• Korean Journal of Soil Science and Fertilizer
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• v.17 no.4
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• pp.319-324
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• 1984

Spatial variability of sand, silt, and clay contents on Hwadong SiCL and Jungdong SL was studied by using geostatistical concept. The measurements were made within a $33{\times}14m^2$ area at the nodes of 2 by 2m grids. The validity of all assumptions (stationarity, variogram models, etc.) was proved by Jack-knifing procedure and frequency distribution performed on the original data grids. The variogram of sand content on Hwadong SiCL was different from the linear model and that of clay content of Jungdong SL the linear and the spherical model in calculation of both kriged values and kriged variances in identification of its choice for simplicity.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.160-165
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• 2008

Kriging interpolation technique has been proposed by Danny Krige of South Africa to find the mineral distribution grade from information of geography and space. It is one of the generally used prediction technique for the mineral distribution grade and underground water level in wide scope also used in computer graphics fields by the ability for the surface regeneration This paper comprises two specific objectives. The first is to examine the applicability of Ordinary Kriging interpolation(OK) to finite element method that is based on variogram modeling in conjunction with different allowable limits of separation distance. The second is to investigate the accuracy according to theoretical variogram such as polynomial, Gauss, and spherical models.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.04a
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• pp.82-85
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• 2003

강원도 춘천시 신북읍 유포리 연구지역의 지하수의 NO$_3$-N 2차원 공간 분포를 정의하기 위하여 지구통계학적 해석 방법인 sequential Gaussian simulation(SGS)을 이용하였다. 원자료의 공간적 clustering을 제거하기 위하여 cell declustering을 수행한 후 normal score 변환을 거친 후 variogram 분석과 모델링을 수행하였다. Exponential, gaussian, spherical variogram model에 대한 각각의 nugget, range, sill을 정의하여 SGS에 이용하였다. SGS에 의해 도출된 결과들은 모두 동일한 결과를 나타낸다. 또한 관측 자료의 분포와 주 오염원의 분포와 상응하는 모델링 결과를 나타내는 것으로 보아 SGS를 이용한 농촌지역 지하수내 NO$_3$-N의 공간적 오염 분포 영상화가 매우 유용하게 활용될 수 있을 것으로 판단된다.

• Journal of Biosystems Engineering
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• v.39 no.4
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• pp.377-388
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• 2014

Purpose: Determining the spatial structure of data is important in understanding within-field variability for site-specific crop management. An understanding of the spatial structures present in the data may help illuminate interrelationships that are important in subsequent explanatory analyses, especially when site variables are correlated or are a combined response to multiple causative factors. Methods: In this study, correlation, principal component analysis, and single and nested variogram models were applied to soil electrical conductivity and chemical property data of two fields in central Missouri, USA. Results: Some variables that were highly correlated, or were strongly expressed in the same principal component, exhibited similar spatial ranges when fitted with a single variogram model. However, single variogram results were dependent on the active lag distance used, with short distances (30 m) required to fit short-range variability. Longer active lag distances only revealed long-range spatial components. Nested models generally yielded a better fit than single models for sensor-based conductivity data, where multiple scales of spatial structure were apparent. Gaussian-spherical nested models fit well to the data at both short (30 m) and long (300 m) active lag distances, generally capturing both short-range and long-range spatial components. As soil conductivity relates strongly to profile texture, we hypothesize that the short-range components may relate to the scale of erosion processes, while the long-range components are indicative of the scale of landscape morphology. Conclusion: In this study, we investigated the effect of changing active lag distance on the calculation of the range parameter. Future work investigating scale effects on other variogram parameters, including nugget and sill variances, may lead to better model selection and interpretation. Once this is achieved, separation of nested spatial components by factorial kriging may help to better define the correlations existing between spatial datasets.

