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

• Geophysics and Geophysical Exploration
• /
• v.3 no.1
• /
• pp.7-12
• /
• 2000

The paper discusses a data processing methodology that derives a three dimensional porosity volume information from the 3-D seismic dataset. The methodology consists of preprocessing and inversion procedures. The purpose of the preprocessing is balancing the amplitudes of seismic traces by using reflectivity series derived from sonic and density logs. There are eight sonic logs are available in the study area; therefore, we can compute only 8 balance functions. The balance function for every seismic trace was derived from these 8 balance functions by kriging. In order to derive a wide-band acoustic impedance --similar to the one can be derived from a sonic log- from a band-limited reflection seismogram, we need to recover missing low- and high-frequency information of the seismic trace. For that Purpose we use the autoregressive method.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.41-41
• /
• 2021

최근 전 세계적으로 급증하는 기후변화의 영향으로 이상기후로 인한 자연재해들의 강도 및 발생 빈도의 증가가 다양한 연구를 통하여 확인되고 있으며, 이를 대비 및 대응하기 위한 방안수립 연구가 세계의 가장 중요한 주제로 부상되고 있다. 우리나라의 경우에는 기후변화에 따른 심각성 문제가 대두되고 있지만 국가적 대응기반조성 및 수자원정책 의사결정에 직접적으로 활용될 수 있는 일관성 있고 통합적인 기후 정보가 부족한 실정이다. 미래 기상 변동성을 나타내는 기후모델은 전 지구적 대규모 기상장(large scale climate pattern)을 비교적 정확하게 묘사하는 것으로 알려져 있으나 모형에 내재해 있는 시·공간적 편의(spatial-temporal bias) 및 불확실성으로 인하여 통계학적 상세화가 필수적으로 요구된다. 이러한 편향성은 일반적으로 지상 관측 자료를 격자에 보간하여 보정하는 방법이 적용되고 있지만, 관측자료의 불연속성 및 관측소의 불균등성으로 인하여 공간적 신뢰성이 낮다. 이에, 본 연구에서는 Bayesian 기반의 Kriging을 통한 공간적 편의보정 및 QDM(quantile delta mapping)을 연계한 새로운 격자 기반의 통계적 상세화 모형 Bayesian Kriging-QDM을 개발하였다. 본 연구를 통하여 산정된 결과는 과거자료에 근거하여 이루어지는 기존의 보수적인 수자원 관리 체계의 위험성을 저감 시킬 수 있는 의사결정에 직접적으로 활용될 수 있는 기초 자료로 이용 가능할 것으로 판단된다.

• Tunnel and Underground Space
• /
• v.15 no.4 s.57
• /
• pp.275-287
• /
• 2005

At the planning and design stages of a development of underground space or tunneling project, the information regarding ground conditions is very important to enhance economical efficiency and overall safety In general, the information can be expressed using RMR or Q-system and with the geophysical exploration image. RMR or Q-system can provide direct information of rock mass in a local scale for the design scheme. Oppositely, the image of geophysical exploration can provide an exthaustive but indirect information. These two types of the information have inherent uncertainties from various sources and are given in different scales and with their own physical meanings. Recently, RMR has been estimated in unsampled areas based on given data using geostatistical methods like Kriging and conditional simulation. In this study, simulated annealing(SA) is applied to overcome the shortcomings of Kriging methods or conditional simulations just using a primary variable. Using this technique, RMR and the image of geophysical exploration can be integrated to construct the spatial distribution of RM and to evaluate its uncertainty. The SA method was applied to solve an optimization problem with constraints. We have suggested the practical procedure of the SA technique for the uncertainty evaluation of RMR and also demonstrated this technique through an application, where it was used to identify the spatial distribution of RMR and quantify the uncertainty. For a geotechnical application, the objective functions of SA are defined using statistical models of RMR and the correlations between RMR and the reference image. The applicability and validity of this application are examined and then the result of uncertainty evaluation can be used to optimize the tunnel layout.

• Journal of the Economic Geographical Society of Korea
• /
• v.15 no.3
• /
• pp.437-455
• /
• 2012

Korea has recently constructed a number of renewable photovoltaic power plants in Jeolla province as an effort to provide environment-friendly energy. However, several problems appeared in the power plant locations because they were not appropriately chosen ignoring social-environmental perspectives. Consequently, locations of both currently existing photovoltaic power plants require an social and environmental evaluations. This study aims to provide appropriate photovoltaic power plants locations and evaluation of current photovoltaic power plants in Jeolla province. By presenting location analysis of photovoltaic power plants, this study would minimize environmental and social side effects regarding photovoltaic power plants. Kriging and Analytic Network Process (ANP) are applied as methodology. ANP generates correct weights in combining spatial data, so that the result would present optimal locations. In addition environmentally sensitive regions were excluded in the analysis process. The results show that South and West coastal areas have a number of appropriate locations for photovoltaic power plants. In addition, evaluating currently running photovoltaic power plant locations, total 23 out 81 are turned out to be inappropriately located. This study is expected to contribute avoiding social and environmental conflicts in photovoltaic power plant locations and present criteria in evaluating photovoltaic power plants.

