A new comprehensive geological prediction method based on constrained inversion and integrated interpretation for water-bearing tunnel structures

Abstract

Advanced geological predictions and ascertainment of the water-bearing geological structures in front of the tunnel face are effective ways to ensure the safety of tunnel construction. Comprehensive geological predictions are often used to obtain adverse geological conditions in front of the tunnel face, and the comprehensive comparison analysis on various prediction methods can help to reduce multiplicity of solutions and improve reliability. However, in the conventional comprehensive prediction system, the prediction data for each method is separately inverted and processed, so the problem of multiple solutions in detection results for each category actually cannot be solved. Therefore a new comprehensive geological prediction method is proposed in this paper based on constrained inversion and integrated interpretation for water-bearing tunnel structures to solve the problem. Firstly, four preferred prediction methods are selected to meet the needs of the advanced detection of water-bearing structure. They are seismic reflection method, transient electromagnetic method, 3D resistivity method and ground penetrating radar (GPR) method. Then, a constrained inversion method of 3D resistivity is proposed based on the inequality and spatial structural constraints. Inequality constraint characterises the electrical resistivity variation range of the detection area, and its prior information is mainly from the geological inference and core-drilling analysis. Spatial structural constraint characterises the geological structure boundary and shape, its prior information is mainly derived from some more effective methods for geological tectonic boundary recognition such as seismic reflection and GPR methods, and it also can be derived from the disclosure by drilling exploration. The role of resistivity constrained inversion to reduce the multiple solutions of prediction is mainly reflected in data processing, and the role of integrated interpretation to reduce the multiple solutions of prediction is mainly reflected in the result interpretation process. Meanwhile, the responding characteristics of methods of seismic reflection, electromagnetism, 3D resistivity, GPR and others to the water-bearing geological structure are summarised from the differences of elasticity, electrical conductivity and dielectric constant. Therefore, an integrated interpretation guideline for water-bearing structures is proposed. Finally, a new comprehensive geological prediction technology system is established through combining the constrained inversion and the integrated interpretation. Numerical examples and engineering applications show that this method can take full advantage of the prior information of inequality constraints and spatial structures, and together with the integrated interpretation on the comprehensive geological prediction results, it has a significant effect in eliminating the false anomalies of inversion, reducing the multiple solutions of inversion and improving prediction accuracy.

Keywords:

• tunnel construction
• water-bearing structure
• constrained inversion
• integrated interpretation
• comprehensive geological prediction

Acknowledgements

This paper is supported by the National Program on Key Basic Research Project of China (973 Program), National Key Scientific Instrument and Equipment Development Project, National Natural Science Foundation of China, and Fundamental Research Funds of Shandong University. The above supports are greatly acknowledged.

Additional information

Funding

This work was supported by the National Program on Key Basic Research Project of China (973 Program) [project number 2013CB036002], [project number 2015CB058101], [project number 2014CB046901]; National Key Scientific Instrument and Equipment Development Project [project number 51327802]; National Natural Science Foundation of China [project number 51139004], [project number 51479104], [project number 41502279]; Fundamental Research Funds of Shandong University [project number 2014QY007].