All-optical filtering of nuclear magnetic resonance logging data based on a diffractive neural network
Yuxuan Mao, Yiming Zhou, Yi Ding, Jingjing Cheng, Wenzhong Liu, Ryszard R. Buczynski, Xiaoqun Yuan · Applied Optics · 2025
The signal-to-noise ratio (SNR) of nuclear magnetic resonance (NMR) logging data is very low; filtering methods based on U-Net and MsEDNet are always employed to extract information for logging stratigraphic evaluation. Since it is difficult to adjust the parameters of U-Net and MsEDNet for logging data, the filtered results suffer from low SNR and distortion. To address the problem, this paper proposes an optical diffractive neural network (DNN)-based filtering system for NMR logging data, which can protect the signal's integrity and avoid degradation of the neural network. In this system, the Sinkhorn-Knopp algorithm upgrades one-dimensional echo data into two-dimensional data for optical diffractive computing. The proposed residue DNN separates the noise in NMR logging effectively. Therefore, the resulting SNR of our method is higher than that of U-Net and MsEDNet. Simulation and experimental results demonstrate the effectiveness of the proposed method.