Optimizing site-specific geostatistics to improve geotechnical spatial information in Seoul, South Korea

  • Han-Saem KimEmail author
  • Hyun-Ki Kim
Original Paper


Subsurface soil and rock profiles are commonly interpreted from borehole log datasets. These datasets include three-dimensional spatial coordinate information, layer information, and standard penetration test results. More reliable spatial distribution of target physical properties can be obtained from additional testing at locations characterized by outlier observations and geotechnical uncertainties. At a given site, irregular measurements typically differ significantly from bulk measurements or proximal observations. In this study, a process for optimizing site-specific geostatistics, which uses geotechnical spatial information and applies optimum outlier thresholds with a multi-clustering method, is proposed to incorporate site-specific geo-layer uncertainties and identify their geotechnical value. Optimized geostatistical characteristic information for geological strata boundaries was derived and verified based on a sequential procedure applied to representative test areas in Seoul, South Korea.


Geotechnical spatial uncertainty Borehole Geostatistical analysis Outlier analyses Clustering 


Funding information

The authors wish to express their gratitude for the support from the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM). This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2012R1A1A1017659).


  1. Anselin L (2004) Exploring spatial data with GeoDaTM: a workbook. Urbana 51(61801):309Google Scholar
  2. Asaoka A, A-Grivas D (1982) Spatial variability of the undrained strength of clays. J Geotech Eng ASCE 108:743–756Google Scholar
  3. Azpurua M, Dos-Ramos K (2016) A comparison of spatial interpolation methods for estimation of average electromagnetic field magnitude. Prog Electromagn Res M 14:135–145CrossRefGoogle Scholar
  4. Barnett V, Lewis T (1994) Outliers in statistical data, 3rd edn. John Wiley & Sons, New YorkGoogle Scholar
  5. Borruso G, Schoier G (2004) Density analysis on large geographical databases. Search for an index of centrality of services at urban scale. Comput Sci Appl 1009–1015Google Scholar
  6. Chandola V, Kumar V (2009) Outlier detection : a survey. ACM Comput Surv 41:1–83CrossRefGoogle Scholar
  7. Chun SH, Sun CG, Chung CK (2005) Application of geostatistical method for geo-layer information. J Korean Soc Civil Eng 25:103–115Google Scholar
  8. David M (1976) The practice of kriging. In: Guarascio M, David M, Huijbregts C (eds) Advanced geostatistics in the mining industry. R. Reidel, Boston, pp 31–48CrossRefGoogle Scholar
  9. Davis LG, Bean DW, Nyers AJ, Brauner DR (2015) GLIMR: a GIS-based method for the geometric morphometric analysis of artifacts. Lithic Technol 40:199–217CrossRefGoogle Scholar
  10. Degroot DJ, Baecher GB (1993) Estimating autocovariance of in-situ soil properties. J Geotech Eng ASCE 119:147–166CrossRefGoogle Scholar
  11. Delfiner P (1976) Linear estimation of nonstationary spatial phenomena. In: Guarascio M, David M, Hujibregts C (eds) Advanced geostatistics in the mining industry. R. Reidel, Boston, pp 49–68CrossRefGoogle Scholar
  12. Deutsch CV, Journel AG (1972) GSLIB: geostatistical software library and user’s guide. Oxford Univ. Press, New YorkGoogle Scholar
  13. Getis A, Ord JK (1996) Local spatial statistics: an overview. In: Longley P, Batty M (eds) Spatial analysis: modeling in a GIS environment. Wiley, New York, pp 261–278Google Scholar
  14. Gökkaya K (2016) Geographic analysis of earthquake damage in Turkey between 1900 and 2012. Geomat Nat Haz Risk 7:1–14Google Scholar
  15. Goovaerts P (1998) Geostatistical tools for characterizing the spatial variability of microbiological and physicochemical soil properties. Biol Fertil Soils 27:315–334CrossRefGoogle Scholar
  16. Goovaerts P (1999) Geostatistics in soil science: state of the art and perspectives. Geoderma 89:1–45CrossRefGoogle Scholar
  17. Goovaerts P (2001) Geostatistical modelling of uncertainty in soil science. Geoderma 103:3–26CrossRefGoogle Scholar
