An Intelligent Denoising Method for Nuclear Magnetic Resonance Logging Measurement Based on Residual Network

Nuclear magnetic resonance (NMR) is an important geophysical technique for the structure and properties measurement of porous media. The small-diameter NMR logging technology has been emerged as a useful tool in terms of evaluating the shallow surface hydrogeological parameters. However, the NMR logging is always suffering from the low signal-to-noise ratio (SNR). This article proposes a denoising network for NMR logging signal based on the combination of the convolutional neural network (CNN) and residual learning, called as Dn-residual network (ResNet). First, a large amount training datasets are constructed according to the characteristics of the NMR logging signal and noise, which is used to train the network. Second, the useful features of the NMR logging signals are learned by the network during training processing, in which the residual learning mechanism is employed to improve the training efficiency and the denoising performance. As a result, the Dn-ResNet may realize adaptive denoising without manual filter parameters tuning, prior knowledge of NMR logging signals, and preprocessing of the original noisy signals. The performance of the proposed denoising network is demonstrated on both the synthetic NMR logging signals and field data. The effective signals are obtained from the noisy signals at different SNRs. In addition, an attempt to use the well-trained Dn-ResNet to deal with a single record provides similar results compared to 200 stacks. It shows that the number of stacks may be reduced to shorten the measurement time and improve the measurement efficiency. The results show the effectiveness and robustness of the proposed approach, which enables the technology of deep learning to be developed in the application of NMR logging data processing.