Surface Nuclear Magnetic Resonance Data Research Articles

Observation of spikelets in steady-state surface nuclear magnetic resonance data

Spikelets in nuclear magnetic resonance (NMR) data occur at predictable frequencies dependent on the repetition time of the excitation sequence. Although spikelets are well documented in other NMR fields, we report their presence in steady-state surface NMR data for the first time. These observations are accompanied by analytical developments to understand and predict their behavior, which follow directly from existing steady-state surface NMR modeling. We show that spikelets represent copies of the surface NMR signal occurring at multiple locations in the frequency domain, including locations that are distinct from the Larmor frequency. These features are shown to be detectable without requiring additional effort in the field and are shown to be readily processed and modeled with only minor modifications to the processing and modeling workflows. Finally, field spikelet data are also inverted to demonstrate that these data can be fit using subsurface models consistent with a reference surface NMR inversion.

Technical note: High-density mapping of regional groundwater tables with steady-state surface nuclear magnetic resonance – three Danish case studies

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.

Signal Processing Steady-State Surface NMR Data

Surface nuclear magnetic resonance (NMR) is a unique technique to study groundwater as it provides direct insights into water content and pore-scale properties. However, surface NMR suffers from an extremely weak signal that limits mapping speeds and can prohibit measurements in high ambient noise environments. It has recently been demonstrated that steady-state sequences can deliver orders of magnitude signal-to-noise ratio enhancement in comparison to standard methods. Most notably, the steady-state approach collapses the surface NMR signal an extremely narrow frequency band, which leads to vastly improved noise rejection. In this paper, we develop the signal processing chain for steady-state surface NMR data. Each step in the signal processing chain, transient culling, pulse windowing, de-spiking, powerline harmonics subtraction and spectral analysis are explained in detail. We demonstrate how error estimation can be efficiently performed using the power spectral density in the vicinity of the transmitting frequency. We also demonstrate how off-resonance excitation can be determined by locating the peak spectrum of surface NMR signal with an array of phase-corrected discrete Fourier transforms. The presented signal processing methods can be easily implemented and applied to extract signal features for quality control and inversion procedures. We give a field example where data collected with steady-state pulse sequences are processed and inverted.

A high precision finite-element forward solver for surface nuclear magnetic resonance incorporating conductivity changes and surface-topography variations

Surface nuclear magnetic resonance (SNMR) is a geophysical method that can be used directly for detecting groundwater resources, and it has attracted the attention of many scholars. In this paper, we propose a new effective algorithm for numerical modeling of 3D SNMR data for arbitrary topography in a conductive medium. We adopt a total-field algorithm for solving the quasi-static variant of Maxwell’s equation and handle a complex-shaped loop source by discretizing the transmitter into electric dipoles, which can be further easily discretized into electric dipoles along the three directions of the Cartesian coordinate system. To solve the 3D SNMR forward-modeling problem quickly and accurately, a new element-integration system based on a new symmetric orthogonal rule is used for calculating the sensitivity (i.e., kernel) functions of all elements. The new rule is based on a special arrangement involving a cubic close-packed lattice structure and is characterized by fast convergence, positive weight, and symmetry. We apply the developed numerical algorithm to SNMR tomography of several typical hydrogeological models. The synthetic results show that higher precision can be achieved with few grids and nodes without increasing the computation time by using the new integration algorithm. In addition, we find that the topography and conductivity can affect the SNMR response, which needs to be considered while interpreting SNMR data.

Removal of Powerline Noise in Geophysical Datasets With a Scientific Machine-Learning Based Approach

The most common noise in geophysical data is probably the interference from powerlines. This noise manifests itself as a sinusoidal signal oscillating at the fundamental 50 Hz or 60 Hz frequency of the power grid and as harmonic components oscillating at integer multiples. Many different mitigation strategies, tailored for the specific geophysical method, have been developed to target powerline noise. One method that applies to fully sampled data is model-based subtraction, where a model of the powerline noise is fitted to the noisy data set and subsequently subtracted. In most cases, this leads to significant improvements in the signal-to-noise ratio. However, the determination of the powerline model parameters, in particular the fundamental powerline frequency, is computationally expensive, as it requires repeated solutions of a least-squares problem. We demonstrate that the powerline frequency can be directly predicted with a scientific machine-learning based approach. We work on both time domain induced polarization and surface nuclear magnetic resonance data. We use a different network for each method to trade-off prediction accuracy and prediction speed. In both cases, the prediction accuracy is fully on par with standard methods, and we obtain speed-ups by factors of 400 and 10 for the two types of data.

