Machine learning of three-dimensional subsurface geological model for a reclamation site in Hong Kong

[1]  Chao Shi,et al.  Data-driven sequential development of geological cross-sections along tunnel trajectory , 2022, Acta Geotechnica.

[2]  Yu Wang,et al.  Data-driven construction of Three-dimensional subsurface geological models from limited Site-specific boreholes and prior geological knowledge for underground digital twin , 2022, Tunnelling and Underground Space Technology.

[3]  X. Qi,et al.  Two-dimensional prediction of the interface of geological formations: A comparative study , 2022, Tunnelling and Underground Space Technology.

[4]  Wengang Zhang,et al.  Rockhead profile simulation using an improved generation method of conditional random field , 2021, Journal of Rock Mechanics and Geotechnical Engineering.

[5]  Yu Wang,et al.  Training image selection for development of subsurface geological cross-section by conditional simulations , 2021, Engineering Geology.

[6]  Jian-Min Zhang,et al.  Machine learning method for CPTu based 3D stratification of New Zealand geotechnical database sites , 2021, Adv. Eng. Informatics.

[7]  Yu Wang,et al.  Development of Subsurface Geological Cross-Section from Limited Site-Specific Boreholes and Prior Geological Knowledge Using Iterative Convolution XGBoost , 2021 .

[8]  K. Phoon,et al.  Challenges in data-driven site characterization , 2021, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards.

[9]  Yu Wang,et al.  Smart Determination of Borehole Number and Locations for Stability Analysis of Multi-layered Slopes using Multiple Point Statistics and Information Entropy , 2021 .

[10]  Kok-Kwang Phoon,et al.  3D Probabilistic Site Characterization by Sparse Bayesian Learning , 2020 .

[11]  Yu Wang,et al.  Nonparametric and data-driven interpolation of subsurface soil stratigraphy from limited data using multiple point statistics , 2020, Canadian Geotechnical Journal.

[12]  T. Lim Housing Policies in Hong Kong , 2020 .

[13]  J. Chu,et al.  Prediction of interfaces of geological formations using the multivariate adaptive regression spline method , 2020, Underground Space.

[14]  C. Juang,et al.  Probabilistic analysis and design of stabilizing piles in slope considering stratigraphic uncertainty , 2019, Engineering Geology.

[15]  Weihua Ming,et al.  A Stratigraphic Prediction Method Based on Machine Learning , 2019, Applied Sciences.

[16]  C. Lee,et al.  Soft Soil Engineering , 2017 .

[17]  Albert T. Yeung,et al.  Statistical Analysis of geotechnical engineering properties of Hong Kong marine clays , 2017 .

[18]  Ke Zhang,et al.  A training image evaluation and selection method based on minimum data event distance for multiple-point geostatistics , 2017, Comput. Geosci..

[19]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[20]  Albert T. Yeung,et al.  Geotechnical works of the Hong Kong-Zhuhai-Macao Bridge Project , 2016 .

[21]  G. Mariéthoz,et al.  Multiple-point Geostatistics: Stochastic Modeling with Training Images , 2014 .

[22]  Julián M. Ortiz,et al.  Verifying the high-order consistency of training images with data for multiple-point geostatistics , 2014, Comput. Geosci..

[23]  Jef Caers,et al.  Modeling Uncertainty in the Earth Sciences , 2011 .

[24]  R. Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[25]  Clayton V. Deutsch,et al.  Geostatistical Software Library and User's Guide , 1998 .

[26]  Clayton V. Deutsch,et al.  Hierarchical object-based stochastic modeling of fluvial reservoirs , 1996 .

[27]  Anil K. Jain,et al.  A modified Hausdorff distance for object matching , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[28]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  R. Walsh,et al.  Land reclamation in Singapore, Hong Kong and Macau , 1991 .

[30]  A. Mood The Distribution Theory of Runs , 1940 .

[31]  Wengang Zhang,et al.  Similarity quantification of soil parametric data and sites using confidence ellipses , 2022 .

[32]  Yu Wang,et al.  CPT-based subsurface soil classification and zonation in a 2D vertical cross-section using Bayesian compressive sampling , 2019 .

[33]  Robert Y. Liang,et al.  A hidden Markov random field model based approach for probabilistic site characterization using multiple cone penetration test data , 2018 .

[34]  J. Swift,et al.  Visualizing Land Reclamation in Hong Kong: A Web Application , 2014 .

[35]  Steven F. Carle,et al.  CONDITIONAL SIMULATION OF HYDROFACIES ARCHITECTURE: A TRANSITION PROBABILITY/MARKOV APPROACH1 , 1998 .

[36]  R. M. Srivastava,et al.  Multivariate Geostatistics: Beyond Bivariate Moments , 1993 .