Graph Attention Networks with Multihead Attention for Improved Resistivity Model Estimation

Magnetotellurics (MT) investigates the subsurface resistivity from naturally occurring electromagnetic field fluctuations. The linearity and noise in MT data cause traditional inversion methods to fail, resulting in poor resistivity reconstruction. While convolutional and recurrent neural networks have been applied to invert MT, they cannot be used to include complex, non-local relationships over depth and frequency. In this research, we propose a hybrid model that combines graph attention networks (GATs) and multihead attention mechanisms. The GAT module serves as both denoiser and encoder, structuring the input frequency–phase data into a compressed representation, and the transformer-based attention selectively emphasizes the most informative spectral elements. This allows the model to predict resistivity distributions more accurately with depth. Experiments on both synthetic and field datasets demonstrate that the proposed model outperforms the Convolutional Neural Network (CNN) Mean Squared Error (MSE) (0.0030), with correlation coefficient R = 0.78, and Vanilla Neural Network (VNN) MSE (0.0173), with correlation coefficient R = 0.71 baselines with a preeminence of MSE of 0.0018 and a correlation coefficient of 0.85. The integration of graph attention with frequency-aware multihead attention allows the current model to capture the underlying physics of MT data in a far more effective way and provides resistivity profiles that are accurate and less noisy.