• 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.

• 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.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.223-228
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• 2019

In order to integrate large amounts of variable generation resources such as wind and solar reliably into power grids, accurate renewable energy forecasting is necessary. Since renewable energy generation output is heavily influenced by environmental variables, accurate forecasting of power generation requires meteorological data at the point where the plant is located. Therefore, a spatial approach is required to predict the meteorological variables at the interesting points. In this paper, we propose the meteorological variable prediction model for enhancing renewable generation output forecasting model. The proposed model is implemented by three geostatistical techniques: Ordinary kriging, Universal kriging and Co-kriging.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.09a
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• pp.140-143
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• 2003

Air permeabilities in unsaturated fractured tuff at the Apache Leap Research Site (ALRS) near Superior, Arizona, exhibit a self-affine behavior, thus renders a field random fractal. Based up fractal scaling, the observed scale effect has been interpreted [Hyun et al., 2002]. Recently, Frantziskonis and Hansen [2000] presented that fractal scaling can be represented based on wavelets. This study deals with the way of using wavelets for fractal scaling. A numerical study is presented to examine the applicability of wavelet-based approach to determining upscaled air permeability values on various data supports at the site. To characterize the scaling property of self-affine fields generated based upon wavelets, Hurst coefficient, H. was inferred by applying the average wavelet coefficient (AWC) method. The result yielded H = 0.24, which is very close to the result of geostatistical analysis using a power variogram (H = 0.22). The study concludes that wavelet-based scaling is a useful way of determining parameter values on different data supports, which is an essential task for modeling of subsurface flow and mass transport in a numeric grid with different resolutions (grid size).

• Geomechanics and Engineering
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• v.36 no.5
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• pp.475-487
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• 2024

Many uncertainties affect the stability assessment of rock structures. Some of these factors significantly influence technology decisions. Some of these factors belong to the geological domain, and spatial uncertainty measurements are useful for structural stability analysis. This paper presents an integrated approach to study the stability of rock structures, including spatial factors. This study models two main components: discrete structures (fault zones) and well known geotechnical parameters (rock quality indicators). The geostatistical modeling criterion are used to quantify geographic uncertainty by producing simulated maps and RQD values for multiple equally likely error regions. Slope stability theorem would be demonstrated by modeling local failure zones and RQDs. The approach proided is validated and finally, the slope stability analysis method and fuzzy Laypunov criterion are applied to mining projects with limited measurement data. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and fuzzy theory.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.138-147
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• 2010

As inaccurate traffic volume prediction may result in inadequate transportation planning and design, traffic volume prediction based on traffic volume data is very important in spatial decision making processes such as transportation planning and operation. In order to improve the accuracy of traffic volume prediction, recent studies are using the geostatistical approach called kriging and according to their reports, the method shows high predictability compared to conventional methods. Thus, this study estimated traffic volume data for St. Louis in the State of Missouri, USA using the kriging method, and tested its accuracy by comparing the estimates with actual measurements. In addition, we suggested a new method for enhancing the accuracy of prediction by the kriging method. In the new method, we estimated traffic volume data: first, by applying anisotropy, which is a characteristic of traffic volume data appearing in determining variogram factors; and second, by performing co-kriging analysis using interstate highway, which is in a high spatial correlation with traffic volume data, as a secondary variable. According to the results of the analysis, the analysis applying anisotropy showed higher accuracy than the kriging method, and co-kriging performed on the application of anisotropy produced the most accurate estimates.

• Journal of the Korean earth science society
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• v.32 no.6
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• pp.525-536
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• 2011

In any applications using various types of spatial data, it is very important to account for the scale differences among available data sets and to change the scale to the target one as well. In this paper, we propose to use a geostatistical downscaling approach based on vaiorgram deconvloution and block simulation to generate fine-scale categorical thematic maps from coarse-scale fraction data. First, an iterative variogram deconvolution method is applied to estimate a point-support variogram model from a block-support variogram model. Then, both a direct sequential simulation based on area-to-point kriging and the estimated point-support variogram are applied to produce alternative fine-scale fraction realizations. Finally, a maximum a posteriori decision rule is applied to generate the fine-scale categorical thematic maps. These analytical steps are illustrated through a case study of land-cover mapping only using the block fraction data of thematic classes without point data. Alternative fine-scale fraction maps by the downscaling method presented in this study reproduce the coarse-scale block fraction values. The final fine-scale land-cover realizations can reflect overall spatial patterns of the reference land-cover map, thus providing reasonable inputs for the impact assessment in change of support problems.

• 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.

• Journal of Environmental Impact Assessment
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• v.20 no.6
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• pp.879-890
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• 2011

The GOSAT (Greenhouse gases Observing SATellite) data provide new opportunities the most regionally complete and up-to-date assessment of $CO_2$. However, in practice, GOSAT records often suffer from missing data values mainly due to unfavorable meteorological condition in specific time periods of data acquisition. The aim of this research was to identify optimal spatial interpolation techniques to ensure the continuity of $CO_2$ from samples taken in the North East Asia. The accuracy among ordinary kriging (OK), universal kriging (UK) and simple kriging (SK) was compared based on the combined consideration of $R^2$ values, Root Mean Square Error (RMSE), Mean Error (ME) for variogram models. Cross validation for 1312 random sampling points indicate that the (UK) kriging is the best geostatistical method for spatial predictions of $CO_2$ in the East Asia region. The results from this study can be useful for selecting optimal kriging algorithm to produce $CO_2$ map of various landscapes. Also, data users may benefit from a statistical approach that would allow them to better understand the uncertainty and limitations of the GOSAT sample data.

• 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.