In this work we use the Timepix chip as a multichannel tester for evaluation of properties of different semiconductor sensors. Different sensors bump-bonded to a Timepix readout chip and exposed to ...energetic protons with different incident angles and energies have been investigated. Data from each recorded proton track were processed individually. The extent of the charge sharing effect was evaluated along the proton track at different sensor depths. The level of charge sharing is affected by the time of charge collection which is related to the local intensity of the electric field in the sensor. This method can provide a 3D map of the electric field in the whole sensor volume.
Three‐dimensional electrical resistivity and induced polarization data were collected on an unstable Alpine rock glacier in Val Thorens (Vanoise massif, France). In addition to these field data, we ...also performed induced polarization measurements during freeze and thaw using a soil sample and the poorly mineralized water, both from this site. In the tomograms, the electrical conductivity and the normalized chargeability show very distinctly the presence of the rock ice mixture. The chargeability itself is however quite constant over the entire investigated area with the exception of a small area associated with the presence of carboniferous rocks rich in graphite. The background chargeability is close to the dimensionless number R = 8 × 10−2, which is consistent with the prediction of the dynamic Stern layer model for the polarization of porous media. The theory implies that this dimensionless number R is independent on saturation and temperature in agreement with field observations. A main implication of this observation is that the classical Archie's law cannot be applied to describe the electrical conductivity in this type of environments with poorly mineralized pore water. Surface conductivity dominates the measured conductivity of the materials implying in turn that the electrical conductivity is related to both the water content and cation exchange capacity of the material. We propose new equations for both the electric conductivity and the normalized chargeability in this type of environments.
Plain Language Summary
Induced polarization is a geophysical method looking at imaging the 3D distribution of properties related to the ability of rocks to store reversibly electrical charges under the application of a primary electrical field. This method has never been applied to rock glaciers. A field application and a laboratory experiment demonstrate the usefulness of this method to characterize Alpine rock glaciers and point up some shortcomings in the interpretation of electrical conductivity tomography done by previous authors in this type of environments.
Key Points
Induced polarization of rock glacier is investigated. During freeze and thaw, rocks show two polarization effects
Surface conductivity controls the rock electrical conductivity of rocks during freeze and thaw
An experiment and a field study on an Alpine rock glacier are performed to investigate the electrical conduction in freezing conditions
•A heat tracer experiment is performed to image the hydraulic conductivity.•Hamiltonian Monte Carlo algorithm is implemented to invert the temperature data.•Karhunen-Loève parameterization is used to ...reduce the computational effort.
Estimating spatial distributions of the hydraulic conductivity in heterogeneous aquifers has always been an important and challenging task in hydrology. Generally, the hydraulic conductivity field is determined from hydraulic head or pressure measurements. In the present study, we propose to use temperature data as source of information for characterizing the spatial distributions of the hydraulic conductivity field. In this way, we performed a laboratory sandbox experiment with the aim of imaging the heterogeneities of the hydraulic conductivity field from thermal monitoring. During the laboratory experiment, we injected a hot water pulse, which induces a heat plume motion into the sandbox. The induced plume was followed by a set of thermocouples placed in the sandbox. After the temperature data acquisition, we performed a hydraulic tomography using the stochastic Hybrid Monte Carlo approach, also called the Hamiltonian Monte Carlo (HMC) algorithm to invert the temperature data. This algorithm is based on a combination of the Metropolis Monte Carlo method and the Hamiltonian dynamics approach. The parameterization of the inverse problem was done with the Karhunen-Loève (KL) expansion to reduce the dimensionality of the unknown parameters. Our approach has provided successful reconstruction of the hydraulic conductivity field with low computational effort.
