최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기자원환경지질 = Economic and environmental geology, v.55 no.4, 2022년, pp.353 - 366
김호림 (한국지질자원연구원) , 유순영 (고려대학교) , 윤성택 (고려대학교) , 김경호 (한국환경연구원) , 이군택 (서울대학교 NICEM) , 이정호 (한국지질자원연구원) , 허철호 (한국지질자원연구원) , 류동우 (한국지질자원연구원)
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) am...
Babak, O. and Deutsch, C.V. (2009) Collocated cokriging based on merged secondary attributes. Mathematical Geosciences, v.41(8), p.921-926. doi: 10.1007/s11004-008-9192-2
Barnett, R.M. and Deutsch, C.V. (2015) Multivariate imputation of unequally sampled geological variables. Mathematical Geosciences, v.47(7) p.791-817. doi: 10.1007/s11004-014-9580-8
Bergen, K.J., Johnson, P.A., de Hoop, M.V. and Beroza, G.C. (2019) Machine learning for data-driven discovery in solid Earth geoscience. Science, v.363(6433). doi: 10.1126/science.aau0323
Chen, T., Morris, J. and Martin, E. (2006) Probability density estimation via an infinite Gaussian mixture model: application to statistical process monitoring. Journal of the Royal Statistical Society: Series C (Applied Statistics), v.55(5), p.699-715. doi: 10.1111/j.1467-9876.2006.00560.x
Galan, E., Fernandez-Caliani, J.C., Gonzalez, I., Aparicio, P. and Romero, A. (2008) Influence of geological setting on geochemical baselines of trace elements in soils. Application to soils of Southwest Spain. Journal of Geochemical Exploration, v.98(3), p.89-106. doi: 10.1016/j.gexplo.2008.01.001
Goovaerts, P. (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, v.228(1-2), p.113-129. doi: 10.1016/s0022-1694(00)00144-x
Goovaerts, P., AvRuskin, G., Meliker, J., Slotnick, M., Jacquez, G. and Nriagu, J. (2005) Geostatistical modeling of the spatial variability of arsenic in groundwater of southeast Michigan. Water Resources Research, v.41(7). doi: 10.1029/2004wr003705
Grana, D., Fjeldstad, T. and Omre, H. (2017) Bayesian Gaussian mixture linear inversion for geophysical inverse problems. Mathematical Geosciences, v.49, p.493-515. doi: 10.1007/s11004-016-9671-9
Han, H.Y. (2009) Introduction to Pattern Recognition, ISBN-9788979146325(8979146329), (570p).
Herms, I., Jodar, J., Soler, A., Lamban, L.J., Custodio, E., Nunez, J. A., ... and Jorge, J. (2021) Evaluation of natural background levels of high mountain karst aquifers in complex hydrogeological settings. A Gaussian mixture model approach in the Port del Comte (SE, Pyrenees) case study. Science of the Total Environment, v.756. doi: 10.1016/j.scitotenv.2020.143864
ISO (International Organization for Standardization) (1995) ISO 11466:1995 Soil Quality Extraction of Trace Elements Soluble in Aqua Regia.
Kim, E.J., Yoo, J.C., Park, S.M., Park, E.R. and Baek, K. (2016) Distribution of arsenic and heavy metals in soil particle sizes in the areas affected by the former smelter. J. Korean Soc. Environ. Anal., v.19(1), p.54-62.
