Tunnel Seismic Detection for Tunnel Boring Machine by Joint Active and Passive Source Method and Imaging Advanced Prediction

Abstract

Abstract Tunnel seismic advanced prediction method enables the detection of anomalous bodies in front of the tunnel face and the reduction of tunnel construction risk. The active source and the passive source detection methods are two commonly used for imaging advanced prediction of tunnel boring machine (TBM). Although both methods achieve good results, each method still has room for improvement. The active detection method is not conducive to long-distance detection because the sources are generally far away from the anomalous bodies and the received signals are weak. The passive detection method usually produces results of low resolution and with limited forecast precision. Since the active and passive source methods are applied separately, the geophone utilization rate is low and the cost is high. Moreover, both methods have low signal-to-noise ratio and low imaging resolution for single detection data. To overcome the above drawbacks, this article proposes a joint TBM tunnel seismic detection method and an imaging advanced prediction method to detect anomalous bodies in medium and long distances. The advanced prediction employs multiple stacking schemes to enhance the reflection wave energy of the anomalous bodies and suppress the interference wave. The proposed method integrates the active source data into the passive source method to extract the P- and S-waves. The joint active and passive source method further incorporates multiple stack schemes to achieve much higher resolution imaging and provides more accurate detection results. Moreover, the proposed joint method allows multiple utilization of the geophone and reduces the cost. Simulation results are presented for performance verification.