Complex envelope retrieval for surface nuclear magnetic resonance data using spectral analysis

Abstract

Aquifer properties can be obtained from envelopes of surface nuclear magnetic resonance (NMR) signals, but this demands high-quality data. To retrieve reliable envelopes using synchronous detection from the intrinsically low signal-to-noise ratio (SNR) surface NMR recordings, a variety of signal processing techniques are employed to mitigate noise. We present a different approach to retrieve complex envelopes using spectral analysis and a sliding window, which can potentially improve SNR significantly. The complex envelope is composed of the spectral values at the Larmor frequency found through the Fourier transform of surface NMR data using a sliding window. We discuss how to maximize the SNR of envelope by selecting the optimum length and shape of the sliding window. An accompanying method for determining the Larmor frequency is presented and we address how noise can deteriorate the envelope retrieval in spectral analysis. Results obtained from synthetic models and field measurements in low and high noise environments reveal that the proposed method not only improves the accuracy and efficiency of envelope retrieval, but also eliminates the transient distortion of early-time signal caused by the filtering procedure.

Envelope Extraction Algorithm for Magnetic Resonance Sounding Signals Based on Adaptive Gaussian Filters

“…However, both methods suffer from mode mixing issues and only achieved satisfactory results at high signal-to-noise ratios. Liu et al [22] proposed a spectral analysis (SA) method based on sliding windows for recovering the envelopes of MRS signals in noisy environments, but this method is not suitable for extracting the envelopes of MRS signals with multiple components. Tian et al [23] introduced an extraction algorithm using adaptive local iterative filtering using the Fokker-Planck equation solution as the filtering function.…”

Steady‐State Surface NMR for Mapping of Groundwater

“…Unfortunately, the surface NMR signal is inherently low amplitude and noise often swamps the signal. Significant advances have been made in surface NMR signal processing (Larsen, 2016; Liu et al., 2019a, 2019b; Müller‐Petke & Costabel, 2014; Walsh, 2008), in receive (Behroozmand et al., 2016; Kremner et al., 2020; Trushkin et al., 1994), and transmit schemes (Grunewald & Walsh, 2013; Legchenko et al., 2010; Walbrecker, Hertrich, & Green, 2011; Walbrecker, Lehmann‐Horn, & Green, 2011). Yet noise remains one of the chief challenges in surface NMR.…”

“…The NMR signal's amplitude is proportional to the abundance of water at depth, and its time-dependence to properties of the pore-scale environment. Figures 1a and 1b illustrate an example surface NMR system (Larsen et al, 2020;Liu et al, 2019aLiu et al, , 2019b.…”

Random Noise Suppression of Magnetic Resonance Sounding Data with Intensive Sampling Sparse Reconstruction and Kernel Regression Estimation

“…However, when the MRS instrument is exploited for groundwater exploration, the received signal is particularly weak and easily degraded in ambient electromagnetic interference [14]. Furthermore, obtaining the MRS signal with nanovolt-level amplitude is a challenging area in regards to strong electromagnetic noise [15].…”