A Novel Method for Detecting Geological Anomalies in Tunnel Space Using Multiparameter Elastic Full Waveform Inversion

During tunnel excavation, precise detection of low-velocity anomalies ahead of the tunnel face is crucial for ensuring the smooth construction of the tunnel project. However, current tunnel seismic prediction (TSP) system can only provide reflection interface imaging, which lacks the required accuracy and reliability in seismic data interpretation. In response to these limitations, this study presents a tunnel geological anomaly detection approach by utilizing the developed GEI-TSP system in combination with the multiparameter elastic full waveform inversion (EFWI) technique. This approach enables the retrieval of multiparameter distributions, including P-wave velocity, S-wave velocity, and density, for the geological anomalies ahead of the tunnel face, thereby enhancing the detection reliability. First, we design the tunnel forward model to generate the hybrid seismic data encompassing both body waves and Rayleigh surface waves. Second, we derive gradients and pseudo-Hessian operators using a wave mode decomposition strategy to mitigate crosstalk effects among various model parameters and improve inversion accuracy. With an activity level measurement, we conduct a novel model updating strategy to balance contributions from different wavefield combinations and avoid crosstalk between velocities and density. Experiments with several theoretical tunnel models demonstrate the feasibility of the proposed detection method and highlight its substantial potential for imaging anomalies. Meanwhile, the inversion performance of the proposed detection method is evaluated under different noise levels and observation layouts. Finally, a comprehensive assessment of the computational and storage performance of this method is carried out.