Implicit modelling and dynamic update of tunnel unfavourable geology based on multi-source data fusion using support vector machine

ABSTRACT

Three-dimensional unfavourable geology models with complex structures and various attributes have become crucial for optimal design and risk control during tunnel construction. In practical applications, it is necessary to integrate multi-source advanced prediction data, including tunnel seismic prediction data, geological radar data, and transient electromagnetic data, to perform dynamic model construction. However, due to the implicit representation of the spatial distribution of single-source data and the heterogeneity of multi-source data, existing methods mainly rely on manual interpretation to perform comprehensive analysis, causing an increase in data uncertainty and unreliable, inaccurate modelling results. Therefore, this study proposes a dynamic implicit modelling method of tunnel unfavourable geology based on multi-source data fusion using a support vector machine (SVM). This method uses the SVM to fuse multi-source data and output unfavourable geological categories, including faults, fracture zones, water-rich areas, and weak rock masses, represented as spatially continuous unfavourable geological points. A globally supported radial basis function combined with a Boolean implicit calculation is used for model construction and local adaptive update. Experiments were implemented in a deep-buried tunnel, and by comparing the results with the realistic status throughout the excavation, the accuracy and adaptive ability of the proposed modelling method were well proven.

KEYWORDS:

• 3D unfavourable geology model
• support vector machine automatic identification
• implicit modelling
• local adaptive update

Acknowledgements

We appreciate the detailed suggestions and constructive comments from the editor and the anonymous reviewers.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and codes that support the findings of this study are available in Zenodo at https://doi.org/10.5281/zenodo.6563133.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China (41941019) and the Open Fund of the Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources (grant number: KF-2020-05-032).