Recent developments in the direct-current geoelectrical ... · Recent developments in the direct-current geoelectrical imaging method M.H. Loke a, ... monly solved using the linear

Journal of Applied Geophysics 95 (2013) 135–156

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Journal of Applied Geophysics

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Recent developments in the direct-current geoelectricalimaging method

M.H. Loke a, J.E. Chambers b, D.F. Rucker c,⁎, O. Kuras b, P.B. Wilkinson b

a Geotomo Software, 115 Cangkat Minden Jalan 6, 11700 Gelugor, Penang, Malaysiab British Geological Survey, Natural Environment Research Council, Kingsley Dunham Centre, Keyworth, Nottingham NG12 5GG, UKc hydroGEOPHYSICS, Inc., 2302 N Forbes Blvd., Tucson AZ 85745, USA

⁎ Corresponding author. Tel.: +1 520 343 5434.E-mail addresses: [email protected] (M.H. Loke

(D.F. Rucker).

0926-9851/$ – see front matter © 2013 Natural Environhttp://dx.doi.org/10.1016/j.jappgeo.2013.02.017

a b s t r a c t
a r t i c l e i n f o
Article history:Received 21 September 2012Accepted 19 February 2013Available online 22 March 2013

Keywords:Electrical resistivityReviewDevelopmentsTomographyApplicationsLimitations

There have been major improvements in instrumentation, field survey design and data inversion techniquesfor the geoelectrical method over the past 25 years. Multi-electrode and multi-channel systems have made itpossible to conduct large 2-D, 3-D and even 4-D surveys efficiently to resolve complex geological structuresthat were not possible with traditional 1-D surveys. Continued developments in computer technology, aswell as fast data inversion techniques and software, have made it possible to carry out the interpretationon commonly available microcomputers. Multi-dimensional geoelectrical surveys are nowwidely used in en-vironmental, engineering, hydrological and mining applications. 3-D surveys play an increasingly importantrole in very complex areas where 2-D models suffer from artifacts due to off-line structures. Large areas onland and water can be surveyed rapidly with computerized dynamic towed resistivity acquisition systems.The use of existing metallic wells as long electrodes has improved the detection of targets in areas wherethey are masked by subsurface infrastructure. A number of PC controlled monitoring systems are also avail-able to measure and detect temporal changes in the subsurface. There have been significant advancements intechniques to automatically generate optimized electrodes array configurations that have better resolutionand depth of investigation than traditional arrays. Other areas of active development include the translationof electrical values into geological parameters such as clay and moisture content, new types of sensors, esti-mation of fluid or ground movement from time-lapse images and joint inversion techniques. In this paper, weinvestigate the recent developments in geoelectrical imaging and provide a brief look into the future of wherethe science may be heading.

© 2013 Natural Environment Research Council and Elsevier BV. All rights reserved.

1. Introduction

The resistivity survey method is one of the oldest and most com-monly used geophysical exploration methods (Reynolds, 2011). It iswidely used in environmental and engineering (Chambers et al.,2006; Dahlin, 2001; Rucker et al., 2010), hydrological (Page, 1968;Wilson et al., 2006), archeological (Griffiths and Barker, 1994; Tsokaset al., 2008) and mineral exploration (Bauman, 2005; Legault et al.,2008; White et al., 2001) surveys. It has been used to image structuresfrom the millimeter scale to kilometers (Linderholm et al., 2008; Storzet al., 2000). Besides surveys on the land surface, it has been used acrossboreholes (Chambers et al., 2003; Daily and Owen, 1991) and in aquaticareas (Loke and Lane, 2004; Rucker et al., 2011b).

Since the first commercial use of the resistivity method in the early1920's (Burger et al., 2006) and right up to the late 1980's, it has beenused essentially as a one-dimensional (1-D) mapping method. However,

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even in moderately complex areas, a 1-D approach is not sufficiently ac-curate. Over the last 25 years, there have been revolutionary improve-ments to the resistivity method where two-dimensional (2-D) surveysare now routinely conducted. Three-dimensional (3-D) surveys arewidely used in areas with complex geology, while there have beensignificant interest and developments in four-dimensional (4-D)surveys. This has been made possible by recent developments infield instrumentation, automatic interpretation algorithms, and com-puter software. With the new tools, complex variations of the subsur-face resistivity in both space and time can now be accurately mapped.

In this review, we summarize key developments in the field ofgeoelectrical characterization and monitoring from the past two de-cades. The review is focused more towards the commercial develop-ment of the method, and is complementary to the research orientedreviews presented by Slater (2007) and Revil et al. (2012). It is largelyfocused on the D.C. resistivity method due to space constraints, withreferences to related methods such as induced polarization (I.P.) andspectral I.P. methods where appropriate.

The following section gives a short description of basic principlesof the resistivity method and traditional 1-D resistivity mapping

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136 M.H. Loke et al. / Journal of Applied Geophysics 95 (2013) 135–156

methods. This is followed by brief overviews of recent developmentsin 2-D, 3-D and 4-D surveying methods, and practical applications invarious fields. Finally new developments in a few specific areas thatpoint to future trends are described.

