Abstract

Abstract. Groundwater is an essential part of the water supply worldwide, and the demands on this water source can be expected to increase in the future. To satisfy the need for resources and to ensure sustainable use of resources, increasingly detailed knowledge of groundwater systems is necessary. However, it is difficult to directly map groundwater with well-established geophysical methods as these are sensitive to both lithology and pore fluid. Surface nuclear magnetic resonance (SNMR) is the only method with a direct sensitivity to water, and it is capable of non-invasively quantifying water content and porosity in the subsurface. Despite these attractive features, SNMR has not been widely adopted in hydrological research, the main reason being an often-poor signal-to-noise ratio, which leads to long acquisition times and high uncertainty in terms of results. Recent advances in SNMR acquisition protocols based on a novel steady-state approach have demonstrated the capability of acquiring high-quality data much faster than previously possible. In turn, this has enabled high-density groundwater mapping with SNMR. We demonstrate the applicability of the new steady-state scheme in three field campaigns in Denmark, where more than 100 SNMR soundings were conducted with a depth of investigation of approximately 30 m. We show how the SNMR soundings enable us to track water level variations at the regional scale, and we demonstrate a high correlation between water levels obtained from SNMR data and water levels measured in boreholes. We also interpret the SNMR results jointly with independent transient electromagnetic (TEM) data, which allows us to identify regions with water bound in small pores. Field practice and SNMR acquisition protocols were optimized during the campaigns, and we now routinely measure high-quality data at 8 to 10 sites per day with a two-person field crew. Together, the results from the three surveys demonstrate that, with steady-state SNMR, it is now possible to map regional variations in water levels with high-quality data and short acquisition times.

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