Kim, H.R., Kim, K.H., Yu, S., Moniruzzaman, M., Hwang, S.I., Lee, G.T. and Yun, S.T. (2019) Better assessment of the distribution of As and Pb in soils in a former smelting area, using ordinary co-kriging and sequential Gaussian co-simulation of portable X-ray fluorescence (PXRF) and ICP-AES data. Geoderma, v.341, p.26-38. doi: 10.1016/j.geoderma.2019.01.031
Kim, H.K., Kim, K.H., Yun, S.T., Oh, J., Kim, H.R., Park, S.H., ... and Kim, T.S. (2019) Probabilistic assessment of potential leachate leakage from livestock mortality burial pits: A supervised classification approach using a Gaussian mixture model (GMM) fitted to a groundwater quality monitoring dataset. Process Safety and Environmental Protection, v.129, p.326-338. doi: 10.1016/j.psep.2019.07.015
Kim, K.H., Yun, S.T., Park, S.S., Joo, Y. and Kim, T.S. (2014) Model-based clustering of hydrochemical data to demarcate natural versus human impacts on bedrock groundwater quality in rural areas, South Korea. Journal of Hydrology, v.519, Part A, p.626-636. doi: 10.1016/j.jhydrol.2014.07.055
Koljonen, T. (1992) Geochemical Atlas of Finland, Part 2. Geological Survey of Finland.
Korea Ministry of Environment (KMOE) (2009a) Counter Measurement Strategies to Remediate Soil Contamination near the Janghang Smelter. Unpublished Report (in Korean).
Korea Ministry of Environment (KMOE) (2009b) Soil Environment Standard Test, Soil Environment Preservation Act. (291p).
Korea Ministry of Environment (KMOE) (2011) Soil Contaminant Risk Assessment Guidance, (139p).
Lee, P.K., Yu, S., Jeong, Y.J., Seo, J., Choi, S.G. and Yoon, B.Y. (2019) Source identification of arsenic contamination in agricultural soils surrounding a closed Cu smelter, South Korea. Chemosphere, v.217, p.183-194. doi: 10.1016/j.chemosphere.2018.11.010
Lemiere, B. (2018) A review of pXRF (field portable X-ray fluorescence) applications for applied geochemistry. Journal of Geochemical Exploration, v.188, p.350-363. doi: 10.1016/j.gexplo.2018.02.006
Qu, J. and Deutsch, C.V. (2018) Geostatistical simulation with a trend using gaussian mixture models. Natural Resources Research, v.27(3), p.347-363. doi: 10.1007/s11053-017-9354-3
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J. and Carvalhais, N. (2019) Deep learning and process understanding for data-driven Earth system science. Nature, v.566, p.195-204. doi: 10.1038/s41586-019-0912-1
Remy, N., Boucher, A. and Wu, J. (2009) Applied geostatistics with SGeMS: A user's guide. Cambridge University Press, (264p).
Ryan, J.G., Shervais, J.W., Li, Y., Reagan, M.K., Li, H.Y., Heaton, D., ... and IODP Expedition 352 Scientific Team (2017) Application of a handheld X-ray fluorescence spectrometer for real-time, high-density quantitative analysis of drilled igneous rocks and sediments during IODP Expedition 352. Chemical Geology, v.451, p.55-66. doi: 10.1016/j.chemgeo.2017.01.007
Ryu, D.W. (2019) New Opportunities and Challenges of Geo-ICT Convergence Technology: GeoCPS and GeoAI. Journal of the Korean Society of Mineral and Energy Resources Engineers, v.56(4), p.383-397. doi: 10.32390/ksmer.2019.56.4.383
Scrucca, L., Fop, M., Murphy, T.B. and Raftery, A.E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. The R Journal, v.8(1), p.289. doi: 10.32614/rj-2016-021
Silva, D.S.F. and Deutsch, C.V. (2018) Multivariate data imputation using Gaussian mixture models. Spatial Statistics, v.26, p.74-90. doi: 10.1016/j.spasta.2016.11.002
US EPA (2007) Method 6200: Field Portable X-ray Fluorescence Spectrometry for the Determination of Elemental Concentrations in Soil and Sediment.
Zhang, X., Chen, N., Chen, Z., Wu, L., Li, X., Zhang, L., Di, L., Gong, J. and Li, D. (2018) Geospatial sensor web: A cyber-physical infrastructure for geoscience research and application. Earth-science Reviews, v.185, p.684-703. doi: j.earscirev.2018.07.006
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.