Geomechanics and Engineering

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Three-dimensional geostatistical modeling of subsurface stratification and SPT-N Value at dam site in South Korea

  • Mingi Kim (Division of Urban Infrastructure Research, Seoul Institute of Technology) ;
  • Choong-Ki Chung (Department of Civil Environmental Engineering, Seoul National University) ;
  • Joung-Woo Han (Department of Civil Environmental Engineering, Seoul National University) ;
  • Han-Saem Kim (Earthquake Research Center, Korea Institute of Geoscience and Mineral Resources)
  • Received : 2022.03.22
  • Accepted : 2023.05.10
  • Published : 2023.07.10
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Abstract

The 3D geospatial modeling of geotechnical information can aid in understanding the geotechnical characteristic values of the continuous subsurface at construction sites. In this study, a geostatistical optimization model for the three-dimensional (3D) mapping of subsurface stratification and the SPT-N value based on a trial-and-error rule was developed and applied to a dam emergency spillway site in South Korea. Geospatial database development for a geotechnical investigation, reconstitution of the target grid volume, and detection of outliers in the borehole dataset were implemented prior to the 3D modeling. For the site-specific subsurface stratification of the engineering geo-layer, we developed an integration method for the borehole and geophysical survey datasets based on the geostatistical optimization procedure of ordinary kriging and sequential Gaussian simulation (SGS) by comparing their cross-validation-based prediction residuals. We also developed an optimization technique based on SGS for estimating the 3D geometry of the SPT-N value. This method involves quantitatively testing the reliability of SGS and selecting the realizations with a high estimation accuracy. Boring tests were performed for validation, and the proposed method yielded more accurate prediction results and reproduced the spatial distribution of geotechnical information more effectively than the conventional geostatistical approach.

Keywords

Acknowledgement

This research was supported by a grant (21SCIP-B119958-06) from the Smart Civil Infrastructure Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean government and the Institute of Construction and Environmental Engineering at Seoul National University. It was also supported by and Daelim Suam Scholarship Culture Foundation and the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources. The authors would like to thank Heesong Geotek Company, Korea, for their contribution to this research.

