Review of Acquisition and Signal Processing Methods for Electromagnetic Noise Reduction and Retrieval of Surface Nuclear Magnetic Resonance Parameters
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 str...
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Published in | Surveys in geophysics Vol. 43; no. 4; pp. 999 - 1053 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Dordrecht
Springer Netherlands
01.08.2022
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 0169-3298 1573-0956 |
DOI: | 10.1007/s10712-022-09695-3 |