An Aeromagnetic Compensation Method Based on Differentiable Architecture Search-Guided Physics-Informed Neural Network

The aeromagnetic compensation method is critical for mitigating magnetic interference in airborne geophysical surveys. In the case of complex magnetic interference, the model-driven linear regression method based on the Toles-Lawson (T-L) model cannot guarantee compensation accuracy. Additionally, purely data-driven methods often require large datasets and lack interpretability. Recent hybrid solutions, particularly Physics-Informed Neural Network (PINN), effectively combine the advantages of model-driven and data-driven methods. However, PINN is heavily reliant on manual architecture tuning, which severely limits optimization efficiency and compensation accuracy. To address this issue, this letter proposes a Differentiable Architecture Search-Guided Physics-Informed Neural Network (DARTS-PINN) method. Experimental results demonstrate that DARTS-PINN can effectively mitigate magnetic interference, improve optimization efficiency, and substantially reduce data dependence. For aeromagnetic compensation of magnetic sensors fixed inside the cabin, the root mean square error (RMSE) of DARTS-PINN is 1.089 nT. In contrast, the optimal RMSEs of the model-driven and data-driven methods are 6.195 nT and 2.026 nT, respectively.