The structural arrangement of peat constituents controls the hydrological and thermal properties of peat. However, the importance of these structural characteristics on other physical processes ...within a peatland has not been fully assessed. Here, we evaluate the importance of peat structure on its ability to entrain biogenic gas bubbles and control ebullition, an important transport mechanism for methane. X‐ray computed tomography (CT) was applied to characterize the structure of a range of peats at varying levels of decomposition. The structural properties of the peat were quantified from a vector representation of the CT images, and the potential of each sample to entrain biogenic gas bubbles was quantified using a rule‐based Monte Carlo model that calculates the tortuosity of bubbles pathways through the peat. Sixty‐six percent of the variability in the trapping potential of the peat results from porosity variations and 34% from structural variations between samples. A metric that represents this structural control was not identified for all peat types because of difficulties adequately representing some peats as a vector network. However, for S. magellanicum peat we were able to establish that the influence of peat structure on the entrainment of gas bubbles is characterized by v, the average vector length of the stems and branches. Peat characterized by longer structural components (larger v) enhances the entrainment of gas bubbles. Our findings demonstrate the need to incorporate some representation of the peat structure in numerical models of biogenic gas transport in peat.
Rocky desertification is a significant threat in the karst regions of southwest China. Studies of soil distribution can contribute to protecting and recovering the fragile karst ecosystem that is ...prevalent in this region. With an underlying aim of being able to assess soil stocks in karstic environments, this study evaluates the use of electrical resistivity tomography (ERT) for delineating the soil–rock interface. Using a synthetic model (that recognizes the three-dimensional nature of the subsurface), experiments are performed to assess the impact of measurement errors and measurement configuration on recovery of the interface. The inverted results show that the accuracy of the delineation of the soil–rock interface decreases with the increase of measurement error and dipole spacing. The results also show the importance of reliable estimation of measurement errors. Field-based applications of ERT at five exposed profiles in southwest China are also reported. For the field data, three-dimensional modelling was necessary to account for the exposed face. The field experiments show that ERT can be effective at delineating the interface between soil and bedrock, but resolution can be limited due to the scale of features or lack of contrast between soil and bedrock. The method shows great promise as a means of assessing, in a non-invasive manner, the soil–bedrock interface, and, perhaps, more significantly, quantifying estimates of total soil stocks, as we seek to quantify the vulnerability or resilience of this important landscape to anthropogenic and natural stresses.
SUMMARY
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 a negative intrinsic phase in frequency-domain IP (FDIP) or a 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 data sets have been discussed, no studies have addressed this topic in detail for FDIP measurements. We describe theory and numerical modelling 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 multifrequency (spectral IP) data sets. 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 data sets.
Time‐lapse geophysical monitoring and inversion are valuable tools in hydrogeology for monitoring changes in the subsurface due to natural and forced (tracer) dynamics. However, the resulting models ...may suffer from insufficient resolution, which leads to underestimated variability and poor mass recovery. Structural joint inversion using cross‐gradient constraints can provide higher‐resolution models compared with individual inversions and we present the first application to time‐lapse data. The results from a synthetic and field vadose zone water tracer injection experiment show that joint 3‐D time‐lapse inversion of crosshole electrical resistance tomography (ERT) and ground penetrating radar (GPR) traveltime data significantly improve the imaged characteristics of the point injected plume, such as lateral spreading and center of mass, as well as the overall consistency between models. The joint inversion method appears to work well for cases when one hydrological state variable (in this case moisture content) controls the time‐lapse response of both geophysical methods.
