Ecohydrologically important subsurface structures in peatlands revealed by ground-penetrating radar and complex conductivity surveys.

The surface pattern of vegetation influences the composition and humification of peat laid down during the development of a bog, producing a subsurface hydrological structure that is expected to affect both the rate and pattern of water flow. Subsurface peat structures are routinely derived from the inspection of peat cores. However, logistical limits on the number of cores that can be collected means that the horizontal extent of these structures must be inferred. We consider whether subsurface patterns in peat physical properties can be mapped in detail over large areas with ground-penetrating radar (GPR) and complex conductivity by comparing geophysical measurements with peat core data along a 36 m transect through different microhabitats at Caribou Bog, Maine. The geophysical methods show promise. Peat horizons produced radar reflections because of changes in the volumetric moisture content. Although these reflections could not be directly correlated with the peat core data, they were related to the depth-averaged peat properties which varied markedly between the microhabitats. Well-decomposed peat below a hollow was characterized by a discontinuous sequence of chaotic wavy reflections, while distinct layering of the peat below an area of hummocks coincided with a pattern of parallel planar reflections. The complex conductivity survey showed spatial variation in the real and imaginary conductivities which resulted from changes in the pore water conductivity; peat structures may also have influenced the spatial pattern in the complex conductivity. The GPR and complex conductivity surveys enabled the developmental history of the different microhabitats along the studied transect to be inferred

Characterization of reactive transport by 3-D electrical resistivity tomography (ERT) under unsaturated conditions

The leaching of nitrate from intensively used arable soil is of major concern in many countries. In this study, we show how time lapse electrical resistivity tomography (ERT) can be used to characterize spatially heterogeneous processes of ion production, consumption, and transport in soils. A controlled release fertilizer was introduced into an undisturbed soil core in a laboratory lysimeter and subjected to infiltration events. The production of ions resulting from processes associated with nitrification and their transport through the soil core was observed by time lapse ERT and analysis of seepage water samples from a multicompartment sampler. ERT images show development and propagation of a high-conductivity plume from the fertilizer source zone. Molar amounts of nitrate produced in and exported from the soil core could be well reproduced by time lapse ERT using a spatial moment analysis. Furthermore, we observed that several shape measures of local breakthrough-curves (BTCs) of seepage water conductivity and nitrate derived by effluent analyses and BTCs of bulk conductivity derived by ERT are highly correlated, indicating the preservation of spatial differences of the plume breakthrough in the ERT data. Also differences between nitrate breakthrough and a conservative tracer breakthrough can be observed by ERT. However, the estimation of target ion concentrations by ERT is error bound and the smoothing algorithm of the inversion masks spatial conductivity differences. This results in difficulties reproducing spatial differences of ion source functions and variances of travel times. Despite the observed limitations, we conclude that time lapse ERT can be qualitatively and quantitatively informative with respect to processes affecting the fate of nitrate in arable soils

Characterization of reactive transport by 3-D electrical resistivity tomography (ERT) under unsaturated conditions

The leaching of nitrate from intensively used arable soil is of major concern in many countries. In this study, we show how time lapse electrical resistivity tomography (ERT) can be used to characterize spatially heterogeneous processes of ion production, consumption, and transport in soils. A controlled release fertilizer was introduced into an undisturbed soil core in a laboratory lysimeter and subjected to infiltration events. The production of ions resulting from processes associated with nitrification and their transport through the soil core was observed by time lapse ERT and analysis of seepage water samples from a multicompartment sampler. ERT images show development and propagation of a high-conductivity plume from the fertilizer source zone. Molar amounts of nitrate produced in and exported from the soil core could be well reproduced by time lapse ERT using a spatial moment analysis. Furthermore, we observed that several shape measures of local breakthrough-curves (BTCs) of seepage water conductivity and nitrate derived by effluent analyses and BTCs of bulk conductivity derived by ERT are highly correlated, indicating the preservation of spatial differences of the plume breakthrough in the ERT data. Also differences between nitrate breakthrough and a conservative tracer breakthrough can be observed by ERT. However, the estimation of target ion concentrations by ERT is error bound and the smoothing algorithm of the inversion masks spatial conductivity differences. This results in difficulties reproducing spatial differences of ion source functions and variances of travel times. Despite the observed limitations, we conclude that time lapse ERT can be qualitatively and quantitatively informative with respect to processes affecting the fate of nitrate in arable soils

Predicting permeability from the characteristic relaxation time and intrinsic formation factor of complex conductivity spectra

