Underground nuclear magnetic resonance (NMR) is introduced to detect the risk of groundwater-induced disasters in the underground engineering such as tunnels and mines. However, underground NMR is in practice often limited to the extremely low signal-to-noise ratios (SNRs). On the one hand, small coils are necessary to be used to detect water in the narrow underground space, which decreases the amplitude of the excited signal. On the other hand, the weak signal is submerged in quite serious electromagnetic noise which is generated from the electrical installations. The low SNRs emphasize the importance of using an optimal post-processing strategy to obtain the reliable underground NMR data. The objective of this paper is to explain the processing of underground NMR data taking the detection of the underground river in Doumo Tunnel as an example. We have evaluated the noise condition in Doumo Tunnel and the noise level of 0.6760 nV/m2 was found in this area. At such a high noise level, the reliable underground NMR signal is difficult to be extracted and the credible depth profile of water content is unable to be provided. Then, we have analyzed the noise interference. Although de-spiking algorithm and reference-based noise cancellation method were applied to remove the major noise sources, the underground NMR signal is still invisible. There is still a lot of additive noise remained, so time-frequency peak filtering method is further used to suppress the remaining noise. The performance of the proposed post-processing strategy is tested on the underground NMR data from the underground river. The result was consistent with the geological structure, which is demonstrated to be able to directly provide a security pre-warning of the underground engineering.
Improved post-processing strategy for MOLLI based tissue characterization allows application in patients with dyspnoe and impaired left ventricular function