2. Basic principles of the resistivity method

The relationship between the electrical resistivity, current and theelectrical potential is governed by Ohm's law. To calculate the poten-tial in a continuous medium, the form of Ohm's Law, combined withthe conservation of current, as given by Poisson's equation is normal-ly used. The potential due to a point current source located at xs isgiven by

∇·1

ρ x; y; zð Þ∇ϕ x; y; zð Þ� �

¼ −∂jc∂t δ xsð Þ ð1Þ

where ρ is the resistivity, ϕ is the potential and jc is the charge density.The potential at any point on the surface or within the medium can becalculated if the resistivity distribution is known. This is the forwardproblem, and we specifically separate it from the inverse problemdiscussed below. For 1-D models (Fig. 1), the forward problem is com-monly solved using the linear filter method (Ghosh, 1971). For 2-Dand 3-D models, analytical methods are used for simple structuressuch as a cylinder or sphere in a homogeneous medium (Wait, 1982;Ward and Hohmann, 1987). Boundary and analytical element methods(Furman et al., 2002; Spiegel et al., 1980; Xu, 2001) can also be used formore general structures but are usually limited to models where thesubsurface is divided into a relatively small number of regions. Formodeling offield data, thefinite-difference and associatedfinite volumemethods (Dey and Morrison, 1979a,b; Pidlisecky et al., 2007), and thefinite-element (Coggon, 1971; Holcombe and Jiracek, 1984) methodare more commonly used. These methods discretize the subsurfaceinto a large number of cells. By using a sufficiently fine mesh and theproper boundary conditions, an accurate solution for the potentialover complex distributions of resistivity can be obtained. In areaswhere anisotropy is significant (LaBrecque and Casale, 2002), Eq. (1)is modified where the resistivity is a vector that includes directionaldependent values instead of a scalar function.

The purpose of the resistivity method is to calculate the electricalresistivity of the subsurface, which is an unknown quantity. Themeasurements for the resistivity survey are made by passing a cur-rent into the ground through two current electrodes (usually metalstakes), and measuring the difference in the resulting voltage at twopotential electrodes. In its most basic form, the resistivity meter hasa current source and voltage measuring circuitry that are connectedby cables to a minimum of four electrodes. The basic data from a

Fig. 1. Resistivity sounding (A) and best-fit three-layer model interpretation (B) for a san(32 Ω.m); middle layer, river terrace sand and gravel (205 Ω.m); lower layer, Oxford Clay

resistivity survey are the positions of the current and potentialelectrodes, the current (I) injected into the ground and the resultingvoltage difference (ΔV) between the potential electrodes (Fig. 2).The current and voltage measurements are then converted into anapparent resistivity (ρa) value by using the following formula

ρa ¼ kΔVI

; ð2Þ

where k is the geometric factor that depends on the configuration of thecurrent and potential electrodes (Koefoed, 1979). Eq. (2) represents thesimplest form of the inverse problem and assumes that the earth is ho-mogeneous for each combination of current and potential measure-ments. Different arrangements of the current and potential electrodes(or arrays) have been devised over the years. The most commonlyused arrays are shown in Fig. 2, along with their associated geometricfactors. The advantages and disadvantages of the different arrays arediscussed in various papers; such as in Dahlin and Zhou (2004),Saydam and Duckworth (1978), Szalai and Szarka (2008) and Zhou etal. (2002). The suitability of an array depends on many factors; amongwhich are its sensitivity to the target of interest, signal-to-noise ratio,depth of investigation, lateral data coverage and more recently the effi-ciency of using it in a multichannel system. The multiple gradient array(Fig. 2F) was designed for use in multi-channel systems (Dahlin andZhou, 2006).

3. Resistivity acquisition, processing and interpretation

3.1. Traditional 1-D resistivity surveys

From the 1920's to the late 1980's there were essentially twosurveying techniques used, the profiling and sounding methods. In aprofiling survey, the distances between the electrodes were kept fixedand the four electrodes were moved along the survey line. A relatedtechnique is the mise-a-la-masse method where one electrode isembedded into a conductive body (with the second current electrodeat a sufficiently far distance) and the potential electrodes are movedaround it to produce an equipotentials map (Parasnis, 1967). The datainterpretation for profiling and mise-a-la-masse surveys were mainlyqualitative. In the sounding method (Koefoed, 1979) the center pointof the electrodes array remained fixed but the spacing between theelectrodes was increased to obtain information about the deeper sec-tions of the subsurface. Usually the Wenner or Schlumberger arrange-ment was used.

The interpretation model consisted of a series of 1-D horizontallayers (Fig. 1A), and the sounding method has been extensivelyused to investigate the ground for resource management, such as

d and gravel reconnaissance survey in the Thames Valley, UK. Note: upper layer, silt(11 Ω.m).

Fig. 2. Some commonly used electrode arrays and their geometric factors. Note that for the multiple gradient array, the total array length is ‘(s + 2)a’ and the distance between thecenter of the potential dipole pair P1–P2 and the center of the current pair C1–C2 is given by ‘ma’.British Geological Survey (c) NERC 2013

137M.H. Loke et al. / Journal of Applied Geophysics 95 (2013) 135–156

mineral, petroleum, and groundwater resources. Initially quantitativeinterpretation of geoelectric sounding data was conducted by usingpre-computed or ‘standard’ sounding curves. Sounding curves werelater superseded by computer inversion techniques with the adventof the linear filter method (Ghosh, 1971; Koefoed, 1979).

The modern application of resistivity processing includes inversemodeling. A commonly used method for sounding data inversion isthe damped least-squares method (Inman, 1975), based on the follow-ing equation:

JTJþ λI� �

Δq ¼ JTΔg; ð3Þ

where, the discrepancy vector Δg contains the difference between thelogarithms of the measured and the calculated apparent resistivityvalues and Δq is a vector consisting of the deviation of the estimatedmodel parameters from the true model. Here, the model parametersare the logarithms of the resistivity and thickness of the model layers.J is the Jacobian matrix of partial derivatives of apparent resistivitywith respect to the model parameters. λ is a damping or regularizationfactor that stabilizes the ill-condition Jacobian matrix usually encoun-tered for geophysical problems. Starting from an initial model (such asa homogeneous earth model), this method iteratively refines themodel so as to reduce the data misfit to a desired level (usually lessthan 5%). Other methods such as the conjugate gradient method, SVDanalysis and global optimization methods (including neural networksand simulated annealing) have also been used for resistivity data inver-sion (Pellerin and Wannamaker, 2005). Fig. 1 shows an example of theresults from a resistivity sounding survey using the offsetWenner array(Barker, 1981) that has a three-layer inversion model.

