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http://dx.doi.org/10.7843/kgs.2021.37.5.47

MLP-based 3D Geotechnical Layer Mapping Using Borehole Database in Seoul, South Korea  

Ji, Yoonsoo (Spatial Information Research Institute, LX)
Kim, Han-Saem (Earthquake Research Center, Korea Institute of Geoscience and Mineral Resources)
Lee, Moon-Gyo (Earthquake Research Center, Korea Institute of Geoscience and Mineral Resources)
Cho, Hyung-Ik (Earthquake Research Center, Korea Institute of Geoscience and Mineral Resources)
Sun, Chang-Guk (Earthquake Research Center, Korea Institute of Geoscience and Mineral Resources)
Publication Information
Journal of the Korean Geotechnical Society / v.37, no.5, 2021 , pp. 47-63 More about this Journal
Abstract
Recently, the demand for three-dimensional (3D) underground maps from the perspective of digital twins and the demand for linkage utilization are increasing. However, the vastness of national geotechnical survey data and the uncertainty in applying geostatistical techniques pose challenges in modeling underground regional geotechnical characteristics. In this study, an optimal learning model based on multi-layer perceptron (MLP) was constructed for 3D subsurface lithological and geotechnical classification in Seoul, South Korea. First, the geotechnical layer and 3D spatial coordinates of each borehole dataset in the Seoul area were constructed as a geotechnical database according to a standardized format, and data pre-processing such as correction and normalization of missing values for machine learning was performed. An optimal fitting model was designed through hyperparameter optimization of the MLP model and model performance evaluation, such as precision and accuracy tests. Then, a 3D grid network locally assigning geotechnical layer classification was constructed by applying an MLP-based bet-fitting model for each unit lattice. The constructed 3D geotechnical layer map was evaluated by comparing the results of a geostatistical interpolation technique and the topsoil properties of the geological map.
Keywords
3D mapping; Borehole database; Engineering strata classification; Geospatial modeling; Machine learning;
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1 Pan, X., Guo, W., Aung, Z., Nyo, A. K., Chiam, K., Wu, D., and Chu, J. (2018), "Procedure for Establishing a 3D Geological Model for Singapore", GeoShanghai International Conference, Springer, Singapore, pp.81-89.
2 Ray, P., Manach, Y. L., Riou, B., Houle, T. T., and Warner, D.S. (2010), "Statistical Evaluation of a Biomarker", The Journal of the American Society of Anesthesiologists, Vol.112, No.4, pp.1023-1040.
3 Sullivan, C. B. and Kaszynski, A. A. (2019), "PyVista: 3D Plotting and Mesh Analysis through a Streamlined Interface for the Visualization Toolkit (VTK)", Journal of Open Source Software, Vol.4, No.37, 1450.   DOI
4 Sun, C. G. and Kim, H. S. (2016), "Site Classification for Incheon According to Site-Specific Seismic Response Parameters by Estimating Geotechnical Spatial Information Based on GIS", Journal of the Korea Association of Geographic Information Studies, Vol.19, No. 4, pp.1-15.   DOI
5 Thornton, J. M., Mariethoz, G., and Brunner, P. (2018), "A 3D Geological Model of a Structurally Complex Alpine Region as a Basis for Interdisciplinary Research", Scientific Data, Vol.5, No.1, pp.1-20.   DOI
6 Wang, Z., Shi, W., Zhou, W., Li, X., and Yue, T. (2020), "Comparison of Additive and Isometric Log-ratio Transformations Combined with Machine Learning and Regression Kriging Models for Mapping Soil Particle Size Fractions", Geoderma, Vol.365, 114214.   DOI
7 Sun, C. G., Kim, H. J., Jung, J. H., and Jung, G. J. (2006), "Synthetic Application of Seismic Piezo-cone Penetration Test for Evaluating Shear Wave Velocity Korean Soil Deposits", Mulli-Tamsa, Vol.9, No.3, pp.207-224.
8 Ayachit, U. (2015), "The ParaView Guide: A Parallel Visualization Application", Kitware, Inc.: USA.
9 Bressan, T. S., Kehl de Souza, M., Girelli, T. J., and Junior, F. C. (2020), "Evaluation of Machine Learning Methods for Lithology Classification Using Geophysical Data", Computers and Geosciences., Vol.139, 104475.   DOI
10 Chen, Q., Mariethoz, G., Liu, G., Comunian, A., and Ma, X. (2019), "Locality-based 3-D Multiple-point Statistics Reconstruction Using 2-D Geological Cross-sections", Hydrology and Earth System Sciences, Vol.22, pp.6547-6566.   DOI
11 Chun, C., Choi, C., and Cho, J. (2019), "Comparison of Ordinary Kriging and Artificial Neural Network for Estimation of Ground Profile Information in Unboring Region", Journal of the Korean Geo-Environmental Society, Vol.20, No.3, pp.15-20.
