Rapid imaging of subsurface media with magnetotellurics based on Pix2Pix GAN

Rapid imaging of subsurface electrical structures is highly challenging, especially for complex geological formations. Conventional inversion algorithms require repeated solutions of large-scale forward problems, which constitute the main computational expense. To address this limitation, we have developed an underground resistivity imaging method based on the Pix2Pix Generative Adversarial Network (GAN) architecture. Our approach integrates impedance phase information with conventional apparent resistivity observations, significantly improving imaging accuracy. For training data generation, we employ Gaussian random fields to synthesize resistivity models. This practice not only enhances the geological representativeness of the data but also introduces meaningful variability that benefits the generalization capability of the GAN. By systematically comparing the prediction accuracy under different loss functions, we determined the optimal form of the loss function.

Detailed qualitative and quantitative evaluations demonstrate that our multi-parameter joint inversion strategy outperforms methods relying on only a single parameter, such as apparent resistivity or impedance phase alone. To improve the method’s robustness in practical applications, we incorporate the objective function from conventional inversion into the GAN’s loss function to handle noisy data. This geophysically constrained loss function greatly enhances the model’s noise resistance. In synthetic data experiments, compared with the Nonlinear Conjugate Gradient (NLCG) inversion method, our approach not only achieves faster prediction but also exhibits superior capability in resolving high-resistivity bodies beneath low-resistivity layers. Validation using real-world data further confirms the practical applicability and generalization potential of the proposed method.