• Title/Summary/Keyword: 크리깅모델

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A Study on the Soil Contamination(Maps) Using the Handheld XRF and GIS in Abandoned Mining Areas (휴대용 XRF와 GIS를 이용한 폐광산 지역의 토양오염에 관한 연구)

  • Lee, Hyeon-Gyu;Choi, Yo-Soon
    • Journal of the Korean Association of Geographic Information Studies
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    • v.17 no.3
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    • pp.195-206
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    • 2014
  • In this study, soil contamination maps related to Cu and Pb were created at the Busan abandoned mine in Korea using a handheld X-Ray Fluorescence(XRF) and Geographic Information Systems(GIS). Hydrological analysis was performed using the Digital Elevation Model(DEM) of the study area to identify the flow directions of surface runoff where pollutants can be dispersed from the soil contamination sources. 24 locations for measuring the soil contamination related to Cu and Pb were selected by considering the result of hydrological analysis. The results measured at the 24 locations using the handheld XRF showed that the highest value of Cu contamination is 8,255ppm and that of Pb is 2,146ppm. The field investigation data were entered into ArcGIS software, and then soil contamination maps regarding Cu and Pb with a 5m grid-spacing were created after performing spatial interpolations using the ordinary kriging method. As a result, we could know that high concentrations of Cu and Pb are presented at the waste and tailings dumps around the abandoned mine openings. This study also showed that the handheld XRF and GIS can be utilized to create soil contamination maps related to Cu and Pb in the field.

Distribution of Electrically Conductive Sedimentary Layer in Jeju Island Derived from Magnetotelluric Measurements (MT 탐사자료를 이용한 제주도 지역의 전도성 퇴적층 분포 연구)

  • Lee, Choon-Ki;Lee, Heuisoon;Oh, Seokhoon;Chung, Hojoon;Song, Yoonho;Lee, Tae Jong
    • Geophysics and Geophysical Exploration
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    • v.17 no.1
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    • pp.28-33
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    • 2014
  • We investigate the spatial distribution of highly conductive layer using the one-dimensional inversions of the new magnetotelluric (MT) measurements obtained at the mid-mountain (400 ~ 900 m in elevation) western area of Jeju Island and the previous MT data over Jeju Island, Korea. The conductive layer indicates the sedimentary layer comprised of Seoguipo Fomation and U Formation. There is a definite positive correlation between the top of conductive layer and the earth surface in elevation. On the contrary, the bottom of conductive layer has a negative correlation with the surface elevation. In other words, the conductive layer has a shape of convex lens, which is thickest in the central part. The basement beneath the conductive layer could be concave in the central part of Jeju Island. A kriging considering the correlation between the layer boundary and the surface elevation provides a reliable geoelectric structure model of Jeju Island. However, further studies, i.e. three-dimensional modeling and interpretation integrated with other geophysical or logging data, are required to reveal the possible presence of three-dimensional conductive body near the subsurface vent of Mt. Halla and the causes of the bias in the depths of layer estimated from MT and core log data.

Location Suitability Assessment on Marine Afforestation Using Habitat Evaluation Procedure(HEP) and 3D kriging: A Case Study on Jeju, Korea (서식지 평가법(HEP)과 3D 공간보간법(Kriging)을 이용한 제주도 바다숲 입지적합성 평가)

  • Lee, Jinhyung;Kim, Youngho
    • Journal of the Economic Geographical Society of Korea
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    • v.17 no.4
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    • pp.771-785
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    • 2014
  • As marine desertification and chlorosis in Korean coast have been intensified over time, Korean government is promoting marine afforestation projects. However, marine afforestation location is mainly decided by administrative convenience. Also, there is limited literature on location suitability about the marine afforestation. This study aims to assess location suitability of marine afforestation considering 3 significant criteria: ecological, submarine topographical, and human-social environment. Jeju, the study area of this study, first observed chlorosis in Korean coast at the small fishery town in Seogwipo. Jeju is currently suffering from chlorosis all around the island. Habitat Evaluation Procedure (HEP), 3D kriging, Analytic Hierarchy Process (AHP) is applied as analysis methods. Especially, 3D kriging is utilized for modeling 3D ocean space reflecting ocean environment appropriately. The result shows that Jocheon coast has better location suitability than Seogwipo Pyoseon coast. Jocheon coast has the maximum 61% suitability as the habitat of Ecklonia cava Kjellman, and is highly evaluated in other criteria. The results of this study are expected to find optimal marine afforestation location, and to contribute to the restoration of the Jeju coastal ecosystem and the revitalization of Jeju fishing village societies.

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Deep Learning based Estimation of Depth to Bearing Layer from In-situ Data (딥러닝 기반 국내 지반의 지지층 깊이 예측)

  • Jang, Young-Eun;Jung, Jaeho;Han, Jin-Tae;Yu, Yonggyun
    • Journal of the Korean Geotechnical Society
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    • v.38 no.3
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    • pp.35-42
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    • 2022
  • The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.