1,721,270 research outputs found
Modelli dielettrici per mezzi porosi: analisi di sensitività e sviluppo di nuove relazioni
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A new method for the interpretation of the constant-head well permeameter
No full textA novel semi-analytical solution for the interpretation of the constant-head permeameter test is introduced, which accounts for the correct mixed-type boundary condition at the wellbore, unlike all published analytical solutions. Capillarity can also be accounted for. The simplifications are that flow from the bottom of the borehole is neglected (therefore the solution is applicable to slender boreholes, where the pending depth is at least 10 times the radius) and capillarity can be modeled with a quasi-linear approach. The Green's function approach leads to an integral equation, the solution of which does not show significant ill-posedness. Two sub-cases are presented: the first neglects capillary effects (the all-saturated approximation) and the second (general solution) takes them into account. The all-saturated solution is successfully tested against finite element simulations. The corresponding values of the borehole shape factor C are slightly larger than the ones obtained with approximate analytical solutions from the literature. When capillarity is accounted for, C changes of a factor of 10 when the dimensionless sorptive number A goes from typical values for fine soils to typical values for coarse soils (about two orders of magnitude of variation for A). This range shifts to lower values of A as the dimensionless borehole depth increases. Consequently, the all-saturated solution is a good approximation of the soil behavior for boreholes with large pending depth, and coarse soils. The proposed semi-analytical solution is fast to compute and thus it is possible to use it in an automated optimization technique to fit field data and estimate the field-saturated hydraulic conductivity and the sorptive number; this would not be feasible using a numerical solution. (C) 1998 Elsevier Science B.V. All rights reserved
Multilayer ground-penetrating radar guided waves in shallow soil layers for estimating soil water content
No full textThe knowledge of moisture-content changes in shallow soil layers has important environmental implications, and ground-penetrating radar (GPR) used in surface-to-surface configuration has been used increasingly to quickly image soil moisture content over large areas. The technique is based on measuring direct GPR wave velocity in the ground. However, in the presence of shallow and thin low-velocity soil layers, dispersive guided GPR waves are generated and the direct ground wave is not identifiable as a simple arrival. Under such conditions, the dispersion relation of guided waves can be estimated from field. data and then inverted to obtain the properties of the guiding layers. This approach is applied to a mountain slope with a 1-m soil cover where repeated measurements over time, inverted by conceptualizing the soil as a single guiding layer, lead to estimates of velocity and thickness varying over time. Varying soil thickness clearly is not a plausible physical process. To remove this problem, we develop a multilayer GPR waveguide model. We first assess, using a Monte Carlo sensitivity analysis, the model error arising from using a single-layer forward model to invert data generated by a multilayer waveguide. The single-layer model always underestimates the total soil thickness because the inversion is sensitive mainly to the layer with the lowest velocity (the wettest layer). We then use a multilayer forward model to invert the actual field data. By constraining the total soil thickness, we still manage to invert accurately only for velocity and thickness of the wettest layer, leaving uncertainty about the position of such a layer in the layer sequence. We conclude that these inversion equivalence problems cannot be neglected when guided GPR data are used to estimate time-lapse moisture content in shallow soils
Incorporating auxiliary geophysical data into ground-water estimation
No full textThe incorporation of auxiliary data into ground-water flow parameter estimation is a challenging task which can ultimately result in a better site characterization. In this study a maximum likelihood estimation procedure has been applied to the joint identification of the parameters of the aquifer transmissivity random field, and the parameters of the linear regression between the logarithm of transmissivity and the logarithm of the electrical transverse formation factor (TF), determined from surface geoelectrical methods (Vertical Electrical Sounding or V.E.S.). This approach is basically a co-kriging technique applied to the transmissivity and transverse formation factor random fields, but it avoids the independent estimation of the cross-covariances and the secondary variable covariance. The procedure needs some direct well data for transmissivity and a (usually larger) number of V.E.S. measurements which have to be in part at a distance from the well locations in order to provide useful information. The algorithm determines the characteristics of the local (site dependent) transmissivity-transverse formation factor relationship and utilizes this auxiliary information for a geostatistical transmissivity field estimation. The methodology is tested on a real field scenario: a fractured aquifer impacted by landfill leachate contamination. The use of the formation factor in place of the raw resistivity of the subsoil layers accounts for possible effects of clay and contaminant concentration on pore-water resistivity. The information provided by the V.E.S. can add, to some extent, to the understanding of the aquifer characteristics and vulnerability. However, tbe specificity of each site has to be fully understood for an effective application of the present procedure. It seems unlikely that geoelectric data can differentiate between transmissivity values differing by less than two or three orders of magnitude
Tracking contaminant transport using time-lapse geophysics: A review on applications of electrical methods
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Analysis of time-lapse vertical radar profiles to extract lithological and hydrological information
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