The main weakness of the sounding method is the assumptionthat there are no lateral changes in the resistivity. It is use-ful in geological situations where this is approximately true, butgives inaccurate results where there are significant lateral changes.The effect of lateral variations on the sounding data can be re-duced by using the offset Wenner method (Barker, 1981) but formore accurate results the lateral changes must be directly incorpo-rated into the interpretation model. It should be noted that someearly combined sounding and profiling surveys to produce 2-Dpseudosections were carried out for mineral exploration (Ward,1990), usually together with I.P. measurements. Conducting thesurveys and quantitative modeling using manual adjustments of aforward model (Hohmann, 1982) was laborious and thus suchsurveys were comparatively rare.

3.2. Multi-electrode systems and 2-D imaging surveys

While the resistivity meter used in sounding and profiling surveystypically has four electrodes connected via four separate cables,a multi-electrode system has 25 or more electrodes connected tothe resistivity meter via a multi-core cable (Fig. 3). Commercialmulti-electrode systems first appeared in the late 1980's (Griffithset al., 1990) and since then have become a standard tool in many geo-physical organizations. An internal switching circuitry controlled by aprogrammable microcomputer or microprocessor within the resistiv-ity meter automatically selects the appropriate 4 electrodes for eachmeasurement. This enables almost any array configuration to beused. By making measurements with different spacings at differentlocations along the cable, a 2-D profile of the subsurface is obtained.Together with the parallel development of fast and stable automaticdata inversion techniques (deGroot-Hedlin and Constable, 1990;

Fig. 3. Schematic diagram of a multi-electrode system, and a possible sequence of measurements to create a 2-D pseudosection.British Geological Survey (c) NERC 2013


Li and Oldenburg, 1992; Loke and Barker, 1996a) that could beimplemented on commonly available microcomputers, 2-D electricalimaging surveys became widely used in the early 1990's.

A 2-D model that consists of a large number of rectangular cellsis commonly used to interpret the data (Loke and Barker, 1996a).The resistivity of the cells is allowed to vary in the vertical and onehorizontal direction, but the size and position of the cells are fixed.Again, different numerical methods can be used to calculate thepotential values for the 2-D forward model. Inverse methods arethen used to back calculate the resistivity that gave rise to the mea-sured potential measurements. Starting from a simple initial model(usually a homogeneous half-space), an optimization method isused to iteratively change the resistivity of the model cells to mini-mize the difference between the measured and calculated apparentresistivity values. The inversion problem is frequently ill-posedand ill-constrained due to incomplete, inconsistent and noisy data.Smoothness or other constraints are usually incorporated to stabilizethe inversion procedure to avoid numerical artifacts. As an example,the following equation includes a model smoothness constraint tothe least-squares optimization method,

JTJþ λF� �

Δqk ¼ JTΔgk−λFqk−1; ð4Þ

where

F ¼ αxCTxCx þ αzC

TzCz: ð5Þ

Cx and Cz are the roughness filter matrices in the horizontal (x)and vertical (z) directions and αx and αz are the respective relativeweights of the roughness filters. k represents the iteration number.One common form of the roughness filter is the first-order differencematrix (deGroot-Hedlin and Constable, 1990), but the elements ofthe matrices can be modified to introduce other desired character-istics into the inversion model (Farquharson, 2008; Pellerin andWannamaker, 2005). An L1-norm criterion can be used to produce‘blocky’ models for regions that are piecewise constant and separatedby sharp boundaries (Farquharson and Oldenburg, 1998; Loke et al.,

2003). The laterally constrained inversion method (Auken andChristiansen, 2004) that includes sharp boundaries has been success-fully used in areas with a layered subsurface structure. Joint inversionalgorithms using other geophysical or geological data to constrain themodel have also been implemented to help produce models that areconsistent with known information (Bouchedda et al., 2012; Hinnellet al., 2010; Linde et al., 2006a). A number of micro-computer basedsoftware are now available that can automatically carry out the inver-sion of a 2-D survey data set in a very short period of time.

There are many commercial multi-electrode resistivity systemscapable of connecting up to several hundreds of electrodes at once,with electrode spacings practically varying from 1 to 20 m. A recentdevelopment over the past 10 years is multi-channeled systemsthat can greatly reduce the survey time. Only two electrodes can beused as the current electrodes at a single time, but the voltagemeasurements can be made between many different pairs of poten-tial electrodes. Commercial systems with 4 to 10 channels are widelyavailable, and some customized systems have more than 100channels (Rucker et al., in press; Stummer et al., 2002). New dataacquisition techniques, such as using the multiple gradient array(Dahlin and Zhou, 2006), have been designed for the multi-channeled systems.

Fig. 4A shows an apparent resistivity pseudosection from a surveyusing the dipole–dipole array to map shallow Quaternary deposits atthe Eddleston experimental site, Scottish Borders, UK (Dochartaighet al., 2011). The dipole–dipole array was used with the dipole length‘a’ ranging from 3 to 18 m, and the dipole separation factor ‘n’ rangingfrom 1 to 8. The mid-point of the array was used to set the horizontallocation of the plotting point in the pseudosection, while the ‘mediandepth of investigation’ method was used to calculate the verticalposition (Edwards, 1977). The southern (left) half of the line lies ina floodplain where it crosses a small stream, while the northern(right) half crosses over into grasslands above the floodplain. Therelatively flat southern section is characterized by a low resistivitytop layer consisting of present-day alluvial deposits, with a generalincrease in the resistivity towards the northern end located on slightlyelevated grasslands (Fig. 4B). The higher resistivity layer at a depth of5 to 15 m corresponds to a coarser alluvial layer with some gravels.

