1,529
Views
5
CrossRef citations to date
0
Altmetric
Articles

Machine learning of geological details from borehole logs for development of high-resolution subsurface geological cross-section and geotechnical analysis

ORCID Icon, &
Pages 2-20 | Received 13 Feb 2021, Accepted 26 Jul 2021, Published online: 30 Sep 2021

References

  • Abdulla, M. B., R. L. Sousa, H. Einstein, and S. Awadalla. 2019. “Optimised Multivariate Gaussians for Probabilistic Subsurface Characterisation.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 13 (4): 303–312.
  • Beer, M., Y. Zhang, S. T. Quek, and K. K. Phoon. 2013. “Reliability Analysis with Scarce Information: Comparing Alternative Approaches in a Geotechnical Engineering Context.” Structural Safety 41: 1–10.
  • Bogusz, W., and T. Godlewski. 2019. “Philosophy of Geotechnical Design in Civil Engineering–Possibilities and Risks.” Bulletin of the Polish Academy of Sciences: Technical Sciences 67 (2): 289–306.
  • Bossi, G., L. Borgatti, G. Gottardi, and G. Marcato. 2016. “The Boolean Stochastic Generation Method-BoSG: A Tool for the Analysis of the Error Associated with the Simplification of the Stratigraphy in Geotechnical Models.” Engineering Geology 203: 99–106.
  • Ching, J., and Y. G. Hu. 2016. “Effect of Element Size in Random Finite Element Analysis for Effective Young’s Modulus.” Mathematical Problems in Engineering 2016: 1–10.
  • Christian, J. T. 2004. “Geotechnical Engineering Reliability: How Well Do We Know What We are Doing?” Journal of Geotechnical and Geoenvironmental Engineering 130 (10): 985–1003.
  • Elfeki, A., and M. Dekking. 2001. “A Markov Chain Model for Subsurface Characterization: Theory and Applications.” Mathematical Geology 33 (5): 569–589.
  • Feng, W., S. Wu, Y. Yin, J. Zhang, and K. Zhang. 2017. “A Training Image Evaluation and Selection Method Based on Minimum Data Event Distance for Multiple-Point Geostatistics.” Computers & Geosciences 104: 35–53.
  • GEO (Geotechnical Engineering Office). 2006. “Foundation Design and Construction.” CEDD, 376.
  • GEO (Geotechnical Engineering Office). 2011. Geotechnical Manual for Slopes. Hong Kong: Civil Engineering and Development Dept., Government of Hong Kong SAR.
  • Guardiano, F. B., and R. M. Srivastava. 1993. “Multivariate Geostatistics: Beyond Bivariate Moments.” In Geostatistics Troia’92, edited by A. O. Soares, 133–144. Dordrecht: Springer.
  • Hansen, T. M., and T. Bach. 2016. “MPSLIB: A C Class for Sequential Simulation of Multiple-Point Statistical Models.” Software X 5: 127–133.
  • Itasca, F. 2011. FLAC-Fast Lagrangian Analysis of Continua, Version. 7.0. Minneapolis: Itasca Consulting Group.
  • Jia, R., Y. Lv, G. Wang, E. Carranza, Y. Chen, C. Wei, and Z. Zhang. 2021. “A Stacking Methodology of Machine Learning for 3D Geological Modeling with Geological-Geophysical Datasets, Laochang Sn Camp, Gejiu (China).” Computers & Geosciences 151: 104754.
  • Jiang, S. H., J. Huang, X. H. Qi, and C. B. Zhou. 2020. “Efficient Probabilistic Back Analysis of Spatially Varying Soil Parameters for Slope Reliability Assessment.” Engineering Geology 271: 105597.
  • Jiang, S. H., I. Papaioannou, and D. Straub. 2018. “Bayesian Updating of Slope Reliability in Spatially Variable Soils with In-Situ Measurements.” Engineering Geology 239: 310–320.
  • Jiang, S. H., I. Papaioannou, and D. Straub. 2020. “Optimization of Site Exploration Programs for Slope Reliability Assessment.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 6 (1): 04020004.
  • Koltermann, C. E., and S. M. Gorelick. 1996. “Heterogeneity in Sedimentary Deposits: A Review of Structure-Imitating, Process-Imitating, and Descriptive Approaches.” Water Resources Research 32 (9): 2617–2658.
  • Li, L., and X. Chu. 2019. “Failure Mechanism and Factor of Safety for Spatially Variable Undrained Soil Slope.” Advances in Civil Engineering, 2019. doi:https://doi.org/10.1155/2019/8575439.
  • Li, A., N. H. Jafari, and F. T. C. Tsai. 2020. “Modelling and Comparing 3-D Soil Stratigraphy Using Subsurface Borings and Cone Penetrometer Tests in Coastal Louisiana, USA.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 14 (2): 158–176.
