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

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Abstract

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.

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References

  • Anselin L (2004) Exploring spatial data with GeoDaTM: a workbook. Urbana 51(61801):309

    Google Scholar 

  • Asaoka A, A-Grivas D (1982) Spatial variability of the undrained strength of clays. J Geotech Eng ASCE 108:743–756

    Google Scholar 

  • Azpurua M, Dos-Ramos K (2016) A comparison of spatial interpolation methods for estimation of average electromagnetic field magnitude. Prog Electromagn Res M 14:135–145

    Article  Google Scholar 

  • Barnett V, Lewis T (1994) Outliers in statistical data, 3rd edn. John Wiley & Sons, New York

    Google Scholar 

  • 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 1009–1015

  • Chandola V, Kumar V (2009) Outlier detection : a survey. ACM Comput Surv 41:1–83

    Article  Google Scholar 

  • Chun SH, Sun CG, Chung CK (2005) Application of geostatistical method for geo-layer information. J Korean Soc Civil Eng 25:103–115

    Google Scholar 

  • 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 31–48

    Chapter  Google Scholar 

  • Davis LG, Bean DW, Nyers AJ, Brauner DR (2015) GLIMR: a GIS-based method for the geometric morphometric analysis of artifacts. Lithic Technol 40:199–217

    Article  Google Scholar 

  • Degroot DJ, Baecher GB (1993) Estimating autocovariance of in-situ soil properties. J Geotech Eng ASCE 119:147–166

    Article  Google Scholar 

  • 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 49–68

    Chapter  Google Scholar 

  • Deutsch CV, Journel AG (1972) GSLIB: geostatistical software library and user’s guide. Oxford Univ. Press, New York

    Google Scholar 

  • 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 261–278

    Google Scholar 

  • Gökkaya K (2016) Geographic analysis of earthquake damage in Turkey between 1900 and 2012. Geomat Nat Haz Risk 7:1–14

    Google Scholar 

  • Goovaerts P (1998) Geostatistical tools for characterizing the spatial variability of microbiological and physicochemical soil properties. Biol Fertil Soils 27:315–334

    Article  Google Scholar 

  • Goovaerts P (1999) Geostatistics in soil science: state of the art and perspectives. Geoderma 89:1–45

    Article  Google Scholar 

  • Goovaerts P (2001) Geostatistical modelling of uncertainty in soil science. Geoderma 103:3–26

    Article  Google Scholar 

  • 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):52

    Article  Google Scholar 

  • Grubbs FE (1969) Procedures for detecting outlying observations in samples. Technometrics 11:1–21

    Article  Google Scholar 

  • 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 24

  • Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New York

    Google Scholar 

  • 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 6:221–228

    Google Scholar 

  • Kim HS, Chung CK, Kim HK (2016) Geo-spatial data integration for subsurface stratification of dam site with outlier analyses. Env Earth Sci 75:1–10

    Article  Google Scholar 

  • 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 6:174

    Article  Google Scholar 

  • Knudsen H, Kim YC (1978) Application of geostatistics to roll front type uranium deposits. Soc. Mining Eng. AIME, Denver, pp 78–94

    Google Scholar 

  • 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 Group

  • Lacasse S, Nadim F (1996) Uncertainties in characterising soil properties. In: Uncertainty in the geologic environment: from theory to practice. ASCE, Madison, WI, pp 49–75

  • Lu C, Chen D, Kou Y (2003) Algorithms for spatial outlier detection. In: Proc. 3rd IEEE Int. Conf. Data-mining (ICDM’03), Melbourne, FL

  • Olea R (1991) Geostatistical glossary and multilingual dictionary. Oxford University Press, New York

    Google Scholar 

  • 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 298–343

    Google Scholar 

  • Öztürk CA, Nasuf E (2002) Geostatistical assessment of rock zones for tunneling. Tunn Undergr Space Technol 17:275–285

    Article  Google Scholar 

  • Phoon KK (2008) Reliability-based design in geotechnical engineering: computations and applications. CRC Press

  • Phoon KK, Kulhawy FH (1999) Characterization of geotechnical variability. Can Geotech J 36:612–624

    Article  Google Scholar 

  • Prasannakumar V, Vijith H, Charutha R, Geetha N (2011) Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Proc Soc Behav Sci 21:317–325

    Article  Google Scholar 

  • Rue H, Follestad T (2003) Gaussian markov random field models with applications in spatial statistics (no. NTNU-S-2003-5), SIS-2003-307

  • 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 14:2161–2183

    Article  Google Scholar 

  • Vanmarke EH (1977) Random vibration approach to soil dynamics. The use of probability in earthquake engineering, ASCE, pp 143–176

  • Yamamoto JK (2005) Correcting the smoothing effect of ordinary kriging estimates. Math Geol 37(1):69–94

    Article  Google Scholar 

  • Yu D, Sheikholeslami G, Zhang A (2002) Findout: finding outliers in very large datasets. Knowl Inf Syst 4:387–412

    Article  Google Scholar 

  • 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, Enschede

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Funding

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).

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Correspondence to Han-Saem Kim.

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Kim, HS., Kim, HK. Optimizing site-specific geostatistics to improve geotechnical spatial information in Seoul, South Korea. Arab J Geosci 12, 104 (2019). https://doi.org/10.1007/s12517-018-4171-5

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  • DOI: https://doi.org/10.1007/s12517-018-4171-5

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