References

  1. ASTM (2002), Standard Test Method for Penetration Test and Splitbarrel Sampling of Soils, American Society of Testing and Materials, Philadelphia, PA, USA.
  2. ASTM (2019), Standard Guide for Contents of Geostatistical Site Investigation Report, American Society of Testing and Materials: Philadelphia, PA, USA.
  3. Boschetti, F., Dentith, M.C. and List, R.D. (1996), "Inversion of seismic refraction data using genetic algorithms", Geophysics, 61(6), 1715-1727. https://doi.org/10.1190/1.1444089.
  4. Chen, G., Zhu, J., Qiang, M. and Gong, W. (2018), "Threedimensional site characterization with borehole data-a case study of Suzhou area", Eng. Geol., 234, 65-82. https://doi.org/10.1016/j.enggeo.2017.12.019.
  5. Ching, J. and Phoon, K.K. (2014), "Correlations among some clay parameters-the multivariate distribution", Can. Geotech. J., 51, 686-704. https://doi.org/10.1139/cgj-2013-0353.
  6. Daya, A.A. and Bejari, H. (2015), "A comparative study between simple kriging and ordinary kriging for estimating and modeling the Cu concentration in Chehlkureh deposit, SE Iran", Arabian J. Geosci., 8(8), 6003-6020. https://doi.org/10.1007/s12517-014-1618-1.
  7. De-fu, C., Li-xin, W. and Zuo-ru, Y. (2008), "3D Urban Geological Modeling and its Application in China: Technologies and Developments", Proceedings of the IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA, July.
  8. De Rienzo, F., Oreste, P. and Pelizza, S. (2008), "Subsurface geological-geotechnical modelling to sustain underground civil planning", Eng. Geol., 96(3-4), 187-204. https://doi.org/10.1016/j.enggeo.2007.11.002.
  9. Delfiner, P. (1976), "Linear estimation of non stationary spatial phenomena", Advanced geostatistics in the mining industry, Rome, Italy, October.
  10. Gallerini, G. and De Donatis, M. (2009), "3D modeling using geognostic data: The case of the low valley of Foglia river (Italy)", Comput. Geosci., 35, 146-164. https://doi.org/10.1016/j.cageo.2007.09.012.
  11. Gomez-Hernandez, J.J. and Srivastava, R.M. (1990), "ISIM3D: An ANSI-C three-dimensional multiple indicator conditional simulation program", Comput. Geosci., 16, 395-440. https://doi.org/10.1016/0098-3004(90)90010-Q.
  12. Guarascio, M., David, M. and Huijbregts, C. (1976). Advanced geostatistics in the mining industry, D. Proc. of the NATO ASI, Reidel Publ., Dordrecht, The Netherlands.
  13. Haeni, F. (1986), "Application of seismic refraction methods in groundwater modeling studies in New England", Geophysics, 51, 236-249. https://doi.org/10.1190/1.1442083.
  14. Han, L., Wang, L., Ding, X., Wen, H., Yuan, X. and Zhang, W. (2022), "Similarity quantification of soil parametric data and sites using confidence ellipses", Geosci. Front., 13(1), https://doi.org/10.1016/j.gsf.2021.101280.
  15. Han, L., Wang, L., Zhang, W., Geng, B. and Li, S. (2021), "Rockhead profile simulation using an improved generation method of conditional random field", J. Rock Mech. Geotech. Eng., 14(3), 896-908, https://doi.org/10.1016/j.jrmge.2021.09.007.
  16. Huang, L., Cheng, Y.M., Leung, Y.F. and Li, L. (2019), "Influence of rotated anisotropy on slope reliability evaluation using conditional random field", Comput. Geotech., 115, 103-133. https://doi.org/10.1016/j.compgeo.2019.103133.
  17. Isaaks, E. and Srivastava, R. (1989), An introduction to applied geostatistics, Oxford University Press, New York, NY, USA.
  18. Kim, H.S., Kim, H.K., Shin, S.Y. and Chung, C.K. (2012), "Application of statistical geo-spatial information technology to soil stratification in the Seoul metropolitan area", Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 6(4), 221-228. https://doi.org/10.1080/17499518.2012.744248.
  19. Kim, H.S., Chung, C.K. and Kim, H.K. (2016), "Geo-spatial data integration for subsurface stratification of dam site with outlier analyses", Environ. Earth Sci., 75, 168. https://doi.org/10.1007/s12665-015-4931-4.
  20. Kim, M. (2020), "Optimization of Three-dimensional geostatistical integration of site investigation information", Ph.D. Dissertation, Seoul National University, Seoul, South Korea.
  21. Kim, M., Kim, H.S. and Chung, C.K. (2020), "A threedimensional geotechnical spatial modeling method for borehole dataset using optimization of geostatistical approaches", KSCE J. Civil Eng., 24, 778-793. https://doi.org/10.1007/s12205-020-1379-1.
  22. Kocbay, A. and Kilic, R. (2006), "Engineering geological assessment of the Obruk dam site (Corum, Turkey)", Eng. Geol., 87(3-4), 141-148. https://doi.org/10.1016/j.enggeo.2006.04.005.
