隧道瞬变电磁克希霍夫偏移成像与地质灾害探测

隧道绿色施工不仅需要预报工作面前方突水突泥等低阻异常，更需要查明灾害体的地质构造，为灾害预警与防治提供保障。传统的瞬变电磁解释方法仅能够提供单一电阻率信息不能满足隧道地质灾害治理要求。因此，将虚拟波场成像技术引入到瞬变电磁隧道探测技术中，期望实现灾害体电性与结构综合解释。首先，根据波场变换原理将瞬变电磁数据转换到虚拟波场；然后，利用电阻率成像算法计算工作面前方电阻率建立虚拟波场速度模型，利用波动方程克希霍夫积分解进行波场延拓实现瞬变电磁虚拟波场偏移成像；最后，综合电阻率与偏移成像结果对工作面前方地质体进行解释，判断地质灾害体的电性与构造特征。分别采用充水溶洞、充水断层两类常见灾害模型对算法进行验证。充水断层模型电阻率成像结果可以识别工作面前方低阻异常，但是异常分布范围略有增加，且不易判断倾斜角度；而偏移成像结果有效地识别异常的边界位置且对应准确，更易容判断异常倾斜方向。充水溶洞模型电阻率成像结果可识别溶洞的低阻异常与位置，但是异常形态与实际模型略有偏差；偏移成像结果圈定了模型的前后边界且与模型吻合较好。实测数据偏移成像结果有效圈定了低阻异常位置与裂隙形态，预测结果与已知出水孔情况相符。理论模型与实测数据结果表明，虚拟波场克希霍夫偏移成像结果既包含地质灾害体的电阻率分布，又能识别灾害体电性分界面，为地质灾害预警与防治提供了丰富的地质信息。 

Geological structure detection with tunnel transient electromagnetic Kirchhoff 2D migration imaging

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.