Study on stochastic reconstruction methods for 3D geological structures along metro lines

doi:10.13745/j.esf.sf.2025.4.75

Abstract

Abstract:

Constructing the metro system is one of the effective solutions to relieve traffic congestion in big cities and enhance the comprehensive carrying capacity and development resilience. High-precision three-dimensional (3D) geological model is an important data infrastructure for determining the geological structure and distribution of unfavorable geological bodies underground. And it is one of the keys to ensuring the safety of metro engineering construction. However, the characteristic that the overall amount of geological data of metro engineering is not large but the local density highly restricts the effective identification and reconstruction of the distribution patterns of geological bodies. Taking the geological structure at a station of Guangzhou Metro Line 11 as the concrete example, this study systematically compares the performance of three modeling methods: random forest (RF), XGBoost and hybrid method of deep learning and multi-point statistics (DL+MPS), under the complex geological conditions of Cretaceous strata, Quaternary sedimentary strata and subvolcanic rocks. The results illustrate that combining the advantages of simulating global features by deep neural network and the local optimization of MPS, the DL+MPS method shows the best performance in key indicators such as accuracy (99.16%), F1 score (98.91%) and AUC value of ROC curve (0.93-0.99). The DL+MPS method can accurately reconstruct the spatial relationship between fault fracture zone and igneous rock mass, and avoid abnormal extension of strata and geological semantic disorder. In contrast, although RF and XGBoost show high training accuracy in the model fitting stage with the accuracy rates of 99.60% and 98.64% respectively, some problems such as discrete distribution of geological bodies, unreasonable extrapolation and strata interpenetration appear in their simulation results. The minimum borehole dispersion value of the results by RF and XGBoost methods reaches 69.93%, is which is significantly lower than that of DL + MPS method (73.33%-87.50%). The research shows that the deep learning model can effectively deal with the challenge of uneven spatial distribution of subway engineering data by virtue of its strong nonlinear feature extraction ability, which provides a better solution for 3D modeling under complex geological conditions, and has important practical value for improving the safety of underground engineering and the application of digital twin system.

Key words: three-dimensional geological models, metro line, multipoint statistics, deep learning

CLC Number:

P628

CHEN Yonghua, HOU Weisheng, GUO Qingfeng, YANG Songhua, YE Shuwan, LI Xin. Study on stochastic reconstruction methods for 3D geological structures along metro lines[J]. Earth Science Frontiers, 2025, 32(4): 222-234.

Figures/Tables 13

References 39

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参数设置		搜索空间
超参数	隐藏层数	[1, 10]
	隐藏层神经元数	50n (n∈[1, 10])
	学习率	[10^-5, 10^-1]
	批量训练样本数量	2n (n∈[5, 10])
	网络训练周期	100n (n∈[1, 10])
评估指标	均方误差损失	最小化指标
搜索周期	30

参数设置		搜索空间
超参数	隐藏层数	[1, 10]
	隐藏层神经元数	50n (n∈[1, 10])
	学习率	[10^-5, 10^-1]
	批量训练样本数量	2n (n∈[5, 10])
	网络训练周期	100n (n∈[1, 10])
评估指标	均方误差损失	最小化指标
搜索周期	30

方法	准确率/%	精确率/%	召回率/%	F1分数/%
DL+MPS	99.16	98.92	98.91	98.91
RF	99.60	98.68	98.14	98.40
XGBoost	98.64	95.67	95.31	95.49

方法	准确率/%	精确率/%	召回率/%	F1分数/%
DL+MPS	99.16	98.92	98.91	98.91
RF	99.60	98.68	98.14	98.40
XGBoost	98.64	95.67	95.31	95.49

钻孔编号	不同方法模拟结果钻孔验证准确率/%
钻孔编号	DL+MPS	RF	XGBoost
B1	79.17	81.25	77.08
B2	83.33	80.21	80.24
B3	73.33	69.93	72.27
B4	74.14	71.42	72.21
B5	87.50	89.09	83.52
B6	81.67	79.73	80.77
平均值	79.86	78.61	77.68