Fast Removal of Powerline Harmonic Noise From Surface NMR Datasets Using a Projection-Based Approach on Graphical Processing Units

Surface nuclear magnetic resonance (NMR) measurements are notorious for their low signal-to-noise ratio (SNR). Powerlines are probably the most common source of noise and give the greatest contribution to noise levels. The noise from powerlines manifests itself as sinusoidal signals oscillating at the fundamental powerline frequency (50 or 60 Hz) and at integer multiples of this frequency. Modeling and subtraction of the powerline noise have been demonstrated as a highly applicable method for improving SNR and are common practice today. However, the methods used to determine the parameters of the powerline noise are computationally expensive. Consequently, it is difficult to do real-time noise removal during the acquisition of field data and, therefore, also difficult to do a real-time quality inspection of data. Here, we demonstrate how the removal of powerline noise in surface NMR data can be significantly faster. We obtain this through two new developments. First, we apply a projection-based method to determine the powerline model, which is twice as fast as the commonly applied least-squares solution of a matrix equation. Second, we obtain a further 10–25 times speed-up by exploiting the high-performance parallel computations offered by graphical processing units (GPUs). We demonstrate the method on a noise-only field dataset with an embedded synthetic NMR signal.

Forward Modeling Steady-State Free Precession in Surface NMR

In efforts to map water at depth, steady-state free-precession (SSFP) sequences promise to rapidly increase data acquisition rates in the practice of surface nuclear magnetic resonance (NMR). Unlike conventional surface NMR excitation schemes, pulses in SSFP are transmitted so frequently that the nuclear magnetization of hydrogen in water can not return to its natural alignment with the earth’s ambient magnetic field, and instead achieve a steady-state; a dynamic equilibrium between pulses. Unfortunately, the traditional formulations of SSFP sequences and the full surface NMR forward models are not immediately compatible with each other. Firstly, the traditional analysis of SSFP sequences assume that relaxation during pulse (RDP) effects are negligible, which is not always valid in surface NMR. Secondly, even for single pulse measurements, the surface NMR forward model can be computationally demanding; this challenge scales with the number of pulses. Here we investigate the incorporation of RDP effects on the dynamic equilibrium of SSFP measurements. This is then incorporated into the full surface NMR forward model by deriving analytical expressions to directly predict processed surface NMR data. The model is validated by jointly inverting an extensive and diverse suite of SSFP measurements; 12 distinct sequences each with 16 pulse moments. The inverted model has a data misfit of 0.99 and is consistent with models derived from standard NMR data. The ability of our forward model to reproduce diverse signals and jointly invert them is a strong indication of its validity.

Coupled magnetic resonance and electrical resistivity tomography: An open-source toolbox for surface nuclear-magnetic resonance

Nuclear-magnetic resonance (NMR) is a powerful tool for groundwater system imaging. Ongoing developments in surface NMR, for example, multichannel devices, allow for investigations of increasingly complex subsurface structures. However, with the growing complexity of field cases, the availability of appropriate software to accomplish the in-depth data analysis becomes limited. The open-source Python toolbox coupled magnetic resonance and electrical resistivity tomography (COMET) provides the community with a software for modeling and inversion of complex surface NMR data. COMET allows the NMR parameters’ water content and relaxation time to vary in one dimension or two dimensions and accounts for arbitrary electrical resistivity distributions. It offers a wide range of classes and functions to use the software via scripts without in-depth programming knowledge. We validated COMET to existing software for a simple 1D example. We discovered the potential of COMET by a complex 2D case, showing 2D inversions using different approximations for the resistivity, including a smooth distribution from electrical resistivity tomography (ERT). The use of ERT-based resistivity results in similar water content and relaxation time images compared with using the original synthetic block resistivity. We find that complex inversion may indicate incorrect resistivity by non-Gaussian data misfits, whereas amplitude inversion shows well-fitted data, but leading to erroneous NMR models.