  23. Koltermann, C.E. and Gorelick, S.M. (1996), "Heterogeneity in sedimentary deposits: A review of structure-imitating, process- imitating, and descriptive approaches", Water Resour. Res., 32, 2617-2658. https://doi.org/10.1029/96WR00025.
  24. Kupfersberger, H. and Deutsch, C.V. (1999), "Methodology for integrating analog geologic data in 3-D variogram modeling", AAPG bulletin, 83, 1262-1278.
  25. Leuangthong, O., McLennan, J.A. and Deutsch, C.V. (2004), "Minimum acceptance criteria for geostatistical realizations", Nat. Resour. Res., 13(3), 131-141. https://doi.org/10.1023/B:NARR.0000046916.91703.bb.
  26. Liu, D., Liu, H., Wu, Y., Zhang, W., Wang, Y. and Santosh, M. (2022), "Characterization of geo-material parameters: Gene concept and big data approach in geotechnical engineering", Geosyst. Geoenviron., 1(1), https://doi.org/10.1016/j.geogeo.2021.09.003.
  27. Li, X., Cheng, G. and Lu, L. (2000), "Comparison of spatial interpolation methods", Adv. Earth Sci., 15(3), 260-265. https://doi.org/10.11867/j.issn.1001-8166.2000.03.0260.
  28. Li, J., Cassidy, M.J., Huang, J., Zhang, L. and Kelly, R. (2016), "Probabilistic identification of soil stratification", Geotechnique, 66(1), 16-26. https://doi.org/10.1680/jgeot.14.P.242.
  29. Murakami, S., Yasuhara, K., Suzuki, K. and Komine, H. (2006), "Reliable land subsidence mapping using a spatial interpolation procedure based on geostatistics", Soils Found., 46(2), 123-134. https://doi.org/10.3208/sandf.46.123.
  30. Oh, S., Chung, H. and Lee, D.K. (2004), "Geostatistical integration of MT and borehole data for RMR evaluation", Environ. Geol., 46, 1070-1078. https://doi.org/10.1007/s00254-004-1115-z.
  31. Osterholt, V. and Dimitrakopoulos, R. (2018), Simulation of orebody geology with multiple-point geostatistics-application at Yandi channel iron ore deposit, WA, and implications for resource uncertainty, In Advances in applied strategic mine planning (pp. 335-352). Springer, Cham.
  32. Phoon, K.-K., Ching, J. and Shuku, T. (2021), "Challenges in data-driven site characterization", Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 1(13), https://doi.org/10.1080/17499518.2021.1896005.
  33. Phoon, K.K. and Kulhawy, F.H. (1999), "Characterization of geotechnical variability", Can. Geotech. J., 36(4), 612-624. https://doi.org/10.1139/t99-038.
  34. Pinheiro, M., Vallejos, J., Miranda, T. and Emery, X. (2016), "Geostatistical simulation to map the spatial heterogeneity of geomechanical parameters: A case study with rock mass rating", Eng. Geol., 205, 93-103. https://doi.org/10.1016/j.enggeo.2016.03.003.
  35. Seoul Metropolis (2006), Geotechnical investigation handbook, Seoul Metropolis, Seoul.
  36. Sotiropoulos, N., Benardos, A. and Mavrikos, A. (2016), "Spatial modelling for the assessment of geotechnical parameters", Procedia Eng., 165, 334-342. https://doi.org/10.1016/j.proeng.2016.11.708.
  37. Wackernagel, H. (2003), Multivariate Geostatistics: An Introduction with Applications, Springer Science & Business Media.
  38. Wang, J., Schweizer, D., Liu, Q., Su, A., Hu, X. and Blum, P. (2021), "Three-dimensional landslide evolution model at the Yangtze River", Eng. Geol., 292, 106275. https://doi.org/10.1016/j.enggeo.2021.106275.
  39. Webster, R., Oliver, M.A. (2007), Geostatistics for environmental scientists, John Wiley & Sons.
  40. Weissmann, G.S., Carle, S.F. and Fogg, G.E. (1999), "Three- dimensional hydrofacies modeling based on soil surveys and transition probability geostatistics", Water Resour. Res., 35, 1761-1770. https://doi.org/10.1029/1999WR900048.
  41. Xu, J., Zhang, L., Li, J., Cao, Z., Yang, H. and Chen, X. (2021), "Probabilistic estimation of variogram parameters of geotechnical properties with a trend based on Bayesian inference using Markov chain Monte Carlo simulation", Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 15(2), 83-97. https://doi.org/10.1080/17499518.2020.1757720.
  42. Zhao, T. and Wang, Y. (2020), "Non-parametric simulation of non-stationary non-gaussian 3D random field samples directly from sparse measurements using signal decomposition and Markov Chain Monte Carlo (MCMC) simulation", Reliab. Eng. Syst. Saf., 203. https://doi.org/10.1016/j.ress.2020.107087.
  43. Zhang, J., Huang, H.W. and Phoon, K.K. (2013), "Application of the Kriging-Based Response Surface Method to the System Reliability of Soil Slopes", J. Geotech. Geoenviron. Eng., 139(4), 651-655. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000801.
  44. Zelt, C.A. and Barton, P.J. (1998), "Three-dimensional seismic refraction tomography: A comparison of two methods applied to data from the Faeroe Basin", J. Geophys.Res.: Solid Earth, 103, 7187-7210. https://doi.org/10.1029/97JB03536.