Near Surface Electrical Characterization of Hydraulic Conductivity: From Petrophysical Properties to Aquifer Geometries—A Review

Slater, Lee

doi:10.1007/s10712-007-9022-y

Cited by 287 publications

(214 citation statements)

References 116 publications

(161 reference statements)

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“…In the first step, the geophysical inversions are typically stabilized by using regularization and smoothing constraints that do not reflect real physical conditions (Day-Lewis, 2005;Linde et al, 2006;Singha and Gorelick, 2006;Singha and Moysey, 2006). Therefore one must be cautious when using such geophysical property estimates to infer hydraulic zones or property estimates to be used in the second step of the SHI (Day-Lewis, 2005;Slater, 2007;Hinnell et al, 2010). Furthermore, with the SHI approach used in the following, the geophysical models cannot be easily updated to conform to the hydrologic observations.…”

Section: Informing Hydrologic Models With Geophysicsmentioning

confidence: 99%

“…In JHI, the hydrologic and geophysical models are coupled directly through some of their parameters using assumed relationships among the geophysical and/or hydrologic parameters (Hyndman et al, 1994). For EM data, JHI is typically done using a relationship between hydraulic conductivity and electrical resistivity inspired by Archie's law (Archie, 1942;Revil and Cathles, 1999;Purvance and Andricevic, 2000;Slater, 2007).…”

Section: Informing Hydrologic Models With Geophysicsmentioning

confidence: 99%

“…Petrophysical relationships between hydraulic conductivity and electrical resistivity are difficult to establish, because such translation tends to be site, scale and facies specific (Hyndman and Tronicke, 2005;Slater, 2007). Therefore methods have been developed that do not rely on a petrophysical relationship between a property of the geophysical model and the property of the hydrological model.…”

Section: Informing Hydrologic Models With Geophysicsmentioning

confidence: 99%

“…In this case, the hydraulic and geophysical property fields must be generated to be dependent. 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).…”

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%

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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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Section: Informing Hydrologic Models With Geophysicsmentioning

confidence: 99%

Section: Informing Hydrologic Models With Geophysicsmentioning

confidence: 99%

Section: Informing Hydrologic Models With Geophysicsmentioning

confidence: 99%

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

confidence: 99%

Section: Reference Geophysical System and Data -Stepmentioning

confidence: 99%

See 3 more Smart Citations

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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Electrical‐hydraulic relationships observed for unconsolidated sediments in the presence of a cobble framework

Slater

Barrash

Montrey

et al. 2014

Water Resources Research

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Mechanistic models now exist to predict hydraulic conductivity (K) from the spectral-induced polarization (SIP) response of granular media. We examined the predictions of such a model on unconsolidated coarse fluvial sediments and compared them to those obtained with a modified Kozeny-Carman (KC) model. Samples were retrieved from the Boise Hydrogeophysical Research Site (BHRS), located on a gravel bar adjacent to the Boise River, Idaho. A sample holder (0.102 m diameter and 0.12 m in length) was designed to include the cobble framework in reconstituted samples representing the primary stratigraphic units defined based on porosity variation at this site. SIP (0.001-1000 Hz) and K (from Darcy tests) measurements were recorded for 12 samples, with SIP measurements made as a function of pore fluid conductivity (3-300 mS/m), grain size distribution (GSD), and total porosity. K prediction with the KC model was improved after discounting of the cobble framework and multiplying by the tortuosity resulting from matrix ''capillaries'' around the cobbles, resulting in estimates within a factor of 5 of the measurements. K prediction with a mechanistic SIP model based on Stern layer polarization (SLP model) that requires an estimate of the GSD also required discounting for the cobble framework to obtain estimates within 0.5 orders of magnitude of the measurements. Similarly, the SLP model overpredicts the measured imaginary conductivity (r 00 ) unless the cobble framework is discounted, which then results in estimates of r 00 within a factor of 2 of the measurements. This can be explained by the fact that the cobbles polarize at frequencies well below the minimum measurement frequency (0.001 Hz). The SLP model for K prediction parameterized in terms of the formation factor and imaginary conductivity performed well for the 10 samples with a cobble framework without modification as the imaginary conductivity directly senses the matrix grain size characteristics, whereas the formation factor captures the porosity reduction and tortuosity resulting from the presence of the cobble framework (capillary tortuosity). Our findings suggest that the estimation of contrasts in K in coarse sediments may be achievable through measurements of electrical properties after appropriate consideration of the cobble fraction.

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Joint inversion of aquifer test, MRS, and TEM data

et al. 2014

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This paper presents two methods for joint inversion of aquifer test data, magnetic resonance sounding (MRS) data, and transient electromagnetic data acquired from a multilayer hydrogeological system. The link between the MRS model and the groundwater model is created by tying hydraulic conductivities (k) derived from MRS parameters to those of the groundwater model. Method 1 applies k estimated from MRS directly in the groundwater model, during the inversion. Method 2 on the other hand uses the petrophysical relation as a regularization constraint that only enforces k estimated for the groundwater model to be equal to MRS derived k to the extent that data can be fitted. Both methodologies can jointly calibrate parameters pertaining to the individual models as well as a parameter pertaining to the petrophysical relation. This allows the petrophysical relation to adapt to the local conditions during the inversion. The methods are tested using a synthetic data set as well as a field data set. In combination, the two case studies show that the joint methods can constrain the inversion to achieve estimates of k, decay times, and water contents for a leaky confined aquifer system. We show that the geophysical data can assist in determining otherwise insensitive k, and vice versa. Based on our experiments and results, we mainly advocate the future application of method 2 since this seems to produce the most reliable results, has a faster inversion runtime, and is applicable also for linking k of 3-D groundwater flow models to multiple MRS soundings.

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Near Surface Electrical Characterization of Hydraulic Conductivity: From Petrophysical Properties to Aquifer Geometries—A Review

Cited by 287 publications

References 116 publications

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

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

Electrical‐hydraulic relationships observed for unconsolidated sediments in the presence of a cobble framework

Joint inversion of aquifer test, MRS, and TEM data

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