Comparison of Deep Learning Models for 1D Magnetotelluric Inversion

This paper presents a comparative study of three deep learning architectures for one-dimensional magnetotelluric (MT) inversion: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Informer models. We developed a comprehensive framework for generating realistic synthetic MT data, training the models, and evaluating their performance through multiple quantitative metrics. Our synthetic data set consisted of 10,000 samples with 25 periods spanning 10 ^-3 to 10 ³ seconds, created using statistical parameters derived from real MT data. Each model was trained on apparent resistivity and phase responses to recover subsurface resistivity profiles. The results show that the recurrent neural network architectures (GRU and LSTM) slightly outperform the attention-based Informer model, with the GRU achieving the best overall performance (MSE of 97.67 Ohm.m ² , R² of 0.44). Despite differences in their architectures, all models successfully captured the major resistivity contrasts in the subsurface. When applied to real MT data from the UAE, the models showed promising results in reconstructing subsurface structures. This study demonstrates the viability of deep learning approaches for MT inversion, with potential applications in subsurface imaging for efficient field interpretations.