• Journal of Korean Society of Transportation
• /
• v.35 no.3
• /
• pp.220-233
• /
• 2017

Traffic volumes are fundamental data widely used in various traffic analysis, such as origin-and-destination establishment, total traveled kilometer distance calculation, congestion evaluation, and so on. The low number of links collecting the traffic-volume data in a large urban highway network has weakened the quality of the analyses in practice. This study proposes a method to estimate the traffic volume data on a highway link where no collection device is available by introducing a spatial statistic technique with (1) the traffic-volume data from TOPIS, and National Transport Information Center in the Ministry of Land, Infrastructure, and (2) the navigation data from private navigation. Two different component models were prepared for the interrupted and the uninterrupted flows respectively, due to their different traffic-flow characteristics: the piecewise constant function and the regression kriging. The comparison of the traffic volumes estimated by the proposed method against the ones counted in the field showed that the level of error includes 6.26% in MAPE and 5,410 in RMSE, and thus the prediction error is 20.3% in MAPE.

• Economic and Environmental Geology
• /
• v.55 no.4
• /
• pp.353-366
• /
• 2022

Spatial estimation of geoscience data (geo-data) is challenging due to spatial heterogeneity, data scarcity, and high dimensionality. A novel spatial estimation method is needed to consider the characteristics of geo-data. In this study, we proposed the application of Gaussian Mixture Model (GMM) among machine learning algorithms with multivariate data for robust spatial predictions. The performance of the proposed approach was tested through soil chemical concentration data from a former smelting area. The concentrations of As and Pb determined by ex-situ ICP-AES were the primary variables to be interpolated, while the other metal concentrations by ICP-AES and all data determined by in-situ portable X-ray fluorescence (PXRF) were used as auxiliary variables in GMM and ordinary cokriging (OCK). Among the multidimensional auxiliary variables, important variables were selected using a variable selection method based on the random forest. The results of GMM with important multivariate auxiliary data decreased the root mean-squared error (RMSE) down to 0.11 for As and 0.33 for Pb and increased the correlations (r) up to 0.31 for As and 0.46 for Pb compared to those from ordinary kriging and OCK using univariate or bivariate data. The use of GMM improved the performance of spatial interpretation of anthropogenic metals in soil. The multivariate spatial approach can be applied to understand complex and heterogeneous geological and geochemical features.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.37 no.8
• /
• pp.1059-1067
• /
• 2013

Automotive fuel tank is generally divided into two parts: main frame and assembly parts. While the car is running, valves are used to prevent liquid carry over and to discharge evaporated gas from the fuel tank. However, current fuel tank designs focus on the gas ventilation or secured location. In this study, the location of the parts used to prevent liquid carry over within the fuel tank is evaluated during an optimal design process. To develop this design process, an approximate optimization is applied. Through the optimal design process, the optimal valve location in fuel tank is determined and the approximate optimization is validated by the Taguchi method. Finally, the optimized valve location is used to reduce the development cost and time and to contribute toward improved automobile quality owing to enhanced reliability.

• The Korean Journal of Applied Statistics
• /
• v.23 no.2
• /
• pp.345-356
• /
• 2010

In this paper, we introduce the linear regression model taking the parametric spatial association structure into account and employ it to five-year averaged wind speed data measured at 460 meteorological monitoring stations in South Korea. From the prediction map obtained by the model with spatial association parameters, we can see that inland area has smaller wind speed than coastal regions. When comparing the spatial linear regression model with classical one by using one-leave-out cross-validation, the former outperforms the latter in terms of similarity between the observations and the corresponding predictions and coverage rate of 95% prediction intervals.

• Geotechnical Engineering
• /
• v.10 no.1
• /
• pp.19-26
• /
• 1994

In this paper, the application of the rock classification method based on indicator kriging and the cost of errors, which can incorporate qualitative data, was presented. In particular, the binary classification of rock masses was considered. To this end, a simplified RMR system was used. Since most of subjectivity in this analysis occur during the estimation of loss functions, a sensitivity analysis of loss functions was performed. Through this research, it was found out that an expected cost of errors could successfully be used as an indication for how well a sampling plan was designed. In certain conditions, qualitative data can be more economical than quantitative data in terms of expected costs of errors and sampling costs. Therefore, an additional sampling should be carefully determined depending upon the surrounding geologic conditions and its sampling cost. The application method shown in this paper can be useful for more systematic rock classifications.