BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals.

Airborne transient electromagnetic (ATEM) surveys provide a fast, flexible approach for identifying conductive metal deposits across a variety of intricate terrains. Nonetheless, the secondary electromagnetic response signals captured by ATEM systems frequently suffer from numerous noise interferences, which impede effective data processing and interpretation. Traditional denoising methods often fall short in addressing these complex noise backgrounds, leading to less-than-optimal signal extraction.