A hydrogeophysical model for relations between electrical and hydraulic properties of aquifers

Mazac, O.; Kelly, William E.; Landa, Ivan

doi:10.1016/0022-1694(85)90178-7

Cited by 176 publications

(42 citation statements)

References 16 publications

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“…The Waxman and Smits model (Waxman and Smits, 1968) combined with the dual-water model by Clavier et al (1984) provides a basis for establishing empirical relationships for shaly sand and sediments containing clays (Revil and Cathles, 1999;Revil et al, 2012). For glacial sedimentary environments, it is reported that clay has low electrical resistivity and also low hydraulic conductivity, and sand has high electrical resistivity and high hydraulic conductivity (Mazáč et al, 1985). For these environments, it is common to use a power law relationship, which is given some theoretical support by Purvance and Andricevic (2000).…”

Section: Hydrological Model Parameterizationmentioning

confidence: 99%

“…In general, petrophysical relationships are difficult to establish because such translation tends to be site-, scale-and facies-specific (Chen et al, 2001;Hyndman and Tronicke, 2005;Slater, 2007) and uncertain (Mazáč et al, 1985;Slater, 2007). The studies by Herckenrath et al (2013) and Christensen et al (2016) used a fixed petrophysical relationship throughout the model domain.…”

Section: Introductionmentioning

confidence: 99%

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Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

Christensen

Ferré

Fiandaca

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. We present a workflow for efficient construction and calibration of large-scale groundwater models that includes the integration of airborne electromagnetic (AEM) data and hydrological data. In the first step, the AEM data are inverted to form a 3-D geophysical model. In the second step, the 3-D geophysical model is translated, using a spatially dependent petrophysical relationship, to form a 3-D hydraulic conductivity distribution. The geophysical models and the hydrological data are used to estimate spatially distributed petrophysical shape factors. The shape factors primarily work as translators between resistivity and hydraulic conductivity, but they can also compensate for structural defects in the geophysical model.The method is demonstrated for a synthetic case study with sharp transitions among various types of deposits. Besides demonstrating the methodology, we demonstrate the importance of using geophysical regularization constraints that conform well to the depositional environment. This is done by inverting the AEM data using either smoothness (smooth) constraints or minimum gradient support (sharp) constraints, where the use of sharp constraints conforms best to the environment. The dependency on AEM data quality is also tested by inverting the geophysical model using data corrupted with four different levels of background noise. Subsequently, the geophysical models are used to construct competing groundwater models for which the shape factors are calibrated. The performance of each groundwater model is tested with respect to four types of prediction that are beyond the calibration base: a pumping well's recharge area and groundwater age, respectively, are predicted by applying the same stress as for the hydrologic model calibration; and head and stream discharge are predicted for a different stress situation.As expected, in this case the predictive capability of a groundwater model is better when it is based on a sharp geophysical model instead of a smoothness constraint. This is true for predictions of recharge area, head change, and stream discharge, while we find no improvement for prediction of groundwater age. Furthermore, we show that the model prediction accuracy improves with AEM data quality for predictions of recharge area, head change, and stream discharge, while there appears to be no accuracy improvement for the prediction of groundwater age.

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Section: Hydrological Model Parameterizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

Christensen

Ferré

Fiandaca

et al. 2017

Hydrol. Earth Syst. Sci.

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“…Various empirical petrophysical relationships between hydraulic conductivity and electrical resistivity have been proposed (Slater, 2007). Both positive and negative relationships have been reported, and there can be significant uncertainty in the relationship (e.g., Mazáč et al, 1985). It is common to use a linear log-log relationship, which is given some theoretical support by Purvance and Andricevic (2000).…”

Section: Step 3 -Generation Of a Reference Geophysical System And Datmentioning

confidence: 99%

“…Using a perfect relationship to generate resistivity from hydraulic conductivity must be characterized as the ideal case because in this case electrical resistivity data can be expected to provide maximal information about hydraulic conductivity. In practice, when possible, estimation of hydraulic conductivity from electrical resistivity is usually based on a site-specific noisy linear log-log relationship (see, e.g., Mazáč et al, 1985;Revil and Cathles, 1999;Purvance and Andricevic, 2000;Slater, 2007), which has been found to be a positive relationship in some cases (Urish, 1981;Frohlich and Kelly, 1985), and a negative relationship in other cases (Worthington, 1975;Heigold et al, 1979;Biella et al, 1983).…”

Section: Reference Geophysical System and Data -Stepmentioning

confidence: 99%

Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

Christensen¹,

Christensen²,

Ferré³

2016

Hydrol. Earth Syst. Sci.