Fig. 4. Example of a 2-D resistivity survey with (A) apparent resistivity pseudosection and (B) inversion model.


Two-dimensional (and three-dimensional) electrical imaging surveysare now widely used for mineral, engineering, environmental andarcheological surveys. The main limitation of the 2-D surveying methodis the assumption that the geological structures do not change in thedirection perpendicular to the survey line. This is a reasonable assump-tion when the survey line can be laid out perpendicular to the strike ofthe structure. However, when there are significant offline variations, dis-tortions in themodel produced can lead to erroneous interpretation. Nev-ertheless, 2-D imaging surveys are still probably the most widely usedtechnique applied commercially. It can be rapidly carried out at a rela-tively lowcostwith lower equipment requirements compared to 3-D sur-veys (following section) that in many situations makes it the method ofchoice due to economic (and in some cases physical) constraints. Insome situations, correction factors can be calculated for the 3-D effects(Wiwattanachang and Giao, 2011) to greatly reduce the distortions inthe 2-D model.

3.3. 3-D imaging surveys

A 3-D resistivity survey and interpretation model should give themost accurate results as all geological structures are 3-D in nature. Al-though at present it has not reached the same level of usage as 2-D sur-veys, it is increasingly more widely used in complex areas for manyenvironmental and engineering problems (Chambers et al., 2006;Dahlin et al., 2002; Jones et al., 2012). It is probably more widely usedin mineral exploration surveys (Legault et al., 2008; White et al.,2001) where the higher costs involved in securing equipment andlabor are less of a barrier. Many of the early 3-D surveys used thepole–pole array over rather small grids (up to about 20 by 20 elec-trodes) with measurements in different directions (Li and Oldenburg,1992; Park and Van, 1991). The use of other arrays, such as thedipole–dipole and Wenner–Schlumberger, is now becoming morecommon in surveys that involve thousands of electrode positions(Chambers et al., 2006; Johnson et al., 2010). A complete 3-D surveyrequires placing of electrodes in the form of a 2-D grid with measure-ments in different directions. However, a more cost-effective strategyis usually followedwherein 3-D data sets are collated from independentparallel 2-D survey lines with possibly orthogonal cross-lines (Ruckeret al., 2009a). This strategy greatly reduces the cost of a 3-D surveyand also allows for 2-D inversions of each individual line for qualitycontrol. Loke and Dahlin (2010) describe techniques to reduce artifactsin the 3-D inversion model that could be caused in the use of parallel2-D lines. A survey procedure using the offset pole–dipole array

(Fig. 2G) designed byWhite et al. (2001) to cover a large area efficientlyis particularly popular in 3-D I.P. surveys for mineral exploration.

The data interpretation techniques used for 2-D surveys can beextended to 3-D surveys with relatively few modifications (Lokeand Barker, 1996b). Fast computer software that takes minutes tohours to invert a 3-D data set (depending on the size of the dataset) on common multi-core PCs is now available (Loke, 2012;Rucker et al., 2010).

Fig. 5 shows an example of the results from a 3-D survey. Thesurvey was carried out within the valley of the Great Ouse, near thevillage of Willington to the east of Bedford, UK to map sand and graveldeposits (Chambers et al., 2012). The survey covers an area of93 × 93 m using 32 orthogonal survey lines with a spacing 6 mbetween the lines (Fig. 5A). The dipole–dipole array was used with aninline unit electrode spacing of 3 m. The use of an inter-line spacing oftwice the inline spacing is common in 3-D surveys using orthogonal2-D lines (Gharibi and Bentley, 2005). After filtering out data pointswith high reciprocal errors, the final data set used for the inversionhas 10,952 points. The inversion model (Fig. 5B) shows a thin nearsurface layer of about 60 Ω.m that corresponds to clay-rich alluviumoverlying sand and gravel deposits with higher resistivity values ofabout 125 Ω.m. The Oxford Clay bedrock has a distinctly lower resistiv-ity of about 15 Ω.m. The middle sand and gravel layer are incised atplaces by silt and clay-rich alluvial river deposits. The distribution andthickness of the sand and gravel deposits estimated from the resistivitymodel were generally in good agreement with logs from a number ofboreholes.

3.4. 4-D imaging surveys

In 4-D surveys, the change of resistivity in both space and time ismeasured. Measurements are repeated at different times using thesame 2-D survey line or 3-D survey grid. These surveys include themonitoring of dams sites (Sjödahl et al., 2008), areas prone to landslides(Supper et al., 2008),methane gas generation in landfills (Rosqvist et al.,2011),movement ofwater in aquifers (Cassiani et al., 2006; Coscia et al.,2012) and reagent solution flow in enhanced recovery of gold from oreheaps (Rucker et al., in press). A number of techniques have been pro-posed for the inversion of time-lapse data (Mitchell et al., 2011), includ-ing independent inversions (Cassiani et al., 2006), using the ratio of thedata from the initial and later data sets (Daily et al., 1992), using the dif-ference in the apparent resistivity values (LaBrecque and Yang, 2001),simultaneous inversion (Hayley et al., 2011), and incorporating the

Fig. 5. (A)Map showing the layout of the survey lines. Three-dimensional ERT model displayed as (B) a solid volume, (C) a solid volumewith opaque volume defining resistivities above200 Ω.m and (D) vertical sections. The boreholes locations are shown as gray cylinders. The southeastern edge of the incised channel structure is indicated as a dashed white line.British Geological Survey (c) NERC 2013.


temporal data and model directly into the least-squares regularizationmethod (Kim et al., 2009; Loke and Dahlin, 2011). The inversion meth-odology by Loke and Dahlin (2011) uses the following equation:

JTi RdJi þ λiWTRmW þ αMTRtM

� �h iΔri

¼ JTi Rdgi− λiWTRmW þ αiM

TRtM� �

ri−1 ð3Þ

where M is the difference matrix applied across the time models withonly the diagonal and one sub diagonal elements having values of 1and −1, respectively. α is the temporal damping factor that gives theweight forminimizing the temporal changes in the resistivity comparedto the model roughness and data misfit. While time-lapse surveys canbe conducted using standard equipment used in 2-D and 3-D surveys,a number of dedicated automatic monitoring systems with perma-nently installed cables and electrodes have been developed.

