A Conflict‐Free Framework for Physical Constraint Transient Electromagnetic Inversion Based on Deep Learning

ABSTRACT

Traditional transient electromagnetic (TEM) inversion methods face challenges such as computational inefficiency, sensitivity to initial models and solution non‐uniqueness. To alleviate these limitations, the study proposes a novel physics‐guided neural network framework (Physics‐guided TEM Neural Network, PhyTEMNet) that tightly integrates deep learning with physical constraints through differentiable forward operators. The core of this framework lies in embedding a pre‐trained forward model as a differentiable constraint within the network training loop, ensuring predictions adhere to Maxwell's equations. A key innovation is the design of the Conflict‐Free Inverse Gradient (ConFIG) dynamic weighting mechanism, which autonomously balances the contributions of data bias and physical residual loss during optimization, effectively mitigating gradient conflicts. A hybrid network architecture extracts relevant spatiotemporal features from input data. Synthetic and field data experiments demonstrate that the proposed method achieves higher inversion accuracy, stronger noise robustness and better physical interpretability compared to purely data‐driven approaches, providing a novel technical pathway for TEM inversion.