Fast Forecasting of Water-Filled Bodies Position Using Transient Electromagnetic Method Based on Deep Learning

The transient electromagnetic method (TEM) is widely used for detecting low-resistivity areas ahead of tunnel. However, implementing the 2-D or 3-D inversion of whole-space geo-electric models is not feasible due to the narrow space within the underground tunnel and the nonuniqueness of TEM inversion. To solve this problem, we develop a fast inversion operator guided by deep learning (DL), which translates the time-domain TEM measurements directly into the spatial probability of water-filled anomalies's position. Trained by synthetic data, our system shows impressive adaptability to predict water-filled anomalies when implementing different transmit currents, source waveforms, coil turn numbers, and abnormal body sizes. Compared to traditional 1-D tunnel TEM inversion, our system demonstrates less ambiguity, superior stability, applicability, noise resistance, and higher computational efficiency. The effectiveness of this method has been further confirmed by physical model experiments and field data. This inversion operator can support instantaneous TEM detection of low resistivity in the tunnel activities.