Seasonal Subsurface Thaw Dynamics of an Aufeis Feature Inferred From Geophysical Methods

AbstractAufeis are sheets of ice unique to cold regions that originate from repeated flooding and freezing events during the winter. They have hydrological importance associated with summer flows and winter insulation, but little is known about the seasonal dynamics of the unfrozen sediment layer beneath them. This layer may support perennial groundwater flow in regions with otherwise continuous permafrost. For this study, ground penetrating radar (GPR) were collected in September 2016 (maximum thaw) and April 2017 (maximum frozen) at the Kuparuk aufeis field on the North Slope of Alaska. Supporting surface nuclear magnetic resonance data were collected during the maximum frozen campaign. These point‐in‐time geophysical data sets were augmented by continuous subsurface temperature data and periodic Structure‐from‐Motion digital elevation models collected seasonally. GPR and difference digital elevation model data showed up to 6 m of ice over the sediment surface. Below the ice, GPR and nuclear magnetic resonance identified regions of permafrost and regions of seasonally frozen sediment (i.e., the active layer) underlain by a substantial lateral talik that reached >13‐m thickness. The seasonally frozen cobble layer above the talik was typically 3‐ to 5‐m thick, with freezing apparently enabled by relatively high thermal diffusivity of the overlying ice and rock cobbles. The large talik suggests that year‐round groundwater flow and coupled heat transport occurs beneath much of the feature. Highly permeable alluvial material and discrete zones of apparent groundwater upwelling indicated by geophysical and ground temperature data allows direct connection between the aufeis and the talik below.

A cautionary tale: How phase compensation during surface nuclear magnetic resonance inversion conceals forward modelling errors

Surface nuclear magnetic resonance (NMR) data are sensitive to key hydrogeological parameters including water content and pore size. The measured data are modelled as a complex sinusoidal exponential decay where the phase is a function of the physics of the experiment and instrumental factors, parameters that are difficult to decouple. When inverting surface NMR data, practitioners typically account for these phases either by considering only the amplitudes of the complex signals, thus eliminating the influence of the phase, or by iteratively rotating the complex data during inversion so the data phase matches the theoretical phase generated during forward modelling. Each of these approaches assumes the user has an accurate forward model; if not, the data will be incorrectly rotated and forced to fit an erroneous forward model. Additionally, this rotation will artificially reduce the total data misfit, thus masking the effect of the erroneous forward model. We demonstrate the pitfalls of using inverse methods that correct for the phase by inverting synthetic data with three types of deliberate modelling errors that may occur during a surface NMR experiment: errors in the offset between the Larmor and transmit frequencies, errors in the subsurface resistivity model, and errors in the relative positioning of a separated transmitter-receiver coil pair. The inverted water content profiles show that the modelling errors can introduce inversion artifacts. However, the amplitude inversions and complex inversions with iterative phase correction frequently produce χ2 misfit values close to unity, showing that these inverse methods will fail to “raise the alarm” when an incorrect forward model is implemented.

Characterizing the Critical Zone Using Borehole and Surface Nuclear Magnetic Resonance