  • Li, Z., X. Wang, H. Wang, and R. Y. Liang. 2016. “Quantifying Stratigraphic Uncertainties by Stochastic Simulation Techniques based on Markov Random Field.” Engineering Geology 201: 106–122.
  • Maharaja, A. 2008. “TiGenerator: Object-based Training Image Generator.” Computers & Geosciences 34 (12): 1753–1761.
  • Malone, A. W. 1990. “Geotechnical Phenomena Associated with Piling in Hong Kong.” Quarterly Journal of Engineering Geology and Hydrogeology 23 (4): 289–305.
  • Mariethoz, G., and J. Caers. 2014. Multiple-point Geostatistics: Stochastic Modeling with Training Images. Chichester, UK: John Wiley & Sons.
  • Mood, A. M. 1940. “The Distribution Theory of Runs.” The Annals of Mathematical Statistics 11 (4): 367–392.
  • Pérez, C., G. Mariethoz, and J. M. Ortiz. 2014. “Verifying the High-Order Consistency of Training Images with Data for Multiple-Point Geostatistics.” Computers & Geosciences 70: 190–205.
  • Phoon, K. K., J. Ching, and T. Shuku. 2021. “Challenges in Data-Driven Site Characterization.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. doi:https://doi.org/10.1080/17499518.2021.1896005.
  • Qi, X. H., D. Q. Li, K. K. Phoon, Z. J. Cao, and X. S. Tang. 2016. “Simulation of Geologic Uncertainty Using Coupled Markov Chain.” Engineering Geology 207: 129–140.
  • Sazzad, M. M., F. I. Rahman, and M. A. A. Mamun. 2015. “Mesh Effect on the FEM Based Stability Analysis of Slope.” International Conference on recent innovation in Civil Engineering for Sustainable Development (IICSD-2015), Gazipur, Bangladesh, 387–391.
  • Shannon, C. E. 1948. “A Mathematical Theory of Communication.” Bell System Technical Journal 27 (3): 379–423.
  • Shi, C., and Y. Wang. 2021a. “Non-Parametric and Data-Driven Interpolation of Subsurface Soil Stratigraphy from Limited Data Using Multiple Point Statistics.” Canadian Geotechnical Journal 58 (2): 261–280.
  • Shi, C., and Y. Wang. 2021b. “Smart Determination of Borehole Number and Locations for Stability Analysis of Multi-Layered Slopes Using Multiple Point Statistics and Information Entropy.” Canadian Geotechnical Journal. doi:https://doi.org/10.1139/cgj-2020-0327.
  • Shi, C., and Y. Wang. 2021c. “Development of Subsurface Geological Cross-Section from Limited Site-Specific Boreholes and Prior Geological Knowledge Using Iterative Convolution XGBoost.” Journal of Geotechnical and Geoenvironmental Engineering 147 (9): 04021082. doi:https://doi.org/10.1061/(ASCE)GT.1943-5606.0002583.
  • Shi, C., and Y. Wang. 2021d. “Non-Parametric Machine Learning Methods for Interpolation of Spatially Varying Non-Stationary and Non-Gaussian Geotechnical Properties.” Geoscience Frontiers 12 (1): 339–350.
  • Strebelle, S., and N. Remy. 2005. “Post-Processing of Multiple-Point Geostatistical Models to Improve Reproduction of Training Patterns.” In Geostatistics Banff 2004, edited by Oy Leuangthong and Clayton V. Deutsch, 979–988. Dordrecht: Springer.
  • Terzaghi, K. 1929. “Effect of Minor Geologic Details on the Safety of Dams.” American Institute of Mining and Metallurgical Engineers. Technical publication 215: 31–44.
  • Vick, S. G. 2002. Degrees of Belief: Subjective Probability and Engineering Judgment. Reston, VA: ASCE Press.
  • Wang, Y., Y. Hu, and T. Zhao. 2020. “CPT-based Subsurface Soil Classification and Zonation in a 2D Vertical Cross-Section Using Bayesian Compressive Sampling.” Canadian Geotechnical Journal 57 (7): 947–958.
  • Wang, H., J. F. Wellmann, Z. Li, X. Wang, and R. Y. Liang. 2017. “A Segmentation Approach for Stochastic Geological Modeling Using Hidden Markov Random Fields.” Mathematical Geosciences 49 (2): 145–177.
  • Wellmann, J. F., and K. Regenauer-Lieb. 2012. “Uncertainties have a Meaning: Information Entropy as a Quality Measure for 3-D Geological Models.” Tectonophysics 526–529: 207–216.
  • Zhou, C., J. Ouyang, W. Ming, G. Zhang, Z. Du, and Z. Liu. 2019. “A Stratigraphic Prediction Method Based on Machine Learning.” Applied Sciences 9 (17): 3553.
  • Zhu, S., R. Hack, K. Turner, and M. Hale. 2003. “How Far will Uncertainty of the Subsurface Limit the Sustainability Planning of the Subsurface.” Proceedings of Sustainable Development & Management of the Subsurface (SDMS) Conference, 5–7 November 2003, Utrecht, The Netherlands, 203–210.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.