Estimation of clay content in soil based on resistivity modelling and laboratory measurements

Shevnin, Vladimir; Mousatov, Aleksandr; Ryjov, Albert; Delgado-Rodriquez, Omar

doi:10.1111/j.1365-2478.2007.00599.x

Cited by 90 publications

(49 citation statements)

References 25 publications

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“…The relationship between a soil's electrical properties and moisture content vary according to the soils' composition and, in particular, the proportion and type of clay minerals (Shevnin et al, 2007;Russell and Barker, 2010). Consequently, for quantitative moisture content information to be extracted from ERT monitoring results, resistivity-moisture relationships must be modelled using either in-situ or laboratory measurements (Brunet et al, 2010;Binley et al, 2002).…”

mentioning

confidence: 99%

Measurement and modelling of moisture—electrical resistivity relationship of fine-grained unsaturated soils and electrical anisotropy

Merritt

Chambers

Wilkinson

et al. 2016

Journal of Applied Geophysics

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A methodology for developing resistivity-moisture content relationships of materials associated with a clayey landslide is presented. Key elements of the methodology include sample selection and preparation, laboratory measurement of resistivity with changing moisture content, and the derivation of models describing the relationship between resistivity and moisture content.Laboratory resistivity measurements show that the techniques utilised (samples and square array) have considerable potential as a means of electropetrophysical calibration of engineering soils and weak rock.Experimental electrical resistivity results show a hierarchy of values dependent on sample lithology, with silty clay exhibiting the lowest resistivities, followed by siltstones and sands, which return the highest resistivities. In addition, finer grained samples show a greater degree of anisotropy between measurement orientations than coarser grained samples.However, suitability of results in light of issues such as sample cracking and electrical conduction must be identified and accounted for if the results are to be accurately up-scaled to inverted model resistivity results.The existence of directional anisotropy makes model calibration curve selection more difficult due to variability in the range of measured laboratory resistances.The use of larger measurement array size means that experimental data will be more representative of bulk lithological properties. In addition, use of electrodes with a relatively high surface area (wide diameter) help maintain low contact resistances and repeat measurement error, relative to narrow electrodes.Variation exists between the fit of experimental data and petrophysical models. Model fit is best for claydominated samples but fits less well for sand-dominated samples. Waxman-Smits equation is appropriately applied in this investigation as all samples have considerable clay mineral content, as is shown in nonnegligible CEC results. The incorporation of pressure plate suction measurements on samples, allows suction dissipation to be quantified and evaluated alongside moisture content and electrical resistivity.

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confidence: 99%

Measurement and modelling of moisture—electrical resistivity relationship of fine-grained unsaturated soils and electrical anisotropy

Merritt

Chambers

Wilkinson

et al. 2016

Journal of Applied Geophysics

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“…The total volume of pores in sand and clay is expressed through porosity values on a separated form for sand and clay. A more detailed explanation about characteristics of the theoretical model can be found in Shevnin et al [10].…”

Section: Electrical Profiling (Ep) Methodsmentioning

confidence: 99%

“…Amakor et al [6] stated an empiric hypothesis to relate soil salinity with resistivity, while Friedman [7] made apparent the influence of soil on electrical conductivity and Heil and Schmidhalter [8] did the same for soil texture and humidity. The electrical resistivity tomography (ERT) method has been efficiently applied on the definition of the plume boundaries in hydrocarbon-contaminated soils [9] and on the estimation of the petrophysical parameters of the soil; resistivity values from ERT and groundwater salinity values obtained in the field allow for the determination of clay content [10], porosity, and CEC sections and maps for the study site [11].…”

Section: Introductionmentioning

confidence: 99%

Using Electrical Profiling to Determine Soil Petrophysical Parameters in an Agricultural Field

Contreras¹,

Ortiz²,

Peinado-Guevara³

et al. 2017

Pol. J. Environ. Stud.

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The soils of a 10-ha agricultural field in the municipality of Guasave, Sinaloa, were characterized through electrical measurements and determination of petrophysical parameters. An electrical profiling survey for AB/2 = 2 m and 3 m was carried out in the study area. The apparent resistivity, soil moisture, and pore water salinity values were recalculated into petrophysical values using the Petrowin software, resulting in fine (clay+silt) content, and porosity and hydraulic conductivity maps. Using the Bouyoucos method, soil texture was determined for 30 soil samples collected in points matched with EP measurement points. The results of textural analysis indicated that the study area is quite complex, with the presence of eight textures of soil, and soil moisture and groundwater salinity variable in twice and five times, respectively. The fines content values obtained from Petrowin software and the Bouyoucos method were compared showing a high correlation (R = 0.91), giving reliability to fast and inexpensive techniques such as electrical profiling for the determination of the soil petrophysical parameters in extensive agricultural land.

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“…7). The electrical conductivity of coarse sediments is controlled by the fluid conductivity, while surface conduction and retained salts on clay mineral grains contribute significantly to the conductivity of clay rich matrix sediments (Shevnin et al, 2006;Zarroca et al, 2011). Grain size decrease involves an increase of the surface conduction (Huntley, 1987).…”

Section: Analysis Of Monthly Datamentioning

confidence: 99%

Time-lapse resistivity analysis of Quaternary sediments in the Midlands of Ireland

Pellicer

Zarroca

Gibson

2012

Journal of Applied Geophysics

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a b s t r a c t a r t i c l e i n f oElectrical Resistivity Tomography (ERT) data are influenced by a number of factors associated with the subsurface such as porosity, moisture content and lithology; as well as external factors such as rainfall and temperature. Two time-lapse ERT profiles with 5 m and two with 2 m electrode spacings were acquired over a range of Quaternary sediment types encompassing till, esker gravel, glaciofluvial sand and silt and glaciolacustrine silt/clay. Data were collected on a monthly basis during 2006 at a site located in the Midlands of Ireland in order to evaluate the influence of such conditioning factors on the resistivity of the subsurface. Effective recharge, the depth of investigation, the texture and the internal architecture of the different sediment types and temperature variation are the main factors influencing the resistivity seasonal variation. The shallow subsurface (b 3 m depth) showed a direct relationship between resistivity variation and effective recharge, whereas, an increasing time-lag between effective recharge and resistivity was recorded at increasing depths. As a result of the time-lag, it was possible to determine the rate of movement of the wetting/drying front for the unconsolidated relatively sorted coarse sediments recorded on the site at 7.8 cm/day. Conversely, poorly sorted and fine sediments show little resistivity variation and the velocity of the wetting front could not be estimated. Other factors influencing the electrical response of the subsurface are the electrode spacing used for data collection and the seasonal temperature variation of the subsurface. Two methods for temperature correction of electrical resistivity data were tested in this study -both gave similar results. Resistivity values recorded in the shallow subsurface (b 5 m) show variations of over 15% subsequent to temperature correction. The results illustrate that seasonal temperature changes and their influence on subsurface temperature have to be accounted for in data interpretation and emphasise the potential of this technique for the estimation of the rate of movement of the wetting/drying front in soft sediments.

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Estimation of clay content in soil based on resistivity modelling and laboratory measurements

Cited by 90 publications

References 25 publications

Measurement and modelling of moisture—electrical resistivity relationship of fine-grained unsaturated soils and electrical anisotropy

Measurement and modelling of moisture—electrical resistivity relationship of fine-grained unsaturated soils and electrical anisotropy

Using Electrical Profiling to Determine Soil Petrophysical Parameters in an Agricultural Field

Time-lapse resistivity analysis of Quaternary sediments in the Midlands of Ireland

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