Core Ideas Unsaturated saprolite, saturated saprolite, and fractured rock have unique NMR responses. Surface NMR signals will be dominated by water in saturated saprolite. The heterogeneous nature of fractured rock results in low‐amplitude surface NMR signals. Understanding critical zone (CZ) structure below the first few meters of Earth's surface is challenging and yet important to understand hydrologic and surface processes that influence life on Earth. Nuclear magnetic resonance (NMR) is an emerging geophysical tool that can quantify the volume of groundwater and pore‐scale properties. Nuclear magnetic resonance has potential to aid in CZ studies, but it can be difficult to collect high‐quality NMR data in weathered and fractured rock. We present data from seven surface NMR soundings and six borehole NMR profiles collected on a weathered and fractured granite in the Laramie Range, Wyoming. First, we show that it is possible to collect high‐quality surface NMR data in a fractured rock. Second, we use the NMR data to delineate the weathering profile into three distinct zones—unsaturated saprolite, saturated saprolite, and fractured rock—and show that the surface NMR signal is dominated by saturated saprolite. Third, we show that lateral heterogeneity significantly reduces the surface NMR signal magnitude, which suggests that the boundary dividing saprolite and fractured rock is laterally heterogeneous. The NMR measurements, when combined with previously collected seismic refraction data, provide a unique opportunity to define the lateral heterogeneity of the boundary dividing saprolite and weathered bedrock in an eroding landscape underlain by crystalline rock.

Gaining insight into the T2*-T2 relationship through complex inversion of surface NMR free-induction decay data

One of the primary shortcomings of the standard surface nuclear magnetic resonance (NMR) measurement, called the free-induction decay (FID), is the uncertainty about the link between the signal’s time dependence and the geometry of the pore space. Ideally, the FID signal’s time dependence, described by the parameter T2*, carries an intimate link to the geometry of the pore space allowing robust estimation of pore-size and permeability. However, T2* can also be strongly influenced by background magnetic field (B0) inhomogeneity, which can mask the link to pore geometry. To improve the utility of surface NMR FID measurements, we investigate whether complex inversion of surface NMR data can be used to provide insight into the link between T2* and T2 (the parameter carrying the link to pore geometry). Synthetic and field measurements are presented to demonstrate that an alternative forward modelling approach that involves direct modelling of relaxation during pulse (RDP) effects can help constrain the relationship between T2* and T2. Complex inversions are performed using forward models that include RDP for varying magnitudes of B0 inhomogeneity (consistent with observed T2* values) and it is observed that satisfactory data fits can only be obtained given reliable T2 estimates. Thus providing insight into the T2*-T2 relationship. We aim to demonstrate that an alternative forward modelling approach may help improve the utility of FID measurements for estimation of pore-scale properties.

An alternative approach to handling co-frequency harmonics in surface nuclear magnetic resonance data

An alternative approach to handling co-frequency harmonics in surface nuclear magnetic resonance data

Frequency cycling to alleviate unknown frequency offsets for adiabatic half-passage pulses in surface nuclear magnetic resonance

Adiabatic half-passage (AHP) pulses show great promise for significantly enhancing the signal-to-noise ratio of the surface nuclear magnetic resonance (NMR) free-induction decay measurement. Performing an AHP requires that the frequency sweep terminates when the transmit frequency is equal to the Larmor frequency, a condition that demands accurate knowledge of the true Larmor frequency. If the frequency sweep is terminated at an incorrect frequency, i.e., with an unknown offset between the transmit and Larmor frequency at the end of the pulse, the net excitation is affected and it can differ from that predicted by modeling that assumes a 0 Hz offset at the end of the sweep. Surface NMR surveys using a traditional single-frequency pulse have previously been shown to display degraded performance in the presence of an uncertain Larmor frequency estimate; the AHP pulse is also likely susceptible to such degraded performance. To ensure that reliable results can be produced by AHP pulses in the presence of an uncertain Larmor frequency estimate, we have developed an approach that adapts the frequency-cycling scheme for use with AHP pulses. We hypothesize that data collected using two similar AHP pulses, each with the exact same frequency sweep but where one sweeps toward the Larmor frequency from higher frequencies and the other from lower frequencies, can be stacked in such a manner that the impact of an unknown frequency offset is significantly reduced. We present synthetic and field results to demonstrate that frequency-cycling AHP pulse surface NMR data can ensure reliable performance even in the presence of an uncertain Larmor frequency estimate.

