Random noise suppression and parameter estimation for Magnetic Resonance Sounding signal based on maximum likelihood estimation

Abstract

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 multi-exponential 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.