Parameter Estimation of Surface Nuclear Magnetic Resonance Signals Based on Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Technique

YU Xiaohui; FENG Hai; TIAN Baofeng; SUN Haixin; SUN Xiaodong

doi:10.11999/JEIT230102

Volume 46 Issue 2

Feb. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(2): 720-727

YU Xiaohui, FENG Hai, TIAN Baofeng, SUN Haixin, SUN Xiaodong. Parameter Estimation of Surface Nuclear Magnetic Resonance Signals Based on Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Technique[J]. Journal of Electronics & Information Technology, 2024, 46(2): 720-727. doi: 10.11999/JEIT230102

Citation:

YU Xiaohui, FENG Hai, TIAN Baofeng, SUN Haixin, SUN Xiaodong. Parameter Estimation of Surface Nuclear Magnetic Resonance Signals Based on Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Technique[J]. Journal of Electronics & Information Technology, 2024, 46(2): 720-727. doi: 10.11999/JEIT230102

Citation:

YU Xiaohui, FENG Hai, TIAN Baofeng, SUN Haixin, SUN Xiaodong. Parameter Estimation of Surface Nuclear Magnetic Resonance Signals Based on Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Technique[J]. Journal of Electronics & Information Technology, 2024, 46(2): 720-727. doi: 10.11999/JEIT230102

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Parameter Estimation of Surface Nuclear Magnetic Resonance Signals Based on Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Technique

doi: 10.11999/JEIT230102 cstr: 32379.14.JEIT230102

1.
School of Communication Engineering, Jilin University, Changchun 130022, China
2.
College of Instrumentation & Electrical Engineering/Key Laboratory of Geo-Exploration Instrumentation, Ministry of Education, Jilin University, Changchun 130026, China
3.
School of Electronic and Information Engineering, Changchun University, Changchun 130022, China

Funds: The National Natural Science Foundation of China (42074217)

Received Date: 2023-02-24
Rev Recd Date: 2023-07-12

Available Online: 2023-07-20

Publish Date: 2024-02-29

Abstract

Abstract

In the Surface Nuclear Magnetic Resonance (SNMR) water searching system, the parameters of SNMR signals can be used to predict the water storage, electrical conductivity, pore structure of underground aquifer. However, the SNMR signals collected on site are very weak in practical application, which is easy to be interfered by environmental noise, resulting in the inability to directly obtain the parameters of SNMR signal. To solve this problem, an estimation method of SNMR signal parameters based on Total Least Squares-Estimation of Signal Parameters via Rotational Invariance Technique (TLS-ESPRIT) is proposed in this paper. Based on the similar signal characteristics of harmonic noise and SNMR signals, a mixed signal model consisting of multiple sinusoidal attenuation signals is constructed. TLS-ESPRIT is used to transform the problem of extracting mixed signal parameters into a generalized eigenvalue solution of a rotation invariant matrix, in order to obtain the Lamor frequency and the relaxation time of the SNMR signal, and its initial amplitude and phase are obtained by combining the least squares method. The experimental results of simulated signal and measured signal show that the proposed method can estimate the parameters of SNMR signal mixed with random noise and power frequency harmonic noise. Compared with the traditional harmonic modeling method, the parameter extraction accuracy is better.

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References(20)

