基于多通道Transformer重构的NMR环境噪声抑制方法OA

Portable nuclear magnetic resonance(NMR)spectrometers are susceptible to external electromagnetic interference(EMI),leading to low signal-to-noise ratio and reduced analytical accuracy.This paper proposes a method based on multiple reference coils and deep learning:multi-channel coils are used to acquire ambient electromagnetic noise in parallel,which is fed into a multi-channel Transformer reconstruction(MCTR)network.The network captures long-range dependencies of ambient electromagnetic signals,predicts and removes the environmental noise in the main receiving coil in real time.Experiments show that this method effectively suppresses noise in both simulated and actual NMR data,improves detection performance,outperforms traditional methods,maintains high signal quality,and has strong robustness.It provides effective support for the application of portable NMR in complex electromagnetic environments,and is expected to promote the development of on-site detection.