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In water-stressed regions of the world, managed aquifer recharge (MAR), the process of intentionally recharging depleted aquifers, is an essential tool for combating groundwater depletion. Many groundwater-dependent regions, including the Central Valley in California, USA, are underlain by thick unsaturated zones (ca. 10 to 40 m thick), nested within complex valley-fill deposits that can hinder or facilitate recharge. Within the saturated zone, interconnected deposits of coarse-grained material (sands and gravel) can act as preferential recharge pathways, while fine-textured facies (silts and clays) accommodate the majority of the long-term increase in aquifer storage. However, this relationship is more complex within the vadose zone. Coarse facies can act as capillary barriers that restrict flow, and contrasts in matric potential can draw water from coarse-grained flow paths into fine-grained, low-permeability zones. To determine the impact of unsaturated-zone stratigraphic heterogeneity on MAR effectiveness, we simulate recharge at a Central Valley almond orchard surveyed with a towed transient electromagnetic system. First, we identified three outcomes of interest for MAR sites: infiltration rate at the surface, residence time of water in the root zone and saturated-zone recharge efficiency, which is defined as the increase in saturated-zone storage induced by MAR. Next, we developed a geostatistical approach for parameterizing a 3D variably saturated groundwater flow model using geophysical data. We use the resulting workflow to evaluate the three outcomes of interest and perform Monte Carlo simulations to quantify their uncertainty as a function of model input parameters and spatial uncertainty. Model results show that coarse-grained facies accommodate rapid infiltration rates and that contiguous blocks of fine-grained sediments within the root zone are >20 % likely to remain saturated longer than almond trees can tolerate. Simulations also reveal that capillary-driven flow draws recharge water into unsaturated, fine-grained sediments, limiting saturated-zone recharge efficiency. Two years after inundation, fine-grained facies within the vadose zone retain an average of 37 % of recharge water across all simulations, where it is inaccessible to either plants or pumping wells. Global sensitivity analyses demonstrate that each outcome of interest is most sensitive to parameters that describe the fine facies, implying that future work to reduce MAR uncertainty should focus on characterizing fine-grained sediments.

In water-stressed regions of the world, managed aquifer recharge (MAR), the process of intentionally recharging depleted aquifers, is an essential tool for combating groundwater depletion. Many groundwater-dependant regions, including the Central Valley in California, USA, are underlain by deep vadose zones (ca. 10 to 40 meters thick), nested within complex valley-fill deposits that can hinder or facilitate recharge. Within the saturated zone, interconnected deposits of coarse-grained material (sands and gravel) can act as preferential recharge pathways, while fine-textured facies (silts and clays) accommodate the majority of the long-term increase in aquifer storage. However, this relationship is more complex within the vadose zone. Coarse facies can act as capillary barriers that restrict flow and contrasts in matric potential draw water from coarse-grained flowpaths into fine-grained, low permeability zones. To determine the impact of unsaturated zone stratigraphic heterogeneity on MAR effectiveness, we simulate recharge at a Central Valley almond orchard surveyed with a towed transient electromagnetic system. First, we identified three outcomes of interest for MAR sites: infiltration rate at the surface, residence time of water in the root zone and saturated zone recharge efficiency, which is defined as the increase in saturated zone storage induced by MAR. Next, we developed a geostatistical approach for parameterizing a 3D variably saturated groundwater flow model using geophysical data. We use the resulting workflow to evaluate the three outcomes of interest and perform Monte Carlo simulations to quantify their uncertainty as a function of model input parameters and spatial uncertainty. Model results show that coarse-grained facies accommodate rapid infiltration rates and that contiguous blocks of fine-grained sediments within the root zone are >20 % likely to remain saturated longer than almond trees can tolerate. Simulations also reveal that capillary-driven flow draws recharge water into unsaturated, fine-grained sediments, limiting saturated zone recharge efficiency. Two years after inundation, fine-grained facies within the vadose zone retain an average of 37 % of recharge water across all simulations, where it is inaccessible to either plants or pumping wells. Global sensitivity analyses demonstrate that each outcome of interest is most sensitive to parameters that describe the fine facies, implying that future work to reduce MAR uncertainty should focus on characterizing fine-grained sediments.

