Three-dimensional reconstruction of subsurface stratigraphy using machine learning with neighborhood aggregation

verfasst von: Yue Hu, Ze Zhou Wang, Xiangfeng Guo, Hardy Yide Kek, Taeseo Ku, Siang Huat Goh, Chun Fai Leung, Ernest Tan, Yunhuo Zhang
Abstract: In engineering geology and geotechnical engineering, it is well recognized that subsurface soils/rocks are natural materials and exhibit variability in stratigraphy due to the complex geological formation processes they have undergone. Knowledge of subsurface soil stratigraphy is of great importance to geotechnical engineers. However, accurate and reliable interpretation of subsurface soil stratigraphy is challenging due to the limited number of site investigation boreholes available at the site and the highly heterogeneous properties of soil stratigraphy (e.g., interbedded or non-ordered layers). This paper proposes an improved data-driven machine learning framework boosted with the neighborhood aggregation technique for modelling three-dimensional (3D) subsurface soil stratigraphy in a more general and robust manner. Neighborhood aggregation, a technique often adopted in graph network learning, is integrated into this framework to regulate and improve the prediction results of classical machine learning models. The proposed framework is then cross-validated using 165 real site investigation boreholes for four selected machine learning models respectively. Cross-validation results suggest that the eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) models are more suitable for the task of soil stratigraphy prediction than Artificial Neural Network (ANN) and Support Vector Machine (SVM). Particularly, the XGBoost and RF are also amenable to neighborhood aggregation and can yield around 5% improvement in terms of average borehole prediction accuracy after introducing neighborhood aggregation. The improved machine learning framework allows for explicit 1D to 3D geological modelling, uncertainty quantification, and convenient visualization. The proposed framework facilitates digital transformation of geological and geotechnical site investigation.
Organisationseinheit(en): Institut für Risiko und Zuverlässigkeit
Externe Organisation(en): National University of Singapore
South China University of Technology
Golder Associates Inc.
Konkuk University
Land Transport Authority, Government of Singapore (LTA)
Typ: Artikel
Journal: Engineering geology
Band: 337
Anzahl der Seiten: 17
ISSN: 0013-7952
Publikationsdatum: 08.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Geotechnik und Ingenieurgeologie, Geologie
Elektronische Version(en): https://doi.org/10.1016/j.enggeo.2024.107588 (Zugang: Geschlossen)

BibTeX

@article{19b80563d7da4a08a91e2c55228d9004,
title = "Three-dimensional reconstruction of subsurface stratigraphy using machine learning with neighborhood aggregation",
abstract = "In engineering geology and geotechnical engineering, it is well recognized that subsurface soils/rocks are natural materials and exhibit variability in stratigraphy due to the complex geological formation processes they have undergone. Knowledge of subsurface soil stratigraphy is of great importance to geotechnical engineers. However, accurate and reliable interpretation of subsurface soil stratigraphy is challenging due to the limited number of site investigation boreholes available at the site and the highly heterogeneous properties of soil stratigraphy (e.g., interbedded or non-ordered layers). This paper proposes an improved data-driven machine learning framework boosted with the neighborhood aggregation technique for modelling three-dimensional (3D) subsurface soil stratigraphy in a more general and robust manner. Neighborhood aggregation, a technique often adopted in graph network learning, is integrated into this framework to regulate and improve the prediction results of classical machine learning models. The proposed framework is then cross-validated using 165 real site investigation boreholes for four selected machine learning models respectively. Cross-validation results suggest that the eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) models are more suitable for the task of soil stratigraphy prediction than Artificial Neural Network (ANN) and Support Vector Machine (SVM). Particularly, the XGBoost and RF are also amenable to neighborhood aggregation and can yield around 5% improvement in terms of average borehole prediction accuracy after introducing neighborhood aggregation. The improved machine learning framework allows for explicit 1D to 3D geological modelling, uncertainty quantification, and convenient visualization. The proposed framework facilitates digital transformation of geological and geotechnical site investigation.",
keywords = "Complex soil stratification, Engineering geology, Machine learning, Neighborhood aggregation, Three-dimensional geological models",
author = "Yue Hu and Wang, {Ze Zhou} and Xiangfeng Guo and Kek, {Hardy Yide} and Taeseo Ku and Goh, {Siang Huat} and Leung, {Chun Fai} and Ernest Tan and Yunhuo Zhang",
year = "2024",
month = aug,
doi = "10.1016/j.enggeo.2024.107588",
language = "English",
volume = "337",
journal = "Engineering geology",
issn = "0013-7952",
publisher = "Elsevier",
}