Regional 3D geological modeling along metro lines based on stacking ensemble model

Abstract

This paper presents a regional 3D geological modeling method based on the stacking ensemble technique to overcome the challenges of sparse borehole data in large-scale linear underground projects. The proposed method transforms the 3D geological modeling problem into a stratigraphic property classification problem within a subsurface space grid cell framework. Borehole data is pre-processed and trained using stacking method with five different machine learning algorithms. The resulting modelled regional cells are then classified, forming a regional 3D grid geological model. A case study for an area of 324 km² along Xuzhou metro lines is presented to demonstrate the effectiveness of the proposed model. The study shows an overall prediction accuracy of 85.4%. However, the accuracy for key stratigraphy layers influencing the construction risk, such as karst carve strata, is only 4.3% due to the limited borehole data. To address this issue, an oversampling technique based on the synthetic minority oversampling technique (SMOTE) algorithm is proposed. This technique effectively increases the number of sparse stratigraphic samples and significantly improves the prediction accuracy for karst caves to 65.4%. Additionally, this study analyzes the impact of sampling distance on model accuracy. It is found that a lower sampling interval results in higher prediction accuracy, but also increases computational resources and time costs. Therefore, in this study, an optimal sampling distance of 1 m is chosen to balance prediction accuracy and computation cost. Furthermore, the number of geological strata is found to have a negative effect on prediction accuracy. To mitigate this, it is recommended to merge less significant stratigraphy layers, reducing computation time. For key strata layers, such as karst caves, which have a significant impact on construction risk, further on-site sampling or oversampling using the SMOTE technique is recommended.

Keywords

3D geological modeling / Borehole data / Stacking / Machine learning

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Xia Bian: Writing - review & editing, Funding acquisition, Conceptualization. Zhuyi Fan: Writing - original draft, Data curation. Jiaxing Liu: Validation, Methodology. Xiaozhao Li: Supervision, Project administration. Peng Zhao: Visualization, Investigation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This study is supported by Yunlong Lake Laboratory of Deep Underground Science and Engineering Project (Grant No. 104023004) and the National Natural Science Foundation of China (Grant Nos. 52178328, and 42377190).

References

[1] Bian, X., Gao, Z. Y., Zhao, P., & Li, X. Z. (2024a). Quantitative analysis of low carbon effect of urban underground space in Xinjiekou district of Nanjing city, China. Tunnelling and Underground Space Technology, 143, 105502.

[2] Bian, X., Ren, Z. L., Zeng, L. L., Yao, Y. K., Zhao, F. Y., & Li, X. Z. (2024b). Effects of biochar on the compressibility of soil with high water content. Journal of Cleaner Production, 434, 140032.

[3] Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.

[4] Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.

[5] Calcagno, P., Chile`s, J. P., Courrioux, G., & Guillen, A. (2008). Geological modeling from field data and geological knowledge: Part I. Modeling method coupling 3D potential-field interpolation and geological rules. Physics of the Earth and Planetary Interiors, 171(1-4), 147-157.

[6] Caumon, G., Gray, G., Antoine, C., & Titeux, M. O. (2013). 3D implicit stratigraphic model building from remote sensing data on tetrahedral meshes: Theory and application to a regional model of La Popa Basin, NE Mexico. IEEE Transactions on Geoscience and Remote Sensing, 51 (3), 1613-1621.

[7] Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357.

[8] Chen, T. Q., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).

[9] Clayton, C. R. (2001). Managing geotechnical risk: Improving productivity in UK building and construction. London: Thomas Telford.

[10] Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63, 3-42.

[11] Hillier, M. J., Schetselaar, E. M., de Kemp, E. A., & Perron, G. (2014). Three-dimensional modeling of geological surfaces using generalized interpolation with radial basis functions. Mathematical geosciences, 46 (8), 931-953.

[12] Hu, Y., & Wang, Y. (2020). Probabilistic soil classification and stratification in a vertical cross-section from limited cone penetration tests using random field and Monte Carlo simulation. Computers and Geotechnics, 124, 103634.

[13] Jia, R., Lv, Y. K., Wang, G. W., Carranza, E., Chen, Y. Q., Wei, C., & Zhang, Z. Q. (2021). A stacking methodology of machine learning for 3D geological modeling with geological-geophysical datasets, Laochang Sn camp, Gejiu (China). Computers and Geotechnics, 151, 104754.