As an example of a large 4-D resistivity monitoring scenario,consider the site presented in Fig. 6. Fig. 6A shows the location mapof a gold mine where a survey was conducted to map reagent flowinjected into low grade ore for enhanced recovery after conventionalsurface leaching (Rucker et al., in press). The resistivity was to be usedto help optimize injection parameters, such as pressure, rate, andduration of the injection. There is a total of 150 electrodes arrangedin a radial pattern on the surface around the injection well andalong six boreholes (Fig. 6B). Six nearby injection wells were alsoused as long electrodes. A total of 780 sets of measurements, or snap-shots, were made over an eight day period. Fig. 7 shows the resultsfrom one set of measurements made on the last day after the injectionof the reagent into a well (marked in red on Fig. 7, adapted fromRucker et al., in press). The movement of the reagent is illustratedas 3-D rendered transparent volumes showing regions with 3, 6 and12% decrease in the resistivity. The movement of the reagent was

also tracked in real-time by monitoring the current and voltage atthe subsurface electrodes.

This field example also provides a glimpse of the tremendousprogress made by the geoelectrical method over the past 25 years,from mainly simple 1-D models with flat layers up to the late1980's to present-day 4-D models in an area with significant surfacetopography with complex variations in both space and time.

4. Practical applications

This section gives a brief outline of the use the geoelectrical imag-ing method in various fields where it plays an important economicrole.

4.1. Mineral exploration

Multi-dimensional resistivity surveys, usually in conjunction withI.P. measurements, are widely used in base and precious metals ex-ploration due to the complex geological environments encountered.The electrical surveys are frequently conducted after reconnaissanceaeromagnetic and airborne EM surveys have identified specific areasof interest. The introduction of 2-D and 3-D data computer inversiontechniques has greatly improved data interpretation for both newsurveys and historical data (Collins, 2009; Mutton, 2000; Nabighianand Asten, 2002). While massive sulfide ore deposits show a distinctresistivity and I.P. anomaly, the I.P. data are the more diagnostic toolin the exploration for desseminated ore in a geochemically reducedstate. In precious metals exploration, where conductive mineralsoccur as accessory minerals, the I.P. information gives useful informa-tion on the shape of the orebody while the resistivity gives moreinformation about the general geology of area (Guo et al., 1999;Spitzer and Chouteau, 2003). Three-dimensional resistivity and I.P.

Fig. 6. (A) Location map of the Cripple Creek and Victor gold mine, Central Colorado, showing injection area within the mine's boundary. (B) Map showing injection wells (stars),surface electrodes (dots), and borehole electrode arrays (diamonds).


surveys are now widely used for such surveys, where efficient fieldtechniques have been developed to cover wide areas rapidly and toreduce EM coupling effects (White et al., 2001). Resistivity surveyshave played an important role in the exploration of deeply burieduranium deposits in Canada's Athabasca Basin (Legault et al., 2008).The search for deeper metalliferous deposits has also led to develop-ments in more powerful field instruments (Eaton et al., 2010; Goldie,2007) some of which includes signal processing functions to improvethe measurement of complex resistivity signals (Chen et al., 2009;Matthews and Zonge, 2003).

Other mineral resources where electrical methods have been usedinclude graphite (Ramazi et al., 2009), bauxite (Bi, 2009), nickel(Robineau et al., 2007), chromite (Frasheri, 2009; Mohanty et al.,2011), manganese (Murthy et al., 2009), boron (Bayrak and Leyla,2012), coal (Singh et al., 2004), and iron ore (Butt and Flis, 1997). Re-sistivity surveys are also widely used to map shallow oil sand deposits(Bauman, 2005), while there is ongoing research in use of resistivityand I.P. surveys for deeper conventional hydrocarbon resources(Davydycheva et al., 2006; Veeken et al., 2009). Additionally, resistiv-ity surveys have proved to be a useful tool in the mapping ofbulk construction materials (Beresnev et al., 2002; Chambers et al.,2012; Magnusson et al., 2010).

The recent developments that have made significant impact inmineral exploration include more powerful field equipment (withstronger currents, better signal detection and more measurementchannels), innovative field techniques for surveying large areas rap-idly and practical 3-D inversion software for microcomputers. Theyhave enabled the search for deeper mineral deposits in more geolog-ically complex areas.

4.2. Hydrological

Applying the electrical resistivity method to solve hydrologicalproblems has ranged from the regional scale of water resource explo-ration to the local scale for estimating hydraulic conductivity. Earlywork for hydrologically-based electrical resistivity problems focusedon locating resources for exploitation with one-dimensional verticalelectrical soundings (Patra and Bhattacharya, 1966; Zohdy andJackson, 1969). Soon thereafter, several noticed that hydraulic param-eter estimation was feasible by combining pump tests with resistiv-ity information from the VES to formulate regression models atco-located measurement locations (Sri Niwas and Singhal, 1985).This methodology is still in use today for extremely deep aquifers

(e.g., Tizro et al., 2010) and in many rapidly developing parts of theworld (e.g., Asfahani, 2012; Chandra et al., 2011; Ekwe et al., 2010;Khalil, 2010) where access to multi-channeled resistivity systems islimited. The popularity and success of the joint physical characteriza-tion of aquifers can likely be attributed to similar support volumesover which these measurements are conducted and the analogousflow characteristics of water and electrons within the most transmis-sive portions of rocks and soils.