  18. Goovaerts P (2006) Geostatistical analysis of disease data: accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging. Int J Health Geogr 5(1):52CrossRefGoogle Scholar
  19. Grubbs FE (1969) Procedures for detecting outlying observations in samples. Technometrics 11:1–21CrossRefGoogle Scholar
  20. Guarascio M, Huybrechts CJ, David M (1976) Advanced geostatistics in the mining industry. In: Proceedings of the NATO Advanced Study Institute held at the Istituto di Geologia Applicata of the University of Rome, p 24Google Scholar
  21. Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New YorkGoogle Scholar
  22. Kim HS, Kim HK, Shin SY, Chung CK (2012) Application of statistical geo-spatial information technology to soil stratification in the Seoul metropolitan area. Georisk 6:221–228Google Scholar
  23. Kim HS, Chung CK, Kim HK (2016) Geo-spatial data integration for subsurface stratification of dam site with outlier analyses. Env Earth Sci 75:1–10CrossRefGoogle Scholar
  24. Kim HS, Sun CG, Cho HI (2017) Geospatial big data-based geostatistical zonation of seismic site effects in Seoul metropolitan area. ISPRS Int J Geo-Inf 6:174CrossRefGoogle Scholar
  25. Knudsen H, Kim YC (1978) Application of geostatistics to roll front type uranium deposits. Soc. Mining Eng. AIME, Denver, pp 78–94Google Scholar
  26. Kulhawy FH, Birgisson B, Grigoriu MD (1972) Reliability-based foundation design for transmission line structures (No. EPRI-EL-5507-Vol. 4). Electric Power Research Inst., Palo Alto, CA (United States); Cornell Univ., Ithaca, NY (United States). Geotechnical Engineering GroupGoogle Scholar
  27. Lacasse S, Nadim F (1996) Uncertainties in characterising soil properties. In: Uncertainty in the geologic environment: from theory to practice. ASCE, Madison, WI, pp 49–75Google Scholar
  28. Lu C, Chen D, Kou Y (2003) Algorithms for spatial outlier detection. In: Proc. 3rd IEEE Int. Conf. Data-mining (ICDM’03), Melbourne, FLGoogle Scholar
  29. Olea R (1991) Geostatistical glossary and multilingual dictionary. Oxford University Press, New YorkGoogle Scholar
  30. Orr TL, Breysse D (2008) Eurocode 7 and reliability-based design. In: Phoon K-K (ed) Reliability-based design in geotechnical engineering. Taylor and Francis, Oxon, pp 298–343Google Scholar
  31. Öztürk CA, Nasuf E (2002) Geostatistical assessment of rock zones for tunneling. Tunn Undergr Space Technol 17:275–285CrossRefGoogle Scholar
  32. Phoon KK (2008) Reliability-based design in geotechnical engineering: computations and applications. CRC PressGoogle Scholar
  33. Phoon KK, Kulhawy FH (1999) Characterization of geotechnical variability. Can Geotech J 36:612–624CrossRefGoogle Scholar
  34. Prasannakumar V, Vijith H, Charutha R, Geetha N (2011) Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Proc Soc Behav Sci 21:317–325CrossRefGoogle Scholar
  35. Rue H, Follestad T (2003) Gaussian markov random field models with applications in spatial statistics (no. NTNU-S-2003-5), SIS-2003-307Google Scholar
  36. Sun CG, Kim HS (2016) Geostatistical assessment for the regional zonation of seismic site effects in a coastal urban area using a GIS framework. Bull Earthq Eng 14:2161–2183CrossRefGoogle Scholar
  37. Vanmarke EH (1977) Random vibration approach to soil dynamics. The use of probability in earthquake engineering, ASCE, pp 143–176Google Scholar
  38. Yamamoto JK (2005) Correcting the smoothing effect of ordinary kriging estimates. Math Geol 37(1):69–94CrossRefGoogle Scholar
  39. Yu D, Sheikholeslami G, Zhang A (2002) Findout: finding outliers in very large datasets. Knowl Inf Syst 4:387–412CrossRefGoogle Scholar
  40. Zhang Y, Meratnia N, Havinga PJM (2007) A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets. Technical Report TR-CTIT-07-79. Centre for Telematics and Information Technology. University of Twente, EnschedeGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.Earthquake Research CenterKorea Institute of Geoscience and Mineral ResourcesDaejeonSouth Korea
  2. 2.Department of Civil and Environmental EngineeringKookmin UniversitySeoulSouth Korea

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