[14] Madsen, R. B., Høyer, A. S., Andersen, L. T., Møller, I., & Hansen, T. M. (2022). Geology-driven modeling: A new probabilistic approach for incorporating uncertain geological interpretations in 3D geological modeling. Engineering Geology, 309, 106833.

[15] Ouyang, J., Zhou, C., Liu, Z., & Zhang, G. (2023). Triangulated irregular network-based probabilistic 3D geological modeling using Markov Chain and Monte Carlo simulation. Engineering Geology, 320, 107131.

[16] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.

[17] Quinlan, J. R. (1996). Learning decision tree classifiers. ACM Computer Surveys, 28(1), 71-72.

[18] Shi, C., & Wang, Y. (2021a). Development of subsurface geological crosssection from limited site-specific boreholes and prior geological knowledge using iterative convolution XGBoost. Journal of Geotechnical and Geoenvironmental Engineering, 147(9), 04021082.

[19] Shi, C., & Wang, Y. (2021b). Development of subsurface geological crosssection from limited site-specific boreholes and prior geological knowledge using iterative convolution XGBoost. Journal of Geotechnical and Geoenvironmental Engineering, 147(9), 1-17.

[20] Smirnoff, A., Boisvert, E., & Paradis, S. J. (2008). Support vector machine for 3D modeling from sparse geological information of various origins. Computer and Geosciences, 34(2), 127-143.

[21] Smyth, P., & Wolpert, D. (1999). Linearly combining density estimators via stacking. Machine Learning, 36(1), 59-83.

[22] Wang, X. (2020). Uncertainty quantification and reduction in the characterization of subsurface stratigraphy using limited geotechnical investigation data. Underground Space, 5(2), 125-143.

[23] Wang, Y., Hu, Y., & Zhao, T. Y. (2020). Cone penetration test (CPT)- based subsurface soil classification and zonation in two-dimensional vertical cross section using Bayesian compressive sampling. Canadian Geotechnical Journal, 57(7), 947-958.

[24] Wang, Y., Hu, Y., & Phoon, K. K. (2022a). Non-parametric modeling and simulation of spatiotemporally varying geo-data. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 16(1), 77-97.

[25] Wang, Y., Shi, C., & Li, X. (2022b). Machine learning of geological details from borehole logs for development of high-resolution subsurface geological cross-section and geotechnical analysis. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 16(1), 2-20.

[26] Wood, D. A. (2019). Lithofacies and stratigraphy prediction methodology exploiting an optimized nearest-neighbour algorithm to mine well-log data. Marine and Petroleum Geology, 110, 347-367.

[27] Wu, S., Zhang, J. M., & Wang, R. (2021). Machine learning method for CPTu based 3D stratification of New Zealand geotechnical database sites. Advanced Engineering Informatics, 50, 101397.

[28] Xie, J. W., Huang, J. S., Zeng, C., Huang, S., & Burton, G. J. (2022). A generic framework for geotechnical subsurface modeling with machine learning. Journal of Rock Mechanics and Geotechnical Engineering, 14 (5), 1366-1379.

[29] Yan, W., Zhu, X., Chu, J., Wang, K. D., Wu, S. F., & Chiam, K. (2021). Use of tree-based machine learning methods for stratigraphic classification in 3D geological modeling. IOP Conference Series: Earth and Environmental Science, 861(7), 072039.

[30] Yang, H. Q., Chu, J., Qi, X. H., Wu, S. F., & Chiam, K. (2023). Bayesian evidential learning of soil-rock interface identification using boreholes. Computers and Geotechnics, 162, 105638.

[31] Yu, X., & Xu, Y. (2015). A methodology for automatically 3D geological modeling based on geophysical data grids. In Proceedings of 2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA) (pp. 40-43).

[32] Zhou, C., Ouyang, J., Ming, W., Zhang, G., Du, Z., & Liu, Z. (2019). A stratigraphic prediction method based on machine learning. Applied Sciences, 9(17), 3553.

[33] Zhu, X., Chu, J., Wang, K. D., Wu, S. F., Yan, W., & Chiam, K. (2021). Prediction of rockhead using a hybrid N-XGBoost machine learning framework. Journal of Rock Mechanics and Geotechnical Engineering, 6, 1231-1245.