Review of Acquisition and Signal Processing Methods for Electromagnetic Noise Reduction and Retrieval of Surface Nuclear Magnetic Resonance Parameters

• Published in: Surveys in geophysics Vol. 43; no. 4; pp. 999 - 1053
• Main Authors: Kremer, Thomas, Irons, Trevor, Müller-Petke, Mike, Juul Larsen, Jakob
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.08.2022; Springer Nature B.V
• Subjects: Astronomy; Best practice; Earth and Environmental Science; Earth Sciences; Electromagnetic noise; Geophysics/Geodesy; Groundwater; Information processing; Liquid phases; Magnetic resonance; Methods; NMR; Noise reduction; Nuclear magnetic resonance; Observations and Techniques; Signal processing; Signal to noise ratio; Surface nuclear magnetic resonance; Signal processing; Signal-to-noise ratio; Review; Electromagnetic noise
• ISSN: 0169-3298; 1573-0956
• DOI: 10.1007/s10712-022-09695-3

Surface nuclear magnetic resonance (sNMR) is an electromagnetic hydrogeophysical method directly sensitive to liquid phase water in the upper ≈ 100 m of the subsurface. For this reason, sNMR is a uniquely capable of unambiguous exploration and quantitative characterization of groundwater and its structural environment in the near-surface. In spite of these physical attributes, the method suffers from notoriously low signal-to-noise ratio (SNR) which can limit its application. A large span of research has therefore been dedicated to sNMR developments including instrument innovations, acquisition methodologies and signal processing techniques which improve the SNR of the method and expand its scope of application outside the research world. Towards this goal, we include a description of community-developed best practice techniques and strategies that can be relied upon to successfully gather and analyse sNMR data sets in a production setting. Complementing this, we provide a comprehensive review of past, recent, and on-going approaches that—while not currently widely adopted—present promising features should further research be dedicated to their development. As such, the objective of this paper is to provide both newcomers and specialists of the sNMR method a clear view of the existing signal processing techniques and strategies along with a structured proposition of promising research leads and future perspectives to be explored.