Low-frequency quadrature conductivity spectra of siliclastic materials exhibit typically a characteristic relaxation time, which either corresponds to the peak frequency of the phase or the quadrature conductivity or a typical corner frequency, at which the quadrature conductivity starts to decrease rapidly toward lower frequencies. This characteristic relaxation time can be combined with the (intrinsic) formation factor and a diffusion coefficient to predict the permeability to flow of porous materials at saturation. The intrinsic formation factor can either be determined at several salinities using an electrical conductivity model or at a single salinity using a relationship between the surface and quadrature conductivities. The diffusion coefficient entering into the relationship between the permeability, the characteristic relaxation time, and the formation factor takes only two distinct values for isothermal conditions. For pure silica, the diffusion coefficient of cations, like sodium or potassium, in the Stern layer is equal to the diffusion coefficient of these ions in the bulk pore water, indicating weak sorption of these couterions. For clayey materials and clean sands and sandstones whose surface have been exposed to alumina (possibly iron), the diffusion coefficient of the cations in the Stern layer appears to be 350 times smaller than the diffusion coefficient of the same cations in the pore water. These values are consistent with the values of the ionic mobilities used to determine the amplitude of the low and high-frequency quadrature conductivities and surface conductivity. The database used to test the model comprises a total of 202 samples. Our analysis reveals that permeability prediction with the proposed model is usually within an order of magnitude from the measured value above 0.1 mD. We also discuss the relationship between the different time constants that have been considered in previous works as characteristic relaxation time, including the mean relaxation time obtained from a Debye decomposition of the spectra and the Cole-Cole time constant

A laboratory study to estimate pore geometric parameters of sandstones using complex conductivity and nuclear magnetic resonance for permeability prediction

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

On negative induced polarization in frequency domain measurements

Induced polarization (IP) has been widely used to non-invasively characterize electrical conduction and polarization in the subsurface resulting from an applied electric field. Earth materials exhibit a lossy capacitance defined by an intrinsic negative phase in frequency-domain IP (FDIP) or positive intrinsic chargeability in time-domain IP (TDIP). However, error-free positive apparent phase or negative apparent chargeability (i.e., negative IP effects) can occur in IP measurements over heterogeneous media. While negative IP effects in TDIP datasets have been discussed, no studies have addressed this topic in detail for FDIP measurements. We describe theory and numerical modeling to explain the origin of negative IP effects in FDIP measurements. A positive apparent phase may occur when a relatively high polarizability feature falls into negative sensitivity zones of complex resistivity measurements. The polarity of the apparent phase is determined by the distribution of subsurface intrinsic phase and resistivity, with the resistivity impacting the apparent phase polarity via its control on the sensitivity distribution. A physical explanation for the occurrence of positive apparent phase data is provided by an electric circuit model representing a four-electrode measurement. We also show that the apparent phase polarity will be frequency dependent when resistivity changes significantly with frequency (i.e. in the presence of significant IP effects). Consequently, negative IP effects manifest themselves in the shape of apparent phase spectra recorded with multi-frequency (spectral IP) datasets. Our results imply that positive apparent phase measurements should be anticipated and should be retained during inversion and interpretation of single frequency and spectral IP datasets

Estimation of the permeability of hydrocarbon reservoir samples using induced polarization and nuclear magnetic resonance methods

We have evaluated several published models using induced polarization (IP) and nuclear magnetic resonance (NMR) measurements for the estimation of permeability of hydrocarbon reservoir samples. IP and NMR measurements were made on 30 samples (clean sands and sandstones) from a Persian Gulf hydrocarbon reservoir. We assessed the applicability of a mechanistic IP-permeability model and an empirical IP-permeability model recently proposed. The mechanistic model results in a broader range of permeability estimates than those measured for sand samples, whereas the empirical model tends to overestimate the permeability of the samples that we tested. We also evaluated an NMR permeability prediction model that is based on porosity φ and the mean of the log transverse relaxation time (T2ml). This model provides reasonable permeability estimations for the clean sandstones that we tested but relies on calibrated parameters. We also examined an IP-NMR permeability model, which is based on the peak of the transverse relaxation time distribution, T2p and the formation factor. This model consistently underestimates the permeability of the samples tested. We also evaluated a new model. This model estimates the permeability using the arithmetic mean of log transverse NMR relaxation time (T2ml) and diffusion coefficient of the pore fluid. Using this model, we improved estimates of permeability for sandstones and sand samples. This permeability model may offer a practical solution for geophysically derived estimates of permeability in the field, although testing on a larger database of clean granular materials is needed

Simulation of soil water flow and heat transport in drip irrigated potato field with raised beds and full plastic-film mulch in a semiarid area