Understanding ecologically sensitive wetlands often requires non‐invasive methods to characterize their complex structure (e.g., deposit heterogeneity) and hydrogeological parameters (e.g., porosity ...and hydraulic conductivity). Here, electrical conductivities of a riparian wetland were obtained using frequency domain electromagnetic induction (EMI) methods. The wetland was previously characterized by extensive intrusive measurements and 3D electrical resistivity tomography (ERT) surveys and hence offers an ideal opportunity to objectively assess EMI methods. Firstly, approaches to obtain structural information (e.g., elevation and thickness of alluvium) from EMI data and inverted models were assessed. Regularized and sharp inversion algorithms were investigated for ERT calibrated EMI data. Moreover, the importance of EMI errors in inversion was investigated. The hydrological information content was assessed using correlations with piezometric data and petrophysical models. It was found that EMI data were dominated by the thickness of peaty alluvial soils and relatively insensitive to topography and total alluvial thickness. Furthermore, although error weighting in the inversion improved the accuracy of alluvial soil thickness predictions, the multi‐linear regression method performed the best. For instance, an iso‐conductivity method to estimate the alluvial soil thickness in the regularized models had a normalized mean absolute difference (NMAD) of 21.4%, and although this performed better than the sharp inversion algorithm (NMAD = 65.3%), the multi‐linear regression approach (using 100 intrusive observations) achieved a NMAD = 18.0%. In terms of hydrological information content, correlations between EMI results and piezometric data were poor, however robust relationships between petrophysically derived porosity and hydraulic conductivity were observed for the alluvial soils and gravels.
Key Points
Raw ECa values are highly correlated with the thickness of alluvial soil in a riparian wetland
Alluvial soil thickness predictions from multi‐linear regressions were more accurate than from electromagnetic induction (EMI) inversion methods
Robust predictions of hydraulic conductivity across the field site require more extensive intrusive data
There has been recent interest in the use of surface‐deployed geophysical methods to estimate soil moisture profiles. In this study, we applied multicoil, frequency domain, electromagnetic induction ...(EMI) geophysical surveys to determine electrical conductivity (σ) profiles of the root zone of four winter wheat (Triticum aestivum L.) genotypes grown in a randomized block experiment with four replicates. Field measurements of apparent electrical conductivity (σa) were obtained at sites with two different soil textures. We used the cumulative sensitivity model to predict EMI conductivity data from the conductivity profile measured with electrical resistivity tomography (ERT) on a subset of the plots we investigated. During the inversion of the EMI data, conductivities were adjusted on all plots so that they were consistent with the ERT data. Changes in electrical conductivity of field soil, with depth computed from inversion of the EMI data, during the growth period were compared with measured changes in soil water content. Laboratory measurements confirmed a positive correlation between electrical conductivity and soil water content. Between crop emergence and maturity, water extraction by the different wheat genotypes reduced the water content by up to 30% Comparing changes in electrical conductivity between reference profiles determined shortly after crop emergence and electrical conductivity profiles at later dates as the crop matured, we were able to use EMI to remotely monitor moisture extraction by the roots of different wheat genotypes with depth and time.
SUMMARY
This paper explores the applicability of ensemble Kalman inversion (EKI) with level-set parametrization for solving geophysical inverse problems. In particular, we focus on its extension to ...induced polarization (IP) data with uncertainty quantification. IP data may provide rich information on characteristics of geological materials due to its sensitivity to characteristics of the pore–grain interface. In many IP studies, different geological units are juxtaposed and the goal is to delineate these units and obtain estimates of unit properties with uncertainty bounds. Conventional inversion of IP data does not resolve well sharp interfaces and tends to reduce and smooth resistivity variations, while not readily providing uncertainty estimates. Recently, it has been shown for DC resistivity that EKI is an efficient solver for inverse problems which provides uncertainty quantification, and its combination with level set parametrization can delineate arbitrary interfaces well. In this contribution, we demonstrate the extension of EKI to IP data using a sequential approach, where the mean field obtained from DC resistivity inversion is used as input for a separate phase angle inversion. We illustrate our workflow using a series of synthetic and field examples. Variations with uncertainty bounds in both DC resistivity and phase angles are recovered by EKI, which provides useful information for hydrogeological site characterization. Although phase angles are less well-resolved than DC resistivity, partly due to their smaller range and higher percentage data errors, it complements DC resistivity for site characterization. Overall, EKI with level set parametrization provides a practical approach forward for efficient hydrogeophysical imaging under uncertainty.
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