scholarly journals Mapping of electromagnetic noise in a magnetic resonance sounding context

2015 ◽  
Vol 2015 (1) ◽  
pp. 1-3
Author(s):  
Jakob Juul Larsen ◽  
Esben Dalgaard ◽  
Philip Christiansen ◽  
Esben Auken
Keyword(s):  
Magnetic Resonance ◽  
Electromagnetic Noise ◽  
Magnetic Resonance Sounding

scholarly journals Electromagnetic Noise Suppression of Magnetic Resonance Sounding Combined with Data Acquisition and Multi-Frame Spectral Subtraction in the Frequency Domain

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1254
Author(s):  
Tingting Lin ◽  
Xiaokang Yao ◽  
Sijia Yu ◽  
Yang Zhang
Keyword(s):  
Magnetic Resonance ◽  
Data Acquisition ◽  
Frequency Domain ◽  
Noise Suppression ◽  
Random Noise ◽  
Spectral Subtraction ◽  
Signal Acquisition ◽  
Noisy Signal ◽  
Electromagnetic Noise ◽  
Magnetic Resonance Sounding

As an advanced groundwater detection method, magnetic resonance sounding (MRS) has received more and more attention. However, the biggest challenge is that MRS measurements always suffer with a bad signal-to-noise ratio (SNR). Aiming at the problem of noise interference in MRS measurement, we propose a novel noise-suppression approach based on the combination of data acquisition and multi-frame spectral subtraction (DA-MFSS). The pure ambient noise from the measurement area is first collected by the receiving coil, and then the noisy MRS signal is recorded following the pulse moments transmitting. The procedure of the pure noise and the noisy MRS signal acquisition will be repeated several times. Then, the pure noise and the noisy signal are averaged to preliminarily suppress the noise. Secondly, the averaged pure noise and the noisy signal are divided into multiple frames. The framed signal is transformed into the frequency domain and the spectral subtraction method is applied to further suppress the electromagnetic noise embedded in the noisy MRS signal. Finally, the de-noised signal is recovered by the overlap-add method and inverse Fourier transformation. The approach was examined by numerical simulation and field measurements. After applying the proposed approach, the SNR of the MRS data was improved by 16.89 dB and both the random noise and the harmonic noise were well suppressed.

Focused Multi-layer Inversion of Magnetic Resonance Sounding Data

2013 ◽  
Author(s):  
G. Vignoli ◽  
G. Fiandaca ◽  
A.A. Behroozmand ◽  
E. Auken
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Sounding

scholarly journals Magnetic resonance sounding measurements as posterior information to condition hydrological model parameters: Application to a hard-rock headwater catchment

2020 ◽  
Vol 587 ◽  
pp. 124941 ◽  
Author(s):  
Nolwenn Lesparre ◽  
Jean-François Girard ◽  
Benjamin Jeannot ◽  
Sylvain Weill ◽  
Marc Dumont ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Hydrological Model ◽  
Hard Rock ◽  
Model Parameters ◽  
Magnetic Resonance Sounding ◽  
Headwater Catchment

scholarly journals Random Noise Suppression of Magnetic Resonance Sounding Data with Intensive Sampling Sparse Reconstruction and Kernel Regression Estimation

2019 ◽  
Vol 11 (15) ◽  
pp. 1829 ◽  
Author(s):  
Yao ◽  
Zhang ◽  
Yu ◽  
Zhao ◽  
Sun
Keyword(s):  
Magnetic Resonance ◽  
Field Experiments ◽  
Noise Suppression ◽  
Random Noise ◽  
Kernel Regression ◽  
Sparse Reconstruction ◽  
Regression Estimation ◽  
Magnetic Resonance Sounding ◽  
Kernel Regression Estimation ◽  
Intensive Sampling

The magnetic resonance sounding (MRS) method is a non-invasive, efficient and advanced geophysical method for groundwater detection. However, the MRS signal received by the coil sensor is extremely susceptible to electromagnetic noise interference. In MRS data processing, random noise suppression of noisy MRS data is an important research aspect. We propose an approach for intensive sampling sparse reconstruction (ISSR) and kernel regression estimation (KRE) to suppress random noise. The approach is based on variable frequency sampling, numerical integration and statistical signal processing combined with kernel regression estimation. In order to realize the approach, we proposed three specific sparse reconstructions, namely rectangular sparse reconstruction, trapezoidal sparse reconstruction and Simpson sparse reconstruction. To solve the distortion of peaks and valleys after sparse reconstruction, we introduced the KRE to deal with the processed data by the ISSR. Further, the simulation and field experiments demonstrate that the ISSR-KRE approach is a feasible and effective way to suppress random noise. Besides, we find that rectangular sparse reconstruction and trapezoidal sparse reconstruction are superior to Simpson sparse reconstruction in terms of noise suppression effect, and sampling frequency is positively correlated with signal-to-noise improvement ratio (SNIR). In one case of field experiment, the standard deviation of noisy MRS data was reduced from 1200.80 nV to 570.01 nV by the ISSR-KRE approach. The proposed approach provides theoretical support for random noise suppression and contributes to the development of MRS instrument with low power consumption and high efficiency. In the future, we will integrate the approach into MRS instrument and attempt to utilize them to eliminate harmonic noise from power line.

Bloch–Siegert Effect in Magnetic-Resonance Sounding

2016 ◽  
Vol 47 (9) ◽  
pp. 1021-1032 ◽  
Author(s):  
Oleg A. Shushakov ◽  
Alexander G. Maryasov
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Sounding
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