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. To tackle this issue, a deep learning-based denoising network, called BA-ATEMNet, is introduced, using Bayesian learning alongside a multi-head self-attention mechanism to effectively denoise ATEM signals. The incorporation of a multi-head self-attention mechanism significantly enhances the feature extraction capabilities of the convolutional neural network, allowing for improved differentiation between signal and noise. Moreover, the combination of Bayesian learning with a weighted integration of prior knowledge and SNR enhances the model's performance across varying noise levels, thereby increasing its adaptability to complex noise environments. Our experimental findings indicate that BA-ATEMNet surpasses other denoising models in both single and multiple noise conditions, achieving an average signal-to-noise ratio of 37.21 dB in multiple noise scenarios. This notable enhancement in SNR, compared to the next best model, which achieves an average SNR of 36.10 dB, holds substantial implications for ATEM-based mineral exploration and geological surveys.