• Korean Journal of Remote Sensing
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• v.36 no.1
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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.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.331-341
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• 2023

When creating a soil contamination map using geostatistical techniques, there are various sources that can affect prediction errors. In this study, a grid-based soil contamination map was created from the sampling data of heavy metal concentrations in soil in abandoned mine areas using Ordinary Kriging. Five factors that were judged to affect the prediction error of the soil contamination map were selected, and the variation of the root mean squared error (RMSE) between the predicted value and the actual value was analyzed based on the Leave-one-out technique. Then, using a machine learning algorithm, derived the top three factors affecting the RMSE. As a result, it was analyzed that Variogram Model, Minimum Neighbors, and Anisotropy factors have the largest impact on RMSE in the Standard interpolation. For the variogram models, the Spherical model showed the lowest RMSE, while the Minimum Neighbors had the lowest value at 3 and then increased as the value increased. In the case of Anisotropy, it was found to be more appropriate not to consider anisotropy. In this study, through the combined use of geostatistics and machine learning, it was possible to create a highly reliable soil contamination map at the local scale, and to identify which factors have a significant impact when interpolating a small amount of soil heavy metal data.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.3
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• pp.295-304
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• 2008

This paper comprises two specific objectives. The first is to examine the applicability of Ordinary Kriging interpolation(OK) to finite element method that is based on variogram modeling in conjunction with different allowable limits of separation distance. The second is to investigate the accuracy according to theoretical variograms such as polynomial, Gauss, and spherical models. For this purpose, the weighted least square method is applied to obtain the estimated new stress field from the stress data at the Gauss points. The weight factor is determined by experimental and theoretical variograms for interpolation of stress data apart from the conventional interpolation methods that use an equal weight factor. The validity of the proposed approach has been tested by analyzing two numerical examples. It is noted that the numerical results by Gauss model using 25% allowable limit of separation distance show an excellent agreement with theoretical solutions in literature.

• Korean Journal of Soil Science and Fertilizer
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• v.17 no.3
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• pp.212-217
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• 1984

Spatial variabilities of particle size distribution of 96 samples from Hwadong SiCL and Jungdong Sl were studied by using geostatistical concepts. The measurement was made at the nodes of the regular grid consisting of 12 rows and 8 columns. Sample spacing within rows and columns was 3 and 2 meters, respectively. The results are summarized as follows. 1. Variograms of Hwadong SiCL were fitted for the linear model and those of Jungdong SL for the spherical model. 2. Variograms of properties for Hwadong and clay for Jungdong showed the pure nugget effect. Those of silt and clay for Jungdong, however, appeared the nugget effect. 3. The minimum number of samples necessary to reproduce results similar to the true mean of the 96 measured values was approximately estimated. The minimum sample sizes of silt, clay, and sand in Hwadong SiCL were 27, 13, and 6, respectively. And the minimum sample size of clay in Jungdong SL was 17. 4. The approximate number of samples required to detect the difference of 5% of the true mean with 0.95 confidence level was estimated. The resulting number of samples for silt and sand in Jungdong was 14, and 26, respectively.

• Geotechnical Engineering
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• v.14 no.1
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• pp.29-36
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• 1998

The application of a multiple rock classification method, which is a generalization of a binary rock classification, is studied in this paper. In particular, this paper shows how to incorporate qualitative data through a case study. The method suggested in this paper can be effectively used for a systematic multiple rock classification such as RMR system developed by Bieniawski. It will be very useful for rock classifications. In addition, it is known that the expected cost of errors can be atopted to indicate how well a investigation plan is made.

• Journal of the Korean Geotechnical Society
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• v.38 no.10
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• pp.17-29
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• 2022

Recently, the interest in integrated underground geospatial information mapping (IUGIM) to ensure the safety of underground spaces and facilities has been increasing. Because IUGIM is used in the fields of underground space development and underground safety management, the up-to-dateness and accuracy of information are critical. In this study, IUGIM and field data were classified, and the accuracy of IUGIM was improved by spatial interpolation. A spatial interpolation technique was used to process borehole data in IUGIM, and a quantitative evaluation was performed with mean absolute error and root mean square error through the cross-validation of seven interpolation results according to the technique and model. From the cross-validation results, accuracy decreased in the order of nonuniform rational B-spline, Kriging, and inverse distance weighting. In the case of Kriging, the accuracy difference according to the variogram model was insignificant, and Kriging using the spherical variogram exhibited the best accuracy.