Removal of Co-Frequency Powerline Harmonics From Multichannel Surface NMR Data

Powerline harmonics are often the primary noise source in surface nuclear magnetic resonance (NMR) measurements. State-of-the-art techniques, such as notch filtering, Wiener filtering, and model-based subtraction, have been demonstrated to greatly mitigate powerline harmonic noise, but these approaches break down when one of the powerline harmonics has a frequency close to or coincident with the Larmor frequency $f_{L}$ , referred to as a co-frequency harmonic. We propose a hybrid scheme where model-based subtraction of powerline harmonics is coupled with data from a synchronous reference coil to specifically subtract the co-frequency harmonic component. In standard model-based subtraction of powerline harmonics, a sinusoidal model of all harmonic components is fit to the data and subtracted. In the new approach, the amplitude and phase of the co-frequency harmonic are determined by a sinusoidal model fit to the synchronous noise-only data recorded in a reference coil. From the reference coil co-frequency model, the co-frequency harmonic in the primary coil is estimated using relationships between the amplitude and phase of the co-frequency harmonic in the two coils established during noise-only segments. By utilizing data from the reference coil to model the co-frequency harmonic, accidental fitting of the surface NMR signal is avoided. We investigate the efficiency of the method using a synthetic surface NMR signal embedded in noise-only data recorded in Denmark. Our results demonstrate that the co-frequency powerline harmonic can be removed efficiently without distorting the surface NMR signal and the new method performs better than standard methods.

Studies of parameter correlations in surface NMR using the Markov chain Monte Carlo method

ABSTRACTSurface nuclear magnetic resonance is a technique capable of providing insight into subsurface aquifer properties. To produce estimates of aquifer properties (such as the spatial distribution of water content and parameters controlling the duration of the nuclear magnetic resonance signal), an inversion is required. Essential to the reliable interpretation of the estimated subsurface models is an understanding of the uncertainty and correlation between the parameters in the estimated models. To quantify parameter uncertainty and correlation in the surface nuclear magnetic resonance inversion, a Markov chain Monte Carlo approach is demonstrated. Markov chain Monte Carlo approaches have been previously employed to invert surface nuclear magnetic resonance data, but the primary focus has been on quantifying parameter uncertainty. The focus of this paper is to further investigate whether the parameters in the estimated models exhibit correlation with one another; equally important to building a reliable interpretation of the subsurface is an understanding of the parameter uncertainty. The utility of the Markov chain Monte Carlo approach is demonstrated through the investigation of three questions. The first question investigates whether the parameters describing the water content and thickness of a layer exhibit a strong correlation. This question stems from applying concepts known to electromagnetic surveys (that the layer thickness and layer resistivity parameters are strongly correlated) to the surface nuclear magnetic resonance inversion. A water content–layer thickness correlation in surface nuclear magnetic resonance would not have large effects for quantifying total water content but would affect the ability to identify layer boundaries. The second question examines whether the parameter controlling the duration of the nuclear magnetic resonance signal exhibits a correlation with the water content and layer thickness parameters. The resolution of surface nuclear magnetic resonance typically does not consider the duration of the signal and focuses primarily on the distribution of current amplitudes that form the suite of transmit pulses. It is common to treat regions with short‐duration signal with greater uncertainty, but it is important to understand whether the signal duration controls resolution for medium to long duration signals as well. The third question explores if the parameter uncertainty produced by the Markov chain Monte Carlo approach is consistent with that produced by an alternative approach based upon the posterior covariance matrix (for the linearised inversion). The ability of the Markov chain Monte Carlo approach to more thoroughly explore the model space provides a means to improve the reliability of surface nuclear magnetic resonance aquifer characterisations by quantifying parameter uncertainty and correlation.

