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首页> 外文期刊>Journal of Applied Geophysics >Random noise suppression and parameter estimation for Magnetic Resonance Sounding signal based on maximum likelihood estimation
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Random noise suppression and parameter estimation for Magnetic Resonance Sounding signal based on maximum likelihood estimation

机译：基于最大似然估计的磁共振探测信号随机噪声抑制与参数估计

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摘要

Magnetic resonance sounding (MRS) is a promising geophysical method for direct detection and quantification of groundwater. However, the application of MRS is considerably limited due to its vulnerability to electromagnetic interference. In this paper, the statistical method of maximum likelihood estimation (MLE) is introduced to estimate nuclear magnetic resonance (NMR) parameters and suppress random noise in MRS. Using synthetic NMR signals and noise models, we analyse the factors that influence the denoising effect of MLE. The results show that MLE denoising can be effective even when the random noise deviates from a Gaussian distribution. A multiexponential NMR signal may reduce the accuracy of MLE-based parameter estimation if a bad initial guess is used, but it has little effect on the suppression of random noise. Harmonic noise can reduce the effect of MLE, while impulsive noise has little effect on MLE. In addition, increasing the recording time or frequency increases the amount of data of the MRS signal, thus improving the effectiveness of the MLE method. Field examples show that this method can effectively suppress random noise interference with less stacking compared with traditional methods, thus greatly shortening the working time of MRS and improving its working efficiency. (C) 2020 Elsevier B.V. All rights reserved.
机译：磁共振声探测（MRS）是一种有前途的地球物理方法，用于直接检测和定量地下水。然而，由于其易受电磁干扰的脆弱性，MRS的应用程度大得多。在本文中，引入了最大似然估计（MLE）的统计方法来估计核磁共振（NMR）参数并抑制MRS中的随机噪声。使用合成NMR信号和噪声模型，我们分析影响MLE的去噪效果的因素。结果表明，即使当随机噪声偏离高斯分布时，MLE去噪也会有效。如果使用较差的初始猜测，则多因素NMR信号可以降低基于MLE的参数估计的准确性，但它对抑制随机噪声几乎没有影响。谐波噪音可以减少MLE的效果，而脉冲噪声对MLE影响不大。另外，增加记录时间或频率增加了MRS信号的数据量，从而提高了MLE方法的有效性。领域的示例表明，与传统方法相比，该方法可以有效地抑制随机噪声干扰较少的堆叠，从而大大缩短了MRS的工作时间并提高了其工作效率。 （c）2020 Elsevier B.V.保留所有权利。

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