Site complexity can also be studied effectively with resistivity todefine aquifer geometry, geological structure, and hydrostratigraphicsequences. Depth to bedrock studies is a classical example of usingresistivity to help define geometry. For example, Zhou et al. (2000)examined the case of overlying soil thickness above a limestonekarst system. Griffiths and Barker (1993), Beauvais et al. (2004),and Hirsch et al. (2008) also examined the bedrock topography forboth resistive and conductive basements. Rayner et al. (2007), Géliset al. (2010), and Schütze et al. (2012) explored aquifer architecturethrough a detailed analysis of subsurface structural components.Mapping the hydrostratigraphy, that is using resistivity to defineunits that are important to hydrogeological analyses, has been effec-tively conducted by Ismail et al. (2005), Clifford and Binley (2010)and Mastrocicco et al. (2010). A nationwide mapping of the ground-water resources in Denmark has been carried out using EM and elec-trical methods (Auken et al., 2006) that is probably the first of itskind.

A growing body of research has emerged around investigations forobtaining hydraulic parameters with resistivity, including saturated hy-draulic conductivity for groundwater systems and parameters that de-scribe soil moisture characteristic curves that fit the van Genuchten,Brooks Corey, or other well known functions. For this work, heretoforereferred to as hydrogeophysics, several strategies for integrating waterflowand geophysicalmodelinghave been taken that includes sequentialassessments (e.g., Daily et al., 1992; French and Binley, 2004; Kemnaet al., 2002; Park, 1998; Sandberg et al., 2002), joint modeling (Chenet al., 2006; Linde et al., 2006a,b), and fully coupled inverse modeling(Hinnell et al., 2010; Lehikoinen et al., 2009; Rucker, 2009). Ferréet al. (2009) provide coherent definitions for each type of thesehydrogeophysical analyses. The advantage of these approaches isthat hydrological inverse modeling on a limited number of pointmeasurements is ill-posed due to an inadequate spatial measure-ment resolution or low information content of the measured signal.Electrical resistivity measurements, or any geophysical measure-ment sensitive to hydrogeological variables, have the ability to

Fig. 7. Inversion results from data extracted during the last day of injections at 34 and 27 m. The results are presented as percent change from background conductivity (positiveindicating an increase in conductivity). The three transparent bodies of the 3D rendered plots represent 3, 6, and 12% change from background. The injection zone is marked by agray cylinder. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)


provide information with a spatial resolution that is much higherthan hydrological measurement methods alone (Huisman et al.,2004). Secondarily, the tomographic model produced from resistiv-ity may be constrained to produce features that are consistent withhydrogeological analyses.

4.3. Environmental

Borrowing the description of environmental geophysics fromReynolds (2011), environmental applications for resistivity includethose that aim to investigate near surface bio-physico-chemicalphenomena having significant implications for environmental man-agement. Regarding this definition, many resistivity projects haveexamined the release of contaminated waters to both the soil andgroundwater, where high ionic strength solutions provide a sufficient

contrast with the host environment for mapping the extent of a sub-surface plume. The focus on environmental applications has produceda very large body of work fromwhich to pull and covers a broad spec-trum of test cases. Over the past decade, the newer directions of theresistivity method for mapping the spatial aspects of contaminatedground have been to detect even more subtle resistivity contrastsin highly complex environments, increasing electrode coverage byupscaling their numbers in hard to reach areas, accommodating asmuch a priori information as possible into the inverse modeling algo-rithms, and developing petrophysical relationships that allow thetransformation of bulk resistivity to a variable of state (such ascontaminant concentration).

Classical applications of electrical resistivity to plume mapping canbe traced to solid or liquid waste repositories (e.g., landfills, refineries,petrol stations, holding ponds, waste storage facilities), groundwater


contamination from salt-laden waters originating from industrial,mining, and agricultural processes (e.g., brine pits, road salt or fertilizerrun off, tailing piles, animal feed lots), and coastal salt water intrusionmapping. Examples for leachate emanating from a landfill are plentifulin the published literature, but a few recent studies include those byBernstone et al. (2000), Chambers et al. (2006), and Clément et al.(2010). Agriculturally based contamination case studies have includednitrate infiltration from nonpoint source releases through the overuseof fertilizers (Boadu et al., 2008) and animal waste at feedlots (Sainatoet al., 2012). Hydrocarbon-based spills have also been shown to beinteresting targets, as a resistive body may develop from free product(Atekwana and Atekwana, 2010) or a conductive body from the degra-dation of chlorinated solvents (Sauck, 2000). Some of the more intensetargets have been discovered around nuclear waste disposal sites(Rucker et al., 2009a) and mine tailings (Martín-Crespo et al., 2011;Rucker et al., 2009b), where ratios of resistivity from background condi-tions to contaminated ground can easily reach 10,000:1. A similar appli-cation with large resistivity contrasts is saline water intrusion mappingin coastal areas (de Franco et al., 2009; Wilson et al., 2006). Severalresearchers have used the resistivity method to examine a reduction incontaminant loading from cleanup activities and remediation (Bentleyand Gharibi, 2004; Chambers et al., 2010; Slater and Binley, 2003).

The examples above discuss environmental applications of resistiv-ity from unplanned releases, whereby contaminants are accidentallyreleased or the consequences of releasewere not meant to be as severe.An alternative realm of study is the planned release of an electrolytictracer tomore fully understand physical and chemical processes withinporous media. Early work by Fried (1975), White (1988), and Lile et al.(1997) used resistivity imaging during salt water injections to under-stand groundwater flow directions, velocities, and mechanical dis-persion. Presently, the practice is quite common in open ground(e.g., Cassiani et al., 2006; Monego et al., 2010; Robert et al., 2012;Wilkinson et al., 2010b) and within small sand tanks and columns(Lekmine et al., 2012; Slater et al., 2002).

