Application of Hilbert-Huang decomposition to reduce noise and characterize for NMR FID signal of proton precession magnetometer

TL;DR

This paper introduces a novel application of Hilbert-Huang Transform to effectively reduce noise and analyze NMR FID signals, enhancing magnetic field measurement and related applications.

Contribution

It is the first to apply HHT for FID signal feature analysis, demonstrating improved noise suppression and signal component separation over traditional methods.

Findings

HHT effectively suppresses interference in FID signals.

The method accurately extracts signal components for magnetic anomaly detection.

Compared to traditional methods, HHT provides better noise reduction and feature extraction.

Abstract

The parameters in a nuclear magnetic resonance (NMR) free induction decay (FID) signal contain information that is useful in magnetic field measurement, magnetic resonance sounding (MRS) and other related applications. A real time sampled FID signal is well modeled as a finite mixture of exponential sequences plus noise. We propose to use the Hilbert-Huang Transform (HHT) for noise reduction and characterization, where the generalized Hilbert-Huang represents a way to decompose a signal into so-called intrinsic mode function (IMF) along with a trend, and obtain instantaneous frequency data. Moreover, the HHT for an FID signal's feature analysis is applied for the first time. First, acquiring the actual untuned FID signal by a developed prototype of proton magnetometer, and then the empirical mode decomposition (EMD) is performed to decompose the noise and original FID. Finally, the HHT is applied to the obtained IMFs to extract the Hilbert energy spectrum, to indicate the energy distribution of the signal on the frequency axis. By theory analysis and the testing of an actual FID signal, the results show that, compared with general noise reduction methods such as auto correlation and singular value decomposition (SVD), combined with the proposed method can further suppress the interfered signals effectively, and can obtain different components of FID signal, which can use to identify the magnetic anomaly, the existence of groundwater etc. This is a very important property since it can be exploited to separate the FID signal from noise and to estimate exponential sequence parameters of FID signal.