References

[1]	TRUSHKIN D V, SHUSHAKOV O A, and LEGCHENKO A V. The potential of a noise-reducing antenna for surface NMR groundwater surveys in the earth’s magnetic field[J]. Geophysical Prospecting, 1994, 42(8): 855–862. doi: 10.1111/j.1365-2478.1994.tb00245.x.
[2]	RANGEL R C, PARSEKIAN A D, FARQUHARSON L M, et al. Geophysical observations of Taliks below drained lake basins on the arctic coastal plain of Alaska[J]. Journal of Geophysical Research:Solid Earth, 2021, 126(3): e2020JB020889. doi: 10.1029/2020JB020889.
[3]	LI Mengna, ZENG Yijian, LUBCZYNSKI M W, et al. A first investigation of hydrogeology and hydrogeophysics of the Maqu catchment in the Yellow River source region[J]. Earth System Science Data, 2021, 13(10): 4727–4757. doi: 10.5194/essd-13-4727-2021.
[4]	LU Kai, LI Fan, PAN Jianwei, et al. Using electrical resistivity tomography and surface nuclear magnetic resonance to investigate cultural relic preservation in Leitai, China[J]. Engineering Geology, 2021, 285: 106042. doi: 10.1016/J.ENGGEO.2021.106042.
[5]	BEHROOZMAND A A, KEATING K, and AUKEN E. A review of the principles and applications of the NMR technique for near-surface characterization[J]. Surveys in Geophysics, 2015, 36(1): 27–85. doi: 10.1007/s10712-014-9304-0.
[6]	KEATING K, WALSH D O, and GRUNEWALD E. The effect of magnetic susceptibility and magnetic field strength on porosity estimates determined from low-field nuclear magnetic resonance[J]. Journal of Applied Geophysics, 2020, 179: 104096. doi: 10.1016/j.jappgeo.2020.104096.
[7]	KREMER T, IRONS T, MÜLLER-PETKE M, et al. Review of acquisition and signal processing methods for electromagnetic noise reduction and retrieval of surface nuclear magnetic resonance parameters[J]. Surveys in Geophysics, 2022, 43(4): 999–1053. doi: 10.1007/S10712-022-09695-3.
[8]	LEGCHENKO A and VALLA P. A review of the basic principles for proton magnetic resonance sounding measurements[J]. Journal of Applied Geophysics, 2002, 50(1/2): 3–19. doi: 10.1016/S0926-9851(02)00127-1.
[9]	COSTABEL S and MÜLLER-PETKE M. Despiking of magnetic resonance signals in time and wavelet domains[J]. Near Surface Geophysics, 2014, 12(2): 185–198. doi: 10.3997/1873-0604.2013027.
[10]	万玲, 张扬, 林君, 等. 基于能量运算的磁共振信号尖峰噪声抑制方法[J]. 地球物理学报, 2016, 59(6): 2290–2301. doi: 10.6038/cjg20160631. WAN Ling, ZHANG Yang, LIN Jun, et al. Spikes removal of magnetic resonance sounding data based on energy calculation[J]. Chinese Journal of Geophysics, 2016, 59(6): 2290–2301. doi: 10.6038/cjg20160631.
[11]	LEGCHENKO A and VALLA P. Removal of power-line harmonics from proton magnetic resonance measurements[J]. Journal of Applied Geophysics, 2003, 53(2/3): 103–120. doi: 10.1016/S0926-9851(03)00041-7.
[12]	LARSEN J J, DALGAARD E, and AUKEN E. Noise cancelling of MRS signals combining model-based removal of powerline harmonics and multichannel Wiener filtering[J]. Geophysical Journal International, 2014, 196(2): 828–836. doi: 10.1093/gji/ggt422.
[13]	田宝凤, 朱慧, 易晓峰, 等. 基于谐波建模和自相关的磁共振信号消噪与提取方法研究[J]. 地球物理学报, 2018, 61(2): 767–780. doi: 10.6038/cjg2018L0091. TIAN Baofeng, ZHU Hui, YI Xiaofeng, et al. Denoising and extraction method of magnetic resonance sounding signal based on adaptive harmonic modeling and autocorrelation[J]. Chinese Journal of Geophysics, 2018, 61(2): 767–780. doi: 10.6038/cjg2018L0091.
[14]	庄双勇, 赵伟, 赵东芳, 等. 一种基于滑窗TLS-ESPRIT算法的超谐波动态分析方法[J]. 计量学报, 2020, 41(4): 475–483. doi: 10.3969/j.issn.1000-1158.2020.04.014. ZHUANG Shuangyong, ZHAO Wei, ZHAO Dongfang, et al. A supraharmonics dynamic analysis method based on sliding-window TLS-ESPRIT algorithm[J]. Acta Metrologica Sinica, 2020, 41(4): 475–483. doi: 10.3969/j.issn.1000-1158.2020.04.014.
[15]	CHEN Jian, JIN Tao, MOHAMED M A, et al. An adaptive TLS-ESPRIT algorithm based on an S-G filter for analysis of low frequency oscillation in wide area measurement systems[J]. IEEE Access, 2019, 7: 47644–47654. doi: 10.1109/ACCESS.2019.2908629.
[16]	SAMAL S K and SUBUDHI B. New signal subspace approach to estimate the inter-area oscillatory modes in power system using TLS-ESPRIT algorithm[J]. IET Generation, Transmission & Distribution, 2019, 13(18): 4123–4140. doi: 10.1049/iet-gtd.2018.6401.
[17]	张硕, 杨君, 葛鹏程, 等. 基于Hankel矩阵改进的TLS-ESPRIT多频带融合处理[J]. 电光与控制, 2022, 29(9): 90–95. doi: 10.3969/j.issn.1671-637X.2022.09.018. ZHANG Shuo, YANG Jun, GE Pengcheng, et al. TLS-ESPRIT multiband fusion processing based on hankel matrix improvement[J]. Electronics Optics &Control, 2022, 29(9): 90–95. doi: 10.3969/j.issn.1671-637X.2022.09.018.
[18]	张小宽, 郑舒予, 奚之飞, 等. 基于改进LS-ESPRIT算法的GTD模型参数估计与RCS重构[J]. 电子与信息学报, 2020, 42(10): 2493–2499. doi: 10.11999/JEIT190747. ZHANG Xiaokuan, ZHENG Shuyu, XI Zhifei, et al. GTD model parameters estimation and RCS reconstruction based on the improved LS-ESPRIT algorithm[J]. Journal of Electronics &Information Technology, 2020, 42(10): 2493–2499. doi: 10.11999/JEIT190747.
[19]	GRUNEWALD E, KNIGHT R, and WALSH D. Advancement and validation of surface nuclear magnetic resonance spin-echo measurements of T₂[J]. Geophysics, 2014, 79(2): EN15–EN23. doi: 10.1190/geo2013-0105.1.
[20]	SRIVASTAVA A K, TIWARI A N, and SINGH S N. Harmonic/interharmonic estimation using standard deviation assisted ESPRIT method[J]. COMPEL:The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 2021, 40(6): 1067–1083. doi: 10.1108/COMPEL-03-2021-0108.