A common noise source in surface nuclear magnetic resonance (NMR) is narrowband noise sources that occur at frequencies close to the Larmor frequency, such as power-line harmonics or other sinusoidal signals of unknown origin. These noise sources can lead to significant perturbation on the estimated NMR signal and degrade the accuracy of subsurface characterizations. We demonstrate that the spectral analysis envelope detection scheme, where the envelope is estimated using the discrete Fourier transform for a suite of sliding windows, can be modified to mitigate the effect of narrowband noise sources. Selection of an appropriate length rectangular window is shown to allow one to effectively place a stopband at frequencies very close to the Larmor frequency, where the stopband may be selected to coincide with narrowband noise sources. The result is a significant reduction in the influence of the narrowband noise source on the estimated envelope, without introduction of distorting transients on the signal. Synthetic and field results are presented to illustrate the effectiveness of the proposed approach, as well as identify limits on the required resolution of the narrowband noise source's frequency.

Surface nuclear magnetic resonance (NMR) is emerging as a competitive method for aquifer exploration due to its direct sensitivity to subsurface water, but the method still has several shortcomings, for example, a signal-to-noise ratio that is often poor, long survey times, and bulky equipment. We have developed Apsu, a new surface NMR system designed for near-surface groundwater investigations. It provides several features such as a compact transmitter unit, separated, small receiver coils, wireless connections between multiple receivers, quasi-zero dead time, and robust phase determination. The transmitter unit is powered by a lightweight generator, and it drives a triangular current in an untuned [Formula: see text] transmitter coil. The peak current of the triangular waveform is up to 145 A, with an effective peak current of 105 A at a Larmor frequency of 2 kHz, corresponding to a 30 m depth of investigation. The frequency and amplitude in each half-oscillation of the transmit pulses can be modulated independently, which gives great flexibility in the pulse design. The receiver uses low-noise preamplifiers and multiple receivers linked to a central unit through Wi-Fi. The use of small receiver coils and wireless connections to multichannel receivers greatly improves the layout configuration flexibility and survey efficiency. The performance of the system under field conditions is demonstrated with high-quality data collected near Silkeborg, Denmark, using on-resonance and numerically optimized modulation pulses.

Geophysical methods show promise for detecting the spatial variability of subsurface clay content and its effect on subsurface hydraulic properties. We have developed a laboratory study that examines the influence of clay content and distribution on the relationships between hydraulic conductivity [Formula: see text] and the physical and geophysical properties of the media. Two geophysical methods are investigated: spectral induced polarization (SIP) and nuclear magnetic resonance (NMR). We used synthetic sediment mixtures of sand and up to 10% kaolinite clay by mass; the clay was homogeneously mixed or was present as large (approximately 5 mm) clusters distributed through the sample. The [Formula: see text] varies moderately well (normalized root-mean-square error [[Formula: see text]rms error] = 0.393) with the pore-volume normalized surface area [Formula: see text] a proxy measure of clay content, in the homogeneous samples and poorly ([Formula: see text]rms error = 0.507) when the clustered samples are included in the fit. The SIP parameters indicate moderately good to excellent fits with [Formula: see text] for homogeneous samples ([Formula: see text]rms error = 0.0783–0.139) and moderately good to good fits for clustered samples ([Formula: see text]rms error = 0.140–0.336), and the coefficients describing the polarizability of the samples depend on the clay distribution. NMR parameters vary moderately well with [Formula: see text] in the homogeneous samples ([Formula: see text]rms error = 0.341–0.412) and poorly ([Formula: see text]rms error = 1.08–6.04) in the clustered samples. The SIP parameters vary moderately well with [Formula: see text] ([Formula: see text]rms error = 0.301–0.466); however, the relationship between the SIP parameters and [Formula: see text] is compromised by the nonnegligible polarization of the clay clusters. NMR parameters indicate good to excellent fits with [Formula: see text] ([Formula: see text]rms error = 0.0789–0.116). For SIP and NMR, fitting homogeneous and clustered samples together does not compromise the fit quality. These results suggest that the geophysical measurements are better predictors of [Formula: see text] in heterogeneous porous media than bulk measures of pore geometry such as [Formula: see text].

