Excavation under complex geological conditions requires effective and accurate geological forward-prospecting to detect the unfavorable geological structure and estimate the classification of surrounding rock in front of the tunnel face. In this work, a forward-prediction method for tunnel geology and classification of surrounding rock is developed based on seismic wave velocity layered tomography. In particular, for the problem of strong multi-solution of wave velocity inversion caused by few ray paths in the narrow space of the tunnel, a layered inversion based on regularization is proposed. By reducing the inversion area of each iteration step and applying straight-line interface assumption, the convergence and accuracy of wave velocity inversion are effectively improved. Furthermore, a surrounding rock classification network based on autoencoder is constructed. The mapping relationship between wave velocity and classification of surrounding rock is established with density, Poisson's ratio and elastic modulus as links. Two numerical examples with geological conditions similar to that in the field tunnel and a field case study in an urban subway tunnel verify the potential of the proposed method for practical application.

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Keywords: Tunnel geological forward-prospecting, Seismic wave velocity, Layered inversion, Surrounding rock classification, Artificial neural network (ANN)