Harmonic pumping tests consist in stimulating an aquifer by the means of hydraulic stimulations at some discrete frequencies. The inverse problem consisting in retrieving the hydraulic properties is ...inherently ill posed and is usually underdetermined when considering the number of well head data available in field conditions. To better constrain this inverse problem, we add self‐potential data recorded at the ground surface to the head data. The self‐potential method is a passive geophysical method. Its signals are generated by the groundwater flow through an electrokinetic coupling. We showed using a 3‐D saturated unconfined synthetic aquifer that the self‐potential method significantly improves the results of the harmonic hydraulic tomography. The hydroelectric forward problem is obtained by solving first the Richards equation, describing the groundwater flow, and then using the result in an electrical Poisson equation describing the self‐potential problem. The joint inversion problem is solved using a reduction model based on the principal component geostatistical approach. In this method, the large prior covariance matrix is truncated and replaced by its low‐rank approximation, allowing thus for notable computational time and storage savings. Three test cases are studied, to assess the validity of our approach. In the first test, we show that when the number of harmonic stimulations is low, combining the harmonic hydraulic and self‐potential data does not improve the inversion results. In the second test where enough harmonic stimulations are performed, a significant improvement of the hydraulic parameters is observed. In the last synthetic test, we show that the electrical conductivity field required to invert the self‐potential data can be determined with enough accuracy using an electrical resistivity tomography survey using the same electrodes configuration as used for the self‐potential investigation.
Key Points:
The self‐potential method improves the results of harmonic hydraulic tomography
Large‐scale tomography of the hydraulic parameters is performed
Large‐scale inversion techniques help reducing significantly the computational effort
Electrical conductivity and polarization properties of 6 samples from Krafla volcano (Iceland) were measured in the frequency range 1 mHz–45 kHz and compared to the data obtained on various basaltic ...rock samples from Hawaii. The results indicate that for altered samples, the surface conductivity, normalized chargeability, and quadrature conductivity of the core samples scales linearly with the cation exchange capacity, taken as a proxy of the alteration facies. The surface conductivity of fresh samples is also controlled by the cation exchange capacity but their normalized chargeability is influenced by the presence of magnetite, especially for unaltered samples. The temperature dependence of quadrature conductivity and normalized chargeability can be modeled with an Arrhenius equation with an activation energy of 16–19 kJ mol−1. The experimental results agree with a model in which the polarization of the metallic and non-metallic grains are both considered in a unified framework. These results are used to interpret two 3D induced polarization surveys performed in the South and East parts of Krafla volcano using two 1.3 km-long cables with 32 electrodes each. The electrical conductivity is in the range 0.3 (clay cap) to 5 × 10−5 S m−1 (unaltered rock) while the normalized chargeability is typically comprised between 10−2 (clay cap) and 10−5 S m−1 (unaltered rock). Induced polarization is used to image porosity and the cation exchange capacity. A long 5.6 km electrical conductivity profile was also performed connecting the two 3D sites and crossing a rhyolitic obsidian ridge called Hrafntinnuhryggur. Hrafntinnuhryggur is characterized by very low conductivity values on the order of 10−4 S m−1. The long conductivity profile shows the position of the inner and outer caldera rims and the feeder dike of Hrafntinnuhryggur. A self-potential survey performed along this long profile shows no shallow active geothermal features in this area, as expected from the low permeability of the clay cap.
•Normalized chargeability is a material property that be imaged using induced polarization.•For volcanic rocks, normalized chargeability is very sensitive to alteration.•Conductivity and normalized chargeability tomography image porosity and alteration
•There is a growing need for the remote detection of leaks from mountain reservoirs.•A new inversion algorithm is applied to the mise-à-la-masse to localize leaks.•The efficiency of this technology ...is demonstrated through sandbox experiments.