The large number of test cases available in the literature is a testa-ment to the versatility of resistivity to solve many different types ofenvironmental problems, from the large catchment scale (Robinsonet al., 2008) to characterization of pore-scale transport processes(Singha et al., 2008). Complex industrial sites also have a particularsubset of novel solutions for acquiring data, including a high densityof borehole arrays (Johnson et al., 2010), push technology (Pidliseckyet al., 2006), and the use of wells as long electrodes (Rucker et al.,2010, 2011a, 2012). Finally, to accommodate all spatial and temporalscales necessary to capture the relevant dynamics of these specializedproblems, both high capacity acquisition hardware (e.g., Kuras et al.,2009; Rucker et al., in press) and software (Loke et al., in press) areavailable to increase the resolution of the method and highlight evenmore subtle features.

4.4. Engineering

Resistivity imaging is widely used across an enormous range ofengineering applications, including the investigation and monitoringof made or artificial ground, and structures such as foundations,tunnels, earthworks and landfills, soil stability, as well as anthropo-genic problems associated with mining related activities.

Spatial information provided by resistivity imaging has proved to beeffective in characterizing heterogeneous made ground, in terms ofthickness and internal and external geometry (Guerin et al., 2004). Forfoundations, it has been applied to pre-installation ground assessment(Soupios et al., 2007), investigation of existing foundations (Cardarelliet al., 2007), and the monitoring of foundation stabilization procedures(Santarato et al., 2011). It has been used to predict ground conditionsahead of tunneling operations (Danielsen and Dahlin, 2009) to differen-tiate between poor (weathered) and good (unweathered) quality rock.Earthwork investigations employing resistivity imaging have been

used to characterize (Bedrosian et al., 2012; Kim et al., 2007; Minsleyet al., 2011; Oh, 2012) and monitor (Sjödahl et al., 2008, 2009, 2010)dams, and assess the condition of rail (Chambers et al., 2008; Donohueet al., 2011) and road (Fortier et al., 2011; Jackson et al., 2002) embank-ments. Mine related engineering applications include in-mine imagingduring working (van Schoor, 2005; van Schoor and Binley, 2010), aswell as the detection and characterization of old abandoned mineworkings (Chambers et al., 2007; Kim et al., 2006; Maillol et al., 1999;Wilkinson et al., 2005, 2006a).

Resistivity imaging is now a well-established technique for land-slide studies (Jongmans and Garambois, 2007), as it provides rapidand lightweight means of acquiring spatial information related toground structure, composition, and hydrogeology on unstable slopes.Landslide investigations are dominated by 2-D resistivity imagingwith numerous recent examples of the use of the technique forstructural characterization in hard-rock settings (Bekler et al., 2011;Jomard et al., 2010; Le Roux et al., 2011; Migon et al., 2010; Paneket al., 2010; Socco et al., 2010; Tric et al., 2010; Zerathe and Lebourg,2012) and soft rock settings (Bievre et al., 2012; Chang et al., 2012;de Bari et al., 2011; Erginal et al., 2009; Grandjean et al., 2011;Hibert et al., 2012; Jongmans et al., 2009; Lebourg et al., 2010;Piegari et al., 2009), and hydrogeological investigations (Bievre et al.,2012; Grandjean et al., 2009; Jomard et al., 2010; Lee et al., 2012;Travelletti et al., 2012; Yamakawa et al., 2010). Three-dimensionalresistivity imaging, while less commonly applied, has also been usedto investigate the internal structure and hydrogeological regimesassociated with landslides (Chambers et al., 2011; Di Maio andPiegari, 2011, 2012; Heincke et al., 2010; Lebourg et al., 2005;Udphuay et al., 2011). A class of landslide hazard for which resistivityimaging has proved to be particularly applicable is quick clay, which isprevalent across parts of Scandinavia and North America. Resistivityimaging has been used to locate and map quick clay formations dueto the higher resistivity exhibited by quick (leached) compared tonon-quick (unleached) formations (Donohue et al., 2012; Lundstromet al., 2009; Solberg et al., 2008, 2012).

A number of studies describe the use of resistivity imaging fordetecting open cavities (Deceuster et al., 2006; Nyquist et al., 2007;Zhu et al., 2012), collapsed or suffused sinkholes (Ezersky, 2008;Gutiérrez et al., 2009; Valois et al., 2011), and caves (Schwartz et al.,2008) that may affect the structural integrity of a building or maypose a safety hazard. As a target, these features can be either conductivefor the water- and clay-filled cavities, or resistive for air-filled cavities(Smith, 1986). Electrical resistivity lines placed either on the surfaceor in boreholes have been used to help find and map these featuresquite successfully.

Others have used resistivity to specifically understand geotechni-cal properties of engineering materials. Unfortunately, there is nodirect causative relationship between electrical resistivity and, say,rock strength. Indirectly, however, the resistivity value can be depen-dent upon jointly influencing parameters that comprise certainhydrogeological (e.g., Boadu, 2011; Boadu and Owusu-Nimo, 2010)or geomechanical attributes of rock, such as porosity, void ratio,water content, cementation, or composition. The Archie equation(Archie, 1942) shows that for fully saturated media, an increase inporosity causes the resistivity to decrease exponentially. In cases ofsedimentary rock, the porosity exponent has been related to cemen-tation or tortuosity through the pore networks (Schon, 1996). Basedon these influencing parameters that link porosity to both elasticmodulus (e.g., Kahraman and Alber, 2006) and electrical resistivity,some have constructed simple correlations between a mechanicalparameter and a resistivity for co-located measurements. This wasthe method applied by Braga et al. (1999), Oh and Sun (2008), andSudha et al. (2009) when presenting blow count from standardpenetration tests to an inverted resistivity value, when acquired asone-dimensional vertical electrical soundings or two-dimensionaltransects. Cosenza et al. (2006) used resistivity to create a scatter


plot of co-located data with the cone resistance and found logicalgroupings of data associated with the specific lithology from whichthe data were collected.

