부터 얻어지는 공간적 상관관계를 기반으로 하고 있는 방법으로 Kriging 기법과 달리 공간분포의 국소적인 특성을 평가할 수 있다는 장점을 가지고 있다.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.382-387
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• 2002

In this paper, we propose a geostatistical approach incorporated to the Bayesian data fusion technique for supervised classification of multi-sensor remote sensing data. Traditional spectral based classification cannot account for the spatial information and may result in unrealistic classification results. To obtain accurate spatial/contextual information, the indicator kriging that allows one to estimate the probability of occurrence of classes on the basis of surrounding observations is incorporated into the Bayesian framework. This approach has its merit incorporating both the spectral information and spatial information and improves the confidence level in the final data fusion task. To illustrate the proposed scheme, supervised classification of multi-sensor test remote sensing data set was carried out.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.48-53
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• 2007

A new way to integrate various geophysical information for evaluation of RQD was developed. In this study, we did not directly define the RQD value where borehole data are not sampled. Instead, we infer the probability of RQD values with prior probability from borehole direct data, and secondary supporting probability from resistivity and seismic tomography data. For the integration, we applied the geostatstical indicator kriging to get prior probability of RQD value, and indicator kriging with soft data to get the supporting probability from resistivity and seismic data. And we finally use the permanence ratio rule to integrate these information. The finally obtained result was also analyzed to fully utilize the probabilistic features. We show the probability of wrongly classifying the RQD evaluation and vice versa. This result may be used for decision making process based on the geophysical exploration.