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• W2948759296 abstract "Surface and underground nuclear magnetic resonance (UNMR) method has the advantage of direct and quantitative detection of groundwater and has been widely used in water resource survey and advance detection of water sources causing disaster in underground space. However, the UNMR signal is extremely weak in tunnels or mines. The signal-to-noise ratio (SNR) of received data at one single record is even less than −20 dB because of the strong environmental noise. A nearby reference noise cancellation (NRNC) method is proposed in this study to improve SNR. The method calculates the transfer function between the reference coil and the detection coil by using the second half of the received data to determine the noise estimation in the first half of the detection coil. The non-linear fitting method is used to estimate and remove the UNMR signal in the noise estimation and avoid the loss of the UNMR signal in the detection coil. We compare the nearby reference coil layouts of coaxial, non-separation tri-axial, and separation tri-axial types, as well as the NRNC results of the number and distance of different reference coils, through a large number of experiments. The non-separation tri-axial reference coil is optimal. The experimental results show that the SNR can be increased significantly by 18.1 dB, and the uncertainty in UNMR signal parameter estimation is evidently decreased. We prove that the NRNC algorithm is superior to the existing remote reference noise cancellation and power frequency modeling algorithms, and discuss that the improvement in SNR will be beneficial to improve the accuracy of inversion results." @default.
• W2948759296 created "2019-06-14" @default.
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• W2948759296 date "2019-01-01" @default.
• W2948759296 modified "2023-10-16" @default.
• W2948759296 title "Improving the Signal-to-Noise Ratio of Underground Nuclear Magnetic Resonance Data Based on the Nearby Reference Noise Cancellation Method" @default.
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• W2948759296 doi "https://doi.org/10.1109/access.2019.2920845" @default.
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