Abstract: To minimize or eliminate the detection deviation in positioning water-bearing bodies in front of the tunnel face, which often occurs with conventional transient electromagnetic inversion methods used in tunnel advance prediction, the authors propose a nonlinear particle swarm inversion method for tunnel transient electromagnetics. The proposed method is based on a full-space forward algorithm for predicting water-bearing bodies in front of tunnel faces. For the numerical simulations, a full-space low-resistivity interlayer model and a low-resistivity body model in front of the tunnel face are established. The full-space forward algorithm is used to calculate the forward data, and Gaussian noise is added, which serves as the observation data. The particle swarm inversion algorithm is used to iteratively invert the observation data. The model is continuously adjusted through an update-evaluation-optimization process within the particle swarm until the fitting error threshold is satisfied, thereby yielding the optimal solution. The particle swarm inversion method is applied to an engineering project and compared with conventional smoke ring inversion results to verify its validity in predicting tunnel water-bearing bodies. The numerical simulation results demonstrate the following: (1) The electrical characteristics and layer thickness of the inversion results are consistent with the theoretical model. The method accurately depicts the position of the low-resistance interlayer interface and the low-resistance body, exhibiting higher positioning accuracy for the low-resistance body. (2) The calculation speed of the algorithm decreases with increasing Gaussian noise in later iterations, but it still exhibits a downward convergence trend, indicating high global optimization ability. Furthermore, the engineering application results demonstrate that compared with the conventional smoke ring inversion results, the low-resistance anomaly range of the particle swarm inversion is smaller, the anomaly contour is clearer, shallow details are more abundant, and the imaging accuracy is relatively higher. The tunnel excavation results verify that the particle swarm inversion method can accurately position the spatial location of the water-bearing body.

Key words: tunnel advance prediction, transient electromagnetic method, particle swarm inversion, water-bearing body positioning