2D magnetotelluric imaging method based on visionary self-attention mechanism and data science

2D magnetotelluric (MT) imaging detects underground structures by measuring electromagnetic fields. This study tackles two issues in the field: traditional methods’ limitations due to insufficient forward modeling data, and the challenge of multiple solutions in complex scenarios. We introduce an enhanced 2D MT imaging approach with a novel self-attention mechanism, involving: 1. Generating diverse geophysical models and responses to increase data variety and volume. 2. Creating a Swin–Unet-based 2D MT Imaging network with self-attention for better modeling and relation capture, incorporating a MT sample generator using real data to lessen large-scale supervised training dependence, and refining the loss function for optimal validation. This method also includes eliminating MT background response to boost training efficiency and reduce training time. 3. Applying a transverse electric/transverse magnetic method for comprehensive 2D MT data response. Tests show that our method greatly improves 2D MT imaging’s accuracy and efficiency, with excellent generalization.