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Abstract. In spite of geophysics being used increasingly, it is often unclear how and when the integration of geophysical data and models can best improve the construction and predictive capability of groundwater models. This paper uses a newly developed HYdrogeophysical TEst-Bench (HYTEB) that is a collection of geological, groundwater and geophysical modeling and inversion software to demonstrate alternative uses of electromagnetic (EM) data for groundwater modeling in a hydrogeological environment consisting of various types of glacial deposits with typical hydraulic conductivities and electrical resistivities covering impermeable bedrock with low resistivity (clay). The synthetic 3-D reference system is designed so that there is a perfect relationship between hydraulic conductivity and electrical resistivity. For this system it is investigated to what extent groundwater model calibration and, often more importantly, model predictions can be improved by including in the calibration process electrical resistivity estimates obtained from TEM data. In all calibration cases, the hydraulic conductivity field is highly parameterized and the estimation is stabilized by (in most cases) geophysics-based regularization.For the studied system and inversion approaches it is found that resistivities estimated by sequential hydrogeophysical inversion (SHI) or joint hydrogeophysical inversion (JHI) should be used with caution as estimators of hydraulic conductivity or as regularization means for subsequent hydrological inversion. The limited groundwater model improvement obtained by using the geophysical data probably mainly arises from the way these data are used here: the alternative inversion approaches propagate geophysical estimation errors into the hydrologic model parameters. It was expected that JHI would compensate for this, but the hydrologic data were apparently insufficient to secure such compensation. With respect to reducing model prediction error, it depends on the type of prediction whether it has value to include geophysics in a joint or sequential hydrogeophysical model calibration. It is found that all calibrated models are good predictors of hydraulic head. When the stress situation is changed from that of the hydrologic calibration data, then all models make biased predictions of head change. All calibrated models turn out to be very poor predictors of the pumping well's recharge area and groundwater age. The reason for this is that distributed recharge is parameterized as depending on estimated hydraulic conductivity of the upper model layer, which tends to be underestimated. Another important insight from our analysis is thus that either recharge should be parameterized and estimated in a different way, or other types of data should be added to better constrain the recharge estimates.

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“…Besides, it is used as an effective tool for ascertaining the subsurface geological framework of an area [3][4][5], and thus being used routinely for aquifer zone delineation and evaluation of the geophysical character of the aquifer. Since a correlation between hydraulic and electrical properties is possible, as both properties are related to the pore space structure and heterogeneity [6][7], the integration of aquifer parameters calculated from boreholes and surface resistivity parameters extracted from surface resistivity measurements is an effective method in estimating aquifer properties. In this study, this approach has been utilized in order to estimate aquifer transmissivity in numerous locations providing effective and inexpensive characterization of the study area aquifer system.…”

Section: Introductionmentioning

confidence: 99%

Geoelectric Sounding for the Determination of Aquifer Transmissivity in Parts of Bayelsa State, South South Nigeria

Kenneth¹,

Ebifuro²

2012

JWARP

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The application of geophysical methods in combination with pumping tests provides a cost-effective and efficient alternative to estimate aquifer parameters. In this study, nineteen Schlumberger vertical electrical soundings (VES) were occupied in parts of Bayelsa State using a maximum current electrode separation ranging beweeen 300 -400 m with the aim of estimating the transmissivity of the alluvial aquifer in areas where no pumping test has been carried out. Four of the soundings were carried out near existing boreholes in which pumping test had been carried out. The VES data obtained was interpreted, and layer parameters such as true resistivities and thickness were determined. The geoelectric parameters were used to generate the Dar Zarrouk parameters. Correlating the Dar Zarrouk parameter (e.g longitudinal unit conductance) with transmissivity derived from pumping test data, a constant was found which translate longitudinal unit conductance to transmissivity in a hydrogeological setting where effective porosity is the primary control on resistivity and hydraulic conductivity.

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A hydrogeophysical model for relations between electrical and hydraulic properties of aquifers

Cited by 176 publications

References 16 publications

Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

Testing alternative uses of electromagnetic data to reduce the prediction error of groundwater models

Geoelectric Sounding for the Determination of Aquifer Transmissivity in Parts of Bayelsa State, South South Nigeria

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