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Open AccessArticle

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

1,2, 2,3, 1,2, 1,2 and 1,2,*
1
College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China
2
Key Laboratory of Geo-exploration Instruments, Ministry of Education of China, Changchun 130061, China
3
College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(15), 1829; https://doi.org/10.3390/rs11151829
Received: 26 June 2019 / Revised: 22 July 2019 / Accepted: 27 July 2019 / Published: 5 August 2019
(This article belongs to the Special Issue Recent Advances in Subsurface Sensing Technologies)
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Abstract

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. View Full-Text
Keywords: magnetic resonance sounding (MRS); random noise suppression; sampling record method; signal estimation; intensive sampling; sparse reconstruction; kernel regression estimation magnetic resonance sounding (MRS); random noise suppression; sampling record method; signal estimation; intensive sampling; sparse reconstruction; kernel regression estimation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yao, X.; Zhang, J.; Yu, Z.; Zhao, F.; Sun, Y. Random Noise Suppression of Magnetic Resonance Sounding Data with Intensive Sampling Sparse Reconstruction and Kernel Regression Estimation. Remote Sens. 2019, 11, 1829.

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