Abstract: The green construction of tunnel requires not only predicting disaster anomalies such as water inrush and mud inrush in front of tunnel face, but also identifying geological structure to support disaster warning and prevention. The traditional transient electromagnetic interpretation method can only provide resistivity information, which not meet the requirements of tunnel geological disaster management. Therefore, virtual wavefield imaging technology is introduced into the tunnel detection to achieve comprehensive interpretation of the electrical information and structures of disaster anomalies. Firstly, transient electromagnetic data is transformed into virtual wave field. Then, the resistivity imaging method was used to calculate the resistivity in front of the tunnel face to establish the virtual wave field velocity model, and the wave field was extended by the Kirchhoff integral to realize the transient electromagnetic virtual wave field migration imaging. Finally, the resistivity and migration imaging results are combined to interpret the geological body in front of the tunnel face to supply the electrical and structural characteristics of the geological disaster anomalous. Two common disaster models of water-filled cave and water-filled fault were used to verify the method. Resistivity results of water-filled fault model can identify low resistivity anomalies in front of the tunnel face, but the distribution range of anomalies is slightly increased, and it is difficult to determine the inclination angle. The migration imaging result can identify the boundary of anomaly effectively and correspond accurately, which can judge the direction of anomaly tilt effectively. Resistivity results of water-filled cave model can identify the low resistivity anomaly and location of the cave, but the anomaly structure is slightly different from the actual model. The migration imaging results delineate the structure of the model and coincides with the model. The migration imaging results of the measured data effectively delineate the low resistivity anomaly location and structure information, and the predicted results are consistent with the geological information. The theoretical and measured data show that the virtual wave field migration imaging results not only contain the resistivity distribution of geological disaster body, but also identify the electrical boundary of geological disaster anomalous, providing abundant geological information for the warning and prevention of geological disaster.