Localizing leaks of water and fluids from storage tanks and water reservoirs with geomembranes is an important task for a variety of environmental applications and water resources applications. The minimally intrusive mise-à-la-masse method is used to detect leaks with the current injected inside the reservoir and a return current electrode located remotely. We test a new approach for the inversion of the voltage data using sandbox experiments and numerical modeling. A method similar to the self-potential inversion method is proposed to inverse the voltages recorded around the tank or reservoir. A global objective function with a data misfit term and regularization term is minimized to invert the voltages. In the inversion process, a depth-weighting matrix is used to strengthen the depth resolution of the current source, and the minimum support method is used to avoid oversmoothed results in terms of leak detection. The distributions of electrical current density on the walls of reservoir indicate the position of leaks. The results show that the inversion method with source compaction accurately identifies the location of single leaks. For two separated leaks, there is an obvious bias for the deeper hole and the bias increases with its depth. For three holes, the source compaction method generally identifies the location of the three leaks when their depth ranges are similar. When one of the leaks becomes deeper, localization of the deeper one becomes more difficult. The influence of the size of the leak on the inversion results is also investigated. The inversion algorithm overestimates the depth of small leaks while it slightly underestimates the depth of large leaks. For a leak having the form of a crack, the inversion results using the source compaction method agree with the position of the leak and its shape.
Pumping tests can be used to estimate the hydraulic conductivity field from the inversion of hydraulic head data taken intrusively in a set of piezometers. Nevertheless, the inverse problem is ...strongly underdetermined. We propose to add more information by adding self‐potential data taken at the ground surface during pumping tests. These self‐potential data correspond to perturbations of the electrical field caused directly by the flow of the groundwater. The coupling is electrokinetic in nature that is due to the drag of the excess of electrical charges existing in the pore water. These self‐potential signals can be easily measured in field conditions with a set of the nonpolarizing electrodes installed at the ground surface. We used the adjoint‐state method for the estimation of the hydraulic conductivity field from measurements of both hydraulic heads and self potential during pumping tests. In addition, we use a recently developed petrophysical formulation of the streaming potential problem using an effective charge density of the pore water derived directly from the hydraulic conductivity. The geostatistical inverse framework is applied to five synthetic case studies with different number of wells and electrodes and thickness of the confining unit. To evaluate the benefits of incorporating the self‐potential data in the inverse problem, we compare the cases in which the data are combined or not. Incorporating the self‐potential information improves the estimate of hydraulic conductivity field in the case where the number of piezometers is limited. However, the uncertainty of the characterization of the hydraulic conductivity from the inversion of the self‐potential data is dependent on the quality of the distribution of the electrical conductivity used to solve the Poisson equation. Consequently, the approach discussed in this paper requires a precise estimate of the electrical conductivity distribution of the subsurface and requires therefore new strategies to be developed for the joint inversion of the hydraulic and electrical conductivity distributions.
Key Points
Self‐potential and head data are complementary to assess permeability
A joint inversion is proposed using the adjoint‐state approach
Five synthetic case studies are evaluated
•Image-guided inversion and interpolation applied to hydraulic tomography.•Hydrofacies boundaries provided by georadar or some prior geological expertise.•The inverted hydraulic conductivity ...distributions are close to the true ones.•The prior textural information is enforced in the model covariance matrix.
In steady-state hydraulic tomography, the head data recorded during a series of pumping or/and injection tests can be inverted to determine the transmissivity distributions of an aquifer. This inverse problem is usually under-determined and ill-posed. We propose to use structural information inferred from a guiding image to constrain the inversion process. The guiding image can be drawn from soft data sets such as seismic and ground penetrating radar sections or from geological cross-sections inferred from the wells and some geological expertise. The structural information is extracted from the guiding image through some digital image analysis techniques. Then, it is introduced into the inversion process of the head data as a weighted four direction smoothing matrix used in the regularizer. Such smoothing matrix allows applying the smoothing along the structural features. This helps preserving eventual drops in the hydraulic properties. In addition, we apply a procedure called image-guided interpolation. This technique starts with the tomogram obtained from the image-guided inversion and focus this tomogram. These new approaches are applied on four synthetic toy problems. The hydraulic distributions estimated from the image-guided inversion are closer to the true transmissivity model and have higher resolution than those computed from a classical Gauss–Newton method with uniform isotropic smoothing.
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