• 대한원격탐사학회지
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• 제36권1호
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• pp.83-92
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• 2020

The purpose of this work was to apply geographical information system (GIS) for geostatistical analyzing by selecting a semi-variogram model to quantify the spatial correlation of the population distribution with residential neighborhoods in the both sides of Mosul city. Two hundred and sixty-eight sample sites in 240 ㎢ are adopted. After determining the population distribution with respect to neighborhoods, data were inserted to ArcGIS10.3 software. Afterward, the datasets was subjected to the semi-variogram model using ordinary kriging interpolation. The results obtained from interpolation method showed that among the various models, Spherical model gives best fit of the data by cross-validation. The kriging prediction map obtained by this study, shows a particular spatial dependence of the population distribution with the neighborhoods. The results obtained from interpolation method also indicates an unbalanced population distribution, as there is no balance between the size of the population neighborhoods and their share of the size of the population, where the results showed that the right side is more densely populated because of the small area of residential homes which occupied by more than one family, as well as the right side is concentrated in economic and social activities.

• 한국지하수토양환경학회지:지하수토양환경
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• 제20권3호
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• pp.51-64
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• 2015

In the present study, we develop two history matching techniques based on Markov chain Monte Carlo method where radial basis function and Gaussian distribution generated by unconditional geostatistical simulation are employed as the random walk transition kernels. The Bayesian inverse methods for aquifer characterization as the developed models can be effectively applied to the condition even when the targeted information such as hydraulic conductivity is absent and there are transient hydraulic head records due to imposed stress at observation wells. The model which uses unconditional simulation as random walk transition kernel has advantage in that spatial statistics can be directly associated with the predictions. The model using radial basis function network shares the same advantages as the model with unconditional simulation, yet the radial basis function network based the model does not require external geostatistical techniques. Also, by employing radial basis function as transition kernel, multi-scale nested structures can be rigorously addressed. In the validations of the developed models, the overall predictabilities of both models are sound by showing high correlation coefficient between the reference and the predicted. In terms of the model performance, the model with radial basis function network has higher error reduction rate and computational efficiency than with unconditional geostatistical simulation.

• 지구물리와물리탐사
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• 제15권4호
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• pp.177-189
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• 2012

복합 물리탐사(전기비저항, MT)와 지질(시추 자료 및 코어 물성)정보에 대해 지구통계학적 복합해석 기법을 적용하여 3차원 광체 모델링 평가를 수행하였다. 우선, 복합 물리탐사를 통해 시추공 및 그 외의 전체적인 지역에 대한 비저항대 분포를 파악할 수 있었으며, 코어 물성 시험을 통해 연구지역의 자철석(광체)이 코어 내부의 밀도가 높은 전도성 성분(Fe)에 의해 밀도의 증가에 따라 비저항이 감소하는 상관관계를 나타냄을 파악하였다. 3차원 광체 모델링을 수행하기 위해 사용된 자료는 전기비저항 탐사, MT 탐사, 물성 자료와 시추 자료 등이며, 전체 획득 자료 및 시추 자료에서 추출한 광체의 품위 자료를 이용하였다. 본 연구에서는 자료의 복합 해석을 위해 지구통계학적 기법 중에서, 부족한 실제 측정 자료의 평균 및 분산을 잘 재생시키는 실현 값을 통해 지역적으로 변화하는 불균질성을 잘 묘사하는 순차 가우시안 시뮬레이션(sequential Gaussian simulation)을 사용하였다. 획득된 전체 자료와 품위 자료만을 이용하여 도출한 시뮬레이션 결과, 광체가 기존에 연구되어 존재하는 잔광체의 일정 부분에서 유사한 분포를 나타냈으며, 추가적으로 하부 깊은 심도에 대해 광체의 분포 양상을 추정할 수 있었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.399-401
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• 2003

This paper presents a geostatistical contextual classifier for the classification of remote sensing data. To obtain accurate spatial/contextual information, a simple indicator kriging algorithm with local means that allows one to estimate the probability of occurrence of certain classes on the basis of surrounding pixel information is applied. To illustrate the proposed scheme, supervised classification of multi-sensor remote sensing data is carried out. Analysis of the results indicates that the proposed method improved the classification accuracy, compared to the method based on the spectral information only.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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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.

• 자원환경지질
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• 제28권2호
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• pp.155-164
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• 1995

The kriging model, one of the geostatistical models, has been used to evaluate the deep-sea manganese nodule deposits until now. The distribution of the manganese nodule deposits estimated by the model shows the smooth surface as well as much difference from the actual distribution. Subsequently, it estimates the deposit distribution roughly in terms of the limited data of surveyed zone. Therefore, this paper presents the interpretation methodology of the deep-sea manganese nodule deposit distribution by using the fractal model to overcome the problems caused by the geostatistical model. Also, the manganese nodule distributions are interpreted by using the manganese nodule data sampled in the GH82-4 zone, west longitude $165^{\circ}40^{\prime}-169^{\circ}00^{\prime}$, and south latitude $0^{\circ}00^{\prime}-2^{\circ}40^{\prime}$ neighboring Nova-Canton Trough in the Pacific Ocean which was surveyed by the Geological Survey of Japan in 1982.

• Journal of Ecology and Environment
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• 제37권2호
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• pp.53-60
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• 2014

The arctic tundra is an important ecosystem in terms of the organic carbon cycle and climate change, and therefore, detailed analysis of vegetation distribution patterns is required to determine their association. We used grid-sampling method and applied geostatistics to analyze spatial variability and patterns of vegetation within a two-dimensional space, and calculated the Moran's I statistics and semivariance to assess the spatial autocorrelation of vegetation. Spatially autocorrelated vegetation consisted of moss, Eriophorum vaginatum, Betula nana, and Rubus chamaemorus. Interpolation maps and cross-correlograms revealed spatial specificity of Carex aquatilis and a strong negative spatial correlation between E. vaginatum and C. aquatilis. These results suggest differences between the species in water requirements for survival in the arctic tundra. Geostatistical methods could offer valuable information for identifying the vegetation spatial distribution.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2000년도 추계학술대회
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• pp.15-22
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• 2000

Long ranged groundwater-level data often have the missing intervals because of the trouble of monitoring systems at the national groundwater monitoring wells. Geostatistical methods are very useful for the supplement of the missing data. Ordinary kriging was applied for the interpolation of the missing groundwater-level data with a smooth sinusoidal variation. Conditional simulation was used for the reproduction of the missing data with high fluctuations. Two geostatistical methods produced the very accurate estimates at the missing intervals and reproduced their original variations. This fact is proved by the cross validation test and graphical method, respectively.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2004년도 학술발표회
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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.

• 환경영향평가
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• 제18권1호
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• pp.31-39
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• 2009

A lot of data which are used in environment analysis of air pollution have characteristics that are distributed continuously in space. In this point, the collected data value such as precipitation, temperature, altitude, pollution density, PM10 have spatial aspect. When geostatistical data analysis are needed, acquisition of the value in every point is the best way, however, it is impossible because of the costs and time. Therefore, it is necessary to estimate the unknown values at unsampled locations based on observations. In this study, spatial interpolation method such as local trend surface model, IDW(inverse distance weighted), RBF(radial basis function), Kriging were applied to PM10 annual average concentration of Seoul in 2005 and the accuracy was evaluated. For evaluation of interpolation accuracy, range of estimated value, RMSE, average error were analyzed with observation data. The Kriging and RBF methods had the higher accuracy than others.