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Deep learning–based stochastic modelling and uncertainty analysis of fault networks

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Bulletin of Engineering Geology and the Environment Aims and scope Submit manuscript

Abstract

Limited by the survey data and current interpretation methods, the modelling processes of fault networks are fraught with uncertainties. In hydraulic geological engineering, the location uncertainty of faults plays a vital role in decision-making and engineering safety. However, traditional uncertainty modelling methods have difficulty obtaining accurate uncertainty quantification and topology representation. To this end, we proposed a novel solution for uncertainty analysis and three-dimensional modelling for faults via a deep learning approach. A spatial uncertainty perception (SUP) method is first presented based on a modified deep mixture density network (MDN), which can be used to learn the spatial distributions of fault zones, calculate the probability of fault models, and simulate stochastic models with certain confidence degrees. After that, a graph representation (GRep) method is developed to express the topological form and geological ages of fault networks. The GRep makes it possible to automatically simulate the spatial distributions of fault belts, thus providing an effective way for the uncertainty modelling and assessment of fault networks. The two methods are then performed in the geological engineering of a practical hydraulic project. The results show that this solution can conduct accurate uncertainty evaluations and visualizations on fault networks, thus providing suggestions for subsequent geological investigations.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant no. 51879185), the Tianjin Science Foundation for Distinguished Young Scientists of China (Grant no. 17JCJQJC44000), and the Hong Kong Research Grants Council Theme-based Research Scheme (Grant no. T22-505/19-N).

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Authors and Affiliations

  1. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hongkong, China

    Shuai Han, Heng Li, Runhao Guo & Jie Ma

  2. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China

    Mingchao Li & Jiawen Zhang

  3. China Water Resources Beifang Investigation, Design and Research Co., Ltd., Tianjin, China

    Wenchao Zhao

Authors
  1. Shuai Han
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  2. Heng Li
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  3. Mingchao Li
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  4. Jiawen Zhang
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  5. Runhao Guo
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  6. Jie Ma
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  7. Wenchao Zhao
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Contributions

Conceptualization: Heng Li, Mingchao Li; methodology: Mingchao Li, Shuai Han; formal analysis and investigation: Jiawen Zhang, Shuai Han, Wenchao Zhao; writing—original draft preparation: Shuai Han, Jiawen Zhang; writing—review and editing: Runhao Guo, Jie Ma; funding acquisition: Mingchao Li, Heng Li; resources: Mingchao Li; supervision: Heng Li.

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Correspondence to Mingchao Li.

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Han, S., Li, H., Li, M. et al. Deep learning–based stochastic modelling and uncertainty analysis of fault networks. Bull Eng Geol Environ 81, 242 (2022). https://doi.org/10.1007/s10064-022-02735-7

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  • DOI: https://doi.org/10.1007/s10064-022-02735-7

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