Relating Electrical Response and Petrophysical Properties of Sands Subjected to Stress Changes

Seabrook, Brian C.; Boadu, Fred Kofi

doi:10.4133/jeeg7.2.88

Cited by 11 publications

(4 citation statements)

References 17 publications

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“…We utilized measured values at 4 Hz based on earlier work (Vanhala ; Boadu and Seabrook ; Boadu and Owusu‐Nimo ) where electromagnetic coupling problems are minimal and the electrical responses are maximum with a high signal‐to‐noise ratio. Furthermore, the resistivity or conductivity as well as the phase shift of rocks and soils are nearly constant at frequencies below 100 Hz (Scott ; Olhoeft ; Vanhala and Soininen ; Borner et al ; Slater and Lesmes ; Seabrook and Boadu ; Schon ). The equipment has an automated signal averaging capability that enhances the signal‐to‐noise ratio and reproducibility in resistivity and phase measurements and provides high resolution with a maximum error less that 2%.…”

Section: Methodsmentioning

confidence: 99%

“…The equipment has an automated signal averaging capability that enhances the signal‐to‐noise ratio and reproducibility in resistivity and phase measurements and provides high resolution with a maximum error less that 2%. A description of the set‐up and calibration of the equipment has been given in Boadu and Owusu‐Nimo () and Seabrook and Boadu ().…”

Section: Methodsmentioning

confidence: 99%

“…Archie (1942) first established the relationship between porosity and bulk resistivity. Several studies have improved or added to this knowledge base of how soil properties influence their electrical responses (e.g., Vanhala and Soininen 1995;Börner et al 1996;Slater and Lesmes 2002;Seabrook and Boadu 2002;Schön 2004;Cosenza et al 2006;Revil and Florsch 2010;Weller et al 2010;Boadu 2011). Börner and Schön (1991) found a linear correlation between the imaginary conductivity of saturated sandstone samples and the specific internal surface related to the pore volume.…”

Section: Engineering and Textural Properties Of Soilmentioning

confidence: 99%

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Artificial neural network and statistical models for predicting the basic geotechnical properties of soils from electrical measurements

Boadu

Owusu‐Nimo

Achampong

et al. 2013

Near Surface Geophysics

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The ability to predict the geotechnical properties of subsurface soils using non-invasive geophysical measurements can be undeniably useful to the geotechnical engineer. Using laboratory data, we assess the potential of artificial neural networks to investigate the relations between geotechnical and electrical parameters characterizing a variety of soils. The geotechnical parameters are: fines content, mean grain size, mean pore size and the specific surface area. The electrical parameters obtained from low-frequency electrical measurements (4 Hz) include the resistivity amplitude, phase shift and the loss tangent. Relations that can be used to predict the geotechnical parameters of a soil given its electrical parameters are developed. The predictive capabilities of the neural networks are compared with traditional multivariate regression models. The performances of the neural network and regression models in predicting (a) the geotechnical parameter given the same electrical parameters as inputs and (b) the electrical parameters given the same geotechnical parameters as inputs are compared. In both cases, the neural network outperforms the multivariate regression as the neural network is able to capture and model the non-linear and complex relationships among the variables. The relative importance of the geotechnical parameters on the overall electrical conduction was examined using the neural networks. The results indicate that mean grain size and fines content are the two geotechnical parameters that influence phase-shift values the most; fines content and mean pore size influence the resistivity amplitude the most, whilst fines content and mean grain size influence the loss tangent the most. It was observed that of the four geotechnical parameters, the mean grain size influences the measured resistivity values the least.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Engineering and Textural Properties Of Soilmentioning

confidence: 99%

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Artificial neural network and statistical models for predicting the basic geotechnical properties of soils from electrical measurements

Boadu

Owusu‐Nimo

Achampong

et al. 2013

Near Surface Geophysics

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“…Several prior studies have added to the knowledge base of how soil properties influence geophysical responses ͑e.g., Schön, 1984Schön, , 2004Börner et al, 1993;Seabrook and Boadu, 2002;Cosenza et al, 2006͒. The relationship between porosity and bulk resistivity has been known since the work of Archie ͑1942͒.…”

Section: Introductionmentioning

confidence: 98%

Influence of petrophysical and geotechnical engineering properties on the electrical response of unconsolidated earth materials

Boadu

Owusu‐Nimo

2010

GEOPHYSICS

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We have investigated the influence of petrophysical and geotechnical engineering properties of unconsolidated near-surface geomaterials ͑soils͒ on their electrical responses or measurements. Complex resistivity measurements were performed at a constant effective stress on 32 samples of varying textures and compositions. Petrophysical and engineering properties that inherently affect the mechanical and strength behavior of the samples -that is, fines content, pore-size parameter, specific surface area, and fractal dimension of the grain-size distributionswere obtained from geotechnical analysis. The electrical parameters that describe the electrical response of the samples -that is, resistivity amplitude , phase shift ⌽, percent frequency effect ͑PFE͒, loss tangent tan ␦ , and the normalized phase Nare computed from the electrical measurements. Crossplots of the electrical and engineering parameters provide useful information on how the geotechnical properties of the soil material influence the electrical measurements. In particular, and tan ␦ values are strongly influenced by variations in the petrophysical and engineering properties ͑R 2 Ͼ 0.60͒ in comparison with the other electrical parameters. Soils become more dissipative as their specific surface area increases. The PFE values are less sensitive ͑R 2 Ͻ 0.55͒ to the petrophysical and engineering properties. Analysis of the correlations also indicates that characteristic or transitional values of the fines content ͑18%͒ and pore size ͑0.03 mm͒ exist, beyond which the phase and normalized phase values are insensitive to their respective increases. The characteristic value of 18% fines content is close to values reported in previous studies, which signifies transition in strength behavior of soils and, thus, such relations could be important in noninvasive strength assessment and monitoring of soils. Normalized phase values were used to assess the relative amount of fines in the studied soils.

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Geoelektrik

Berktold¹,

Büttgenbach²,

Greinwald³

et al.

Geophysik

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Die unter dem Begriff Geoelektrik zusammengefaßten elektrischen und elektromagnetischen Verfahren werden eingesetzt zur • Bestimmung der räumlichen Verteilung der elektrischen Leitfähigkeit bzw. des spezifischen elektrischen Widerstandes ,d. h. daß aus jedem Volumenelement genau soviel Strom ausfließt wie eintritt: 0 = J q t + = J J (5.12) Dies ist die physikalisch anschauliche Aussage, daß der aus einem Volumenelement austretende Leitungs-und Quellstrom der zeitlichen Abnahme der elektrischen Ladung des Volumenelements entspricht. In der Praxis können die vorstehenden Gleichungen stark vereinfacht werden: l s = J J (5.13) d. h. die "Quellen" des Leitungsstromes sind die "Senken" des Quellstromes. Damit reduzieren sich (5.5) und (5.9) außerhalb der Quellen auf das Ohmsche Gesetz = J E oder = E J 1 1 n n J J = erreicht. Sie sind bei Eintritt in einen schlechten Leiter (d. h. 2 1 <

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Relating Electrical Response and Petrophysical Properties of Sands Subjected to Stress Changes

Cited by 11 publications

References 17 publications

Artificial neural network and statistical models for predicting the basic geotechnical properties of soils from electrical measurements

Artificial neural network and statistical models for predicting the basic geotechnical properties of soils from electrical measurements

Influence of petrophysical and geotechnical engineering properties on the electrical response of unconsolidated earth materials

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