3D block QT inversion of surface nuclear magnetic resonance data

Surface nuclear magnetic resonance (surface NMR) has up to now rarely been applied to 3D subsurface modeling. Inversion approaches currently in use are smooth inversion techniques that are not useful for identifying sharp geologic boundaries. Although they are already computationally expensive, the resulting models are restricted to imaging the subsurface water content distribution and do not deliver relaxation times [Formula: see text] based on the QT inversion scheme established elsewhere. We have developed a method of 3D block QT inversion that uses horizontal smoothness constraints to resolve sharp boundaries in the vertical direction and the distributions of the water content and relaxation time [Formula: see text]. We have improved the computational efficiency, i.e., the ability to perform the inversion using a common desktop computer, by gating the surface NMR data, reducing the model space to monoexponential decays within the subsurface bodies, and inverting based on blocklike structures instead of smooth distributions. We have developed a synthetic study to assess the effectiveness of our block QT inversion technique in imaging 3D water content distributions, and we compared the results with those of a smooth inversion. Furthermore, we evaluated results from a field survey conducted on the frozen surface of an artificial lake. We found that our block QT inversion approach provides results that are superior to those of smooth inversion and consistent with the available construction plan of the lake. We expect that 3D block QT inversion will be a useful approach also in other geologic settings, such as buried valleys, because it overcomes the current limitations of applying 3D surface NMR inversion.

MRSmatlab — A software tool for processing, modeling, and inversion of magnetic resonance sounding data

MRSmatlab is a manufacturer-independent software tool for processing, modeling, and inversion of surface nuclear magnetic resonance data. Intended as an open platform, MRSmatlab has been growing over the past few years by connecting researchers and making the latest research available to the community. We have developed insights into yet unpublished numerical implementations of signal processing and complex inversion. In addition, we have evaluated a field example demonstrating the need to carefully inspect all the steps of handling surface nuclear magnetic resonance data. MRSmatlab can be obtained by contacting the authors.

Model-based subtraction of spikes from surface nuclear magnetic resonance data

Surface nuclear magnetic resonance (surface NMR) has progressed significantly in recent years due to advances in instrumentation. In particular, the introduction of multichannel surface NMR instruments has been effective in improving the signal-to-noise ratio. The current methodology for noise reduction with multichannel instruments is, however, inadequate in complex noise environments, and there is a need for improved signal processing. We have evaluated a study of impulsive noise (spikes) in surface NMR data acquired with a Numis Poly instrument. We have determined how the spectral content can be used to classify spikes as originating from electric fences or sferics. Measurements of spikes from two electric fences were evaluated. The spikes were highly deterministic and can be modeled as impulsive excitations of the band-pass filter in the surface NMR receiver system. We investigated the feasibility of a model-based approach for subtraction of electric fence spikes. Model-based subtraction was shown to be possible, but it is limited by accidental fitting of the NMR signal in its current embodiment. We evaluated an example of a surface NMR data set in which subtraction of powerline harmonic noise and electric fence spike noise removed all coherence in the multichannel data, and the consequences for further noise reduction using multichannel methods were developed.

Frequency cycling for compensation of undesired off-resonance effects in surface nuclear magnetic resonance

To produce reliable estimates of aquifer properties using surface nuclear magnetic resonance (NMR), an accurate forward model is required. The standard surface NMR forward model assumes that excitation occurs through a process called on-resonance excitation, which occurs when the transmit frequency is set to the Larmor frequency. However, this condition is often difficult to satisfy in practice due to the challenge of accurately determining the Larmor frequency within the entire volume of investigation. As such, in situations where an undesired offset is present between the assumed and true Larmor frequency, the accuracy of the forward model is degraded. This is because the undesired offset leads to a condition called off-resonance excitation, which impacts the signal amplitude, phase, and spatial distribution in the subsurface, subsequently reducing the accuracy of surface NMR estimated aquifer properties. Our aim was to reduce the impact of an undesired offset between the assumed and true Larmor frequency to ensure an accurate forward model in the presence of an uncertain Larmor frequency estimate. We have developed a methodology where data are collected using two different transmit frequencies, each an equal magnitude above and below the assumed Larmor frequency. These data are combined, through a method we refer to as frequency cycling, in a manner that allow the component well-described by our estimate of the Larmor frequency to be stacked coherently, whereas the component related to the presence of an undesired offset is combined destructively. In synthetic and field studies, we have determined that frequency cycling is able to mitigate the influence of an undesired offset providing more accurate estimates of aquifer properties. Furthermore, the frequency-cycling method stabilized the complex inversion of surface NMR data, allowing advantages associated with complex inversion to be exploited.