12 Costa, I., Tavares, F., and Oliveira, J. (2019), "Predictive Lithological Mapping through Machine Learning Methods: A Case Study in the Cinzento Lineament, Carajas Province, Brazil", Journal of the Geological Survey of Brazil, Vol.2, No.1, pp.26-36.   DOI
13 Fawcett, T. (2006), "An Introduction to ROC Analysis", Pattern recognition letters, Vol.27, No.8, pp.861-874.   DOI
14 Fuentes, I., Padarian, J., Iwanaga, T., and Willem Vervoort, R. (2020), "3D Lithological Mapping of Borehole Descriptions Using Word Embeddings", Computers & Geosciences., Vol.141, 104516.   DOI
15 Greve, M. H., Kheir, R. B., Greve, M. B., and Bocher, P. K. (2012), "Using Digital Elevation Models as an Environmental Predictor for Soil Clay Contents", Soil Science Society of America Journal, Vol.76, No.6, pp.2116-2127.   DOI
16 Hanley, J. A. and McNeil, B. J. (1982), "The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve", Radiology, Vol.143, No.1, pp.29-36.   DOI
17 Entwisle, D. (2019), "3D Geological Modelling at the British Geological Survey" (BGS), NTU-BCA Workshop on 3D Geological Modelling, BCA Academy, Building and Construction Authority, Singapore.
18 Kim, H. S., Sun, C. G., and Cho, H. I. (2017), "Geospatial Big DataBased Geostatistical Zonation of Seismic Site Effects in Seoul Metropolitan Area", ISPRS International Journal of Geo-Information, Vol.6, No.6, pp.174.   DOI
19 Kim, H. R., Kim, K. H., Yun, S. T., Hwang, S. I., Kim, H. D., Lee, G. T., and Kim, Y. J. (2012), "Evaluation of Geostatistical Approaches for better Estimation of Polluted Soil Volume with Uncertainty Evaluation", Journal of Soil and Groundwater Environment, Vol.17, No.6, pp.69-81.   DOI
20 Kim, H. S., Chung, C. K., and Kim, J. J. (2018), "Three-dimensional Geostatistical Integration of Borehole and Geophysical Datasets in Developing Geological Unit Boundaries for Geotechnical Investigations", Quarterly Journal of Engineering Geology and Hydrogeology, Vol.51, No.1, pp.79-95.   DOI
21 Kim, H. S., Sun, C. G., Cho, H. I., and Nam, J. H. (2018), "Regional Assessment of Seismic Site Effects and Induced Vulnerable Area in Gyeonggi-do, South Korea, Using GIS", Journal of the Korean Geotechnical Society, Vol.34, No.5, pp.19-35.   DOI
22 Kim, J., Lim, H. S., and Nahm, W. -H. (2019), "Three-dimensional Modelling of Urban Area based on the Urban Geological Approach", Journal of the Geological Society of Korea, Vol.55, No.3, pp. 333-342.   DOI
23 McBratney, A. B., Santos, M. M., and Minasny, B. (2003), "On digital soil mapping", Geoderma, Vol.117, No.1-2, pp.3-52.   DOI
24 Mohamed, I. M., Mohamed, S., Mazher, I., and Chester, P. (2019), "Formation Lithology Classification: Insights into Machine Learning Methods", Proc. - SPE Annu. Tech. Conf. Exhib. 2019-September.
25 Osterholt, V. and Dimitrakopoulos, R. (2007), "Simulation of Orebody Geology with Multiple-point Geostatistics: Application at Yandi Channel Iron Ore Deposit, WA and Implications for Resource Uncertainty", Orebody modelling and strategic mine planning, The Australasian Institute of Mining and Metallurgy, pp.51-60.
26 Kim, H. S., Chung, C. K., and Kim, H. K. (2016), "Geo-spatial Data Integration for Subsurface Stratification of Dam Site with Outlier Analyses", Environmental Earth Sciences, Vol.75, No.2, pp.168.   DOI
27 Korea Institute of Geoscience and Mineral Resources (2009), Development on Technology of the Real Time Seismic Monitoring and the Seismic Hazard Prediction at Metropolitan Areas.
28 Getis, A. and J. K. Ord. (1992), "The Analysis of Spatial Association by Use of Distance Statistics", Geographical Analysis, Vol.24, No.3, pp.189-206.   DOI