Early warning of large deformation in soft rock tunnels: Implications from microseismic monitoring and weighted time-varying network

Abstract

To address the challenge of reliable early identification of surrounding-rock deterioration and large deformation in deep-buried soft rock tunnels under high in-situ stress, this study proposes a deformation-calibrated stability assessment and early-warning method based on microseismic (MS) monitoring and weighted time-varying networks. The results demonstrate that the time-varying MS event network can effectively capture the spatiotemporal evolution of MS activity, while the weighted network formulation further highlights structural evolution dominated by high-energy events. Under the spatiotemporal proximity thresholds of (d_c, τ_c)=(10 m, 8 h), the contributions of largest connected component ratio (LCC_k), average shortest path length (L_k), average clustering coefficient (C_k), and modularity (Q_k) to comprehensive stability index (S_k) were relatively balanced, accounting for 9.3%, 33.6%, 24.2%, and 32.9%, respectively. This parameter combination provided a more informative representation of the structural evolution of MS events. Across multiple tunnel sections, the multivariate logistic model achieved a high mean accuracy of 92%, a low mean false alarm rate of 7%, and strong discriminative capability, with a mean area under the curve (AUC) score of 0.94. The model enabled prediction of whether large deformation would occur within the subsequent 3 days, with a lead time of approximately 2 days. The proposed method provides a new technical pathway for the early warning of large deformation in similar projects.

Keywords

soft rock tunnel / MS monitoring / time-varying network / weighted network / large deformation / early warning

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