Noise cancellation of a multi-reference full-wave magnetic resonance sounding signal based on a modified sigmoid variable step size least mean square algorithm  

作　　者：TIAN Bao-feng ZHOU Yuan-yuan ZHU Hui JIANG Chuan-dong YI Xiao-feng 田宝凤;周媛媛;朱慧;蒋川东;易晓峰

机构地区：[1]Key Laboratory of Geo-Exploration Instrumentation, Ministry of Education (Jilin University),Changchun 130026, China [2]College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130026, China

出　　处：《Journal of Central South University》2017年第4期900-911,共12页中南大学学报（英文版）

基　　金：Projects(41204079,41504086)supported by the National Natural Science Foundation of China;Project(20160101281JC)supported by the Natural Science Foundation of Jilin Province,China;Projects(2016M590258,2015T80301)supported by the Postdoctoral Science Foundation of China

摘　　要：Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.

关 键 词：magnetic resonance SOUNDING SIGNAL MULTI-REFERENCE coils adaptive noise CANCELLATION SIGMOID variable step size least mean SQUARE (SVSLMS) 

分 类 号：P631[天文地球—地质矿产勘探]