Envelope detection is an integral step of the surface NMR data processing workflow to estimate these parameters. We present a new method for retrieving NMR signal envelopes using spectral analysis and the subsequent inversion scheme with the new data space. By exploiting the fact that the spectral magnitude at the Larmor frequency is proportional to the product of the initial amplitude and relaxation time, a high-SNR complex envelope can be extracted by Fourier transform for a number of sliding windows. However, SA estimated envelopes are weighted by the NMR relaxation time during processing and the envelopes have units of volt-seconds. We propose to modify the surface NMR forward model such that it predicts data directly in the voltage-time data space. Field data inversions are presented to demonstrate advantages of pairing the SA envelope detection scheme with a forward model that works in the voltage-time data space.

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.

The advection-dispersion equation (ADE) fails to describe commonly observed non-Fickian solute transport in saturated porous media, necessitating the use of other models such as the dual-domain mass-transfer (DDMT) model. DDMT model parameters are commonly calibrated via curve fitting, providing little insight into the relation between effective parameters and physical properties of the medium. There is a clear need for material characterization techniques that can provide insight into the geometry and connectedness of pore spaces related to transport model parameters. Here, we consider proton nuclear magnetic resonance (NMR), direct-current (DC) resistivity, and complex conductivity (CC) measurements for this purpose, and assess these methods using glass beads as a control and two different samples of the zeolite clinoptilolite, a material that demonstrates non-Fickian transport due to intragranular porosity. We estimate DDMT parameters via calibration of a transport model to column-scale solute tracer tests, and compare NMR, DC resistivity, CC results, which reveal that grain size alone does not control transport properties and measured geophysical parameters; rather, volume and arrangement of the pore space play important roles. NMR cannot provide estimates of more-mobile and less-mobile pore volumes in the absence of tracer tests because these estimates depend critically on the selection of a material-dependent and flow-dependent cutoff time. Increased electrical connectedness from DC resistivity measurements are associated with greater mobile pore space determined from transport model calibration. CC was hypothesized to be related to length scales of mass transfer, but the CC response is unrelated to DDMT.

The Rutgers-Newark near-surface geophysics research group is dedicated to advancing the use of our science both at home and abroad. Our chapter has been involved in a remarkable number of international projects across the globe. Here we briefly discuss three ongoing projects in which students from our group traveled abroad to four countries, each on a different continent, to pursue a research project. These projects have given our students unique insight into how geophysical methods can be used to solve problems locally and globally.

We estimate parameters from the Katz and Thompson permeability model using laboratory complex electrical conductivity (CC) and nuclear magnetic resonance (NMR) data to build permeability models parameterized with geophysical measurements. We use the Katz and Thompson model based on the characteristic hydraulic length scale, determined from mercury injection capillary pressure estimates of pore throat size, and the intrinsic formation factor, determined from multi-salinity conductivity measurements, for this purpose. Two new permeability models are tested, one based on CC data and another that incorporates CC and NMR data. From measurements made on forty-five sandstone cores collected from fifteen different formations, we evaluate how well the CC relaxation time and the NMR transverse relaxation times compare to the characteristic hydraulic length scale and how well the formation factor estimated from CC parameters compares to the intrinsic formation factor. We find: (1) the NMR transverse relaxation time models the characteristic hydraulic length scale more accurately than the CC relaxation time (R2 of 0.69 and 0.39 and normalized root mean square errors (NRMSE) of 0.16 and 0.20, respectively); (2) the CC estimated formation factor is well correlated with the intrinsic formation factor (NRMSE=0.23). We demonstrate that that permeability estimates from the joint-NMR-CC model (NRMSE=0.13) compare favorably to estimates from the Katz and Thompson model (NRMSE=0.074). This model advances the capability of the Katz and Thompson model by employing parameters measureable in the field giving it the potential to more accurately estimate permeability using geophysical measurements than are currently possible.

In this laboratory study, permeability models using spectral induced polarization (SIP) and nuclear magnetic resonance (NMR) data are tested on a large set of sandstone cores in order to compare the effectiveness of each method. Models from the literature are first tested to assess and compare their applicability to the sandstone cores. In order to correct for the effect of variations in mineralogy on the models, the samples are split into two distinct groups: cores without large amounts of iron oxides and cores with visible concentrations of iron oxides. Established petrophysical models are used to determine fitting parameters that for each group of cores, suggesting that mineralogy is a controlling factor in SIP and NMR data. This simple categorization according to the mineralogy is enough to reduce scatter in the predicted permeability values. In general, the NMR models outperform the SIP models, although accounting for mineralogy improves the SIP models to a greater extent.