Surface drip irrigation with full plastic-film mulch can increase crop yield and save water by regulating soil water and heat conditions for potato (Solanum tuberosum L.) production with raised beds in semiarid area where the rainfall is scarce and evaporation is high. For efficient use of plastic film mulch an understanding of the soil water flow and heat transport is needed. Here we use a model (HYRUS-2D) which is calibrated with field experiments to simulate soil water movement and heat transport. The field experiments were conducted with three treatments, characterized as wetted soil percentages: 35% (P1), 55% (P2), and 75% (P3). Furthermore, the effects of the uncertainty of key soil hydraulic parameters on soil water contents were evaluated using three approaches: (1) soil hydraulic parameters estimated from measured soil textural information (S1); (2) from experimentally measured soil water retention curve (S2); and (3) from inverse modeling (S3). The performance of S2 was the worst in all treatments; the root mean square error (RMSE) was > 0.05 cm3 cm-3. The performance of S3 was the best with RMSE ranged from 0.015 to 0.038 cm3 cm-3 at 10-50 cm soil depth. The simulated soil water in the raised bed decreased quickly after irrigation, maintaining adequate aeration for potato growth, irrespective of the wetted soil percentage. The downward transport of soil water still existed during the second and third days after irrigation in the simulations of the P2 and P3 treatments. The soil temperatures between the P1 and P3 treatments were similar. In conclusion, the HYDRUS-2D simulations could be used to estimate the soil hydraulic and thermal parameters with inverse modeling. The calibrated model can be used in the design and management of surface drip irrigation with raised beds and full plastic-film mulch to provide favorable soil water and heat conditions for potato growth

On the field estimation of moisture content using electrical geophysics‐the impact of petrophysical model uncertainty

The spatiotemporal distribution of pore water in the vadose zone can have a critical control on many processes in the near-surface Earth, such as the onset of landslides, crop yield, groundwater recharge, and runoff generation. Electrical geophysics has been widely used to monitor the moisture content (θ) distribution in the vadose zone at field sites, and often resistivity (ρ) or conductivity (σ) is converted to moisture contents through petrophysical relationships (e.g., Archie's law). Though both the petrophysical relationships (i.e., choices of appropriate model and parameterization) and the derived moisture content are known to be subject to uncertainty, they are commonly treated as exact and error-free. This study examines the impact of uncertain petrophysical relationships on the moisture content estimates derived from electrical geophysics. We show from a collection of data from multiple core samples that significant variability in the θ(ρ) relationship can exist. Using rules of error propagation, we demonstrate the combined effect of inversion and uncertain petrophysical parameterization on moisture content estimates and derive their uncertainty bounds. Through investigation of a water injection experiment, we observe that the petrophysical uncertainty yields a large range of estimated total moisture volume within the water plume. The estimates of changes in water volume, however, generally agree within (large) uncertainty bounds. Our results caution against solely relying on electrical geophysics to estimate moisture content in the field. The uncertainty propagation approach is transferrable to other field studies of moisture content estimation

EMagPy:open-source standalone software for processing, forward modeling and inversion of electromagnetic induction data

Frequency domain electromagnetic induction (EMI) methods have had a long history of qualitative mapping for environmental applications. More recently, the development of multi-coil and multi-frequency instruments is such that the focus has shifted toward inverting data to obtain quantitative models of electrical conductivity. However, whilst collection of EMI data is relatively straightforward, the inverse modeling is more complicated. Furthermore, although several commercial and open-source inversion codes, exist, there is still a need for a user-friendly software that can bring EMI inversion to non-specialist audience. Here the open-source EMagPy software is presented as an intuitive approach to modeling EMI data. It comprises a graphical user (GUI) interface and a Python application programming interface (API). EMagPy implements both cumulative sensitivity and Maxwell-based forward operators and can model data for 1D and quasi-2D/3D cases using either deterministic or probabilistic methods. The EMagPy GUI has a logical ‘tab-based’ layout to lead the user through data importing, data filtering, inversion, and plotting of raw and inverted data. In addition, a dedicated forward modeling tab is presented to generate synthetic data. In this publication necessary considerations, and background, of EMI theory are described before EMagPy’s capabilities are presented through a series of synthetic and field-based case studies. Firstly, the performance of cumulative sensitivity and Maxwell-based forward models, and the influence of measurement noise are assessed for synthetic cases. Then the importance of data calibration for a riparian wetland dataset, the ability to include a priori information for a river-borne survey and the potential for monitoring soil moisture in a time-lapse example are all investigated. It is anticipated that EMagPy offers a user-friendly tool suitable for novice and experienced practitioners alike, and its intuitive nature mean it can provide a useful tool for teaching purposes