4.5. Agriculture and soil science

Resistivity, and in particular its reciprocal, electrical conductivity(EC), have played an important role in the fields of agriculture andsoil science for many years as they are among the most useful andeasily obtained spatial properties of soil that influence crop produc-tivity (Corwin and Lesch, 2003, 2005; Samouelian et al., 2005). Thesensitivity of both parameters to soil moisture content, salinity andclay fraction makes them ideal candidates for mapping and evalu-ating the properties of agricultural land, for characterizing fieldvariability in precision agriculture and for monitoring of hydrologicalprocesses and soil performance in terms of nutrient cycling andstorage. Traditionally, high-resolution lateral mapping of resistivityor soil EC at discrete depths of investigation (Dabas, 2009; Dabaset al., 2012; Gebbers et al., 2009) has been favored over resistivityimaging in the strict interpretation of the term, and a range of mea-surement techniques based on inductively, galvanically and capaci-tively coupled sensors is available (Allred et al., 2006; Dabas andTabbagh, 2003).

Direct-current resistivity imaging (2-D and 3-D) has been success-fully applied to characterize the tillage layer (Basso et al., 2010;Besson et al., 2004; Seger et al., 2009), to detect soil cracking at smallscales (Samouelian et al., 2004), to estimate and monitor soil moisturein the root zone and monitor plant uptake (Celano et al., 2011;Nijland et al., 2010; Schwartz et al., 2008), to assess soil water deficit(Brunet et al., 2010), to monitor water percolation and optimize irriga-tion patterns (Greve et al., 2011; Kelly et al., 2011), to map and quantifyroot biomass (al Hagrey, 2007; Amato et al., 2009; Rossi et al., 2011),to characterize soil contamination and monitor remedial treatment(West et al., 1999), to define management zones on farms, plantationsand vineyards (Morari et al., 2009), to investigate soil weatheringprofiles (Beauvais et al., 2004), and to establish integrated 3D soil-geologymodels (Tye et al., 2011). Resistivity imaging is also used exten-sively in the related field of geoforensics (Pringle et al., 2008; Ruffell andMcKinley, 2005).

4.6. Archeology and cultural heritage

In terms of its historical development, the ERT method is arguablyvery closely associated with archeological investigation (Noel and Xu,1991). For archeological purposes however, the lateral mapping ofresistivity (or more typically earth resistance, i.e. the response of afixed-geometry four-electrode array inserted into the soil) has longbeen dominant amongst electrical methods for characterizing largearcheological sites and discriminating buried man-made structures(Gaffney, 2008). More recently though, detailed 2-D and 3-D resistiv-ity imaging have become increasingly popular amongst archeologistsas the method permits closer scrutiny of archeological targets prior toexcavation, for example by volumetric analysis and visualization.

Successful archeological applications of DC resistivity imaging in-clude the non-destructive characterization of mounds and tumuli(Griffiths and Barker, 1994; Papadopoulos et al., 2010; Wake et al.,2012), the investigation of multilayered human settlements (Bergeand Drahor, 2011a,b), the mapping of buried walls, voids and passage-ways (Leucci et al., 2007; Negri and Leucci, 2006; Orfanos andApostolopoulos, 2011), the imagingof ancient citywalls andpreeminentmonuments (Tsokas et al., 2011; Tsourlos and Tsokas, 2011), the detec-tion of tombs and the definition of their geometry (Elwaseif and Slater,2010; Matias et al., 2006), the detection of archeological structuresburied under exceptionally thick soil (Drahor, 2006), the investigationof previously excavated and then backfilled features (Sambuelli et al.,1999), the improved understanding of geological constraints on

archeological sites (Ercoli et al., 2012; Similox-Tohon et al., 2006), andthe comprehension of historic workflows and manufacturing processes(Leopold et al., 2011). Seafloor archeological applications of marineresistivity imaging have also been reported (Passaro, 2010).

A promising field of application is “rescue archeology”, whichcomprises archeological survey and excavation carried out in areasthreatened by, or revealed by, construction or other land develop-ment. This type of archeological investigation must be carried out atspeed; hence resistivity imaging proves to be a useful tool to supportthese surveys (Batayneh, 2011; Loperte et al., 2011). Cultural heritagepreservation is another related field where the benefits of resistivityimaging have been recognized (Mol and Preston, 2010). Its use hasbeen frequently reported for the structural assessment and resto-ration of historical buildings built over more ancient structures(Capizzi et al., 2012; Dabas et al., 2000; Di Maio et al., 2012; Tsokaset al., 2008).

4.7. Waterborne resistivity

Electrical resistivity investigations applied in marine environ-ments can be traced back to at least the mid-1930s (Schlumbergeret al., 1934) where the method was used to map limestone outcropsin the Caspian Sea and to determine the depth to bedrock in Algiersharbor (Corwin et al., 1985). Since then, applications for waterborneresistivity have spanned a wide array of problems that mostlymimic terrestrial applications, but with a few that are unique to themarine setting. For example, geological mapping, including bedrockgeometry, structure, lithology, and hydrostratigraphy is a rathertypical application of both land and waterborne resistivity methods(e.g. Rinaldi et al., 2006; Rucker et al., 2011b). Submarine groundwa-ter discharge (SGD), on the other hand, is a particular area of study foroff-shore resistivity characterization. SGD describes how freshwatermay move from aquifers to the sea and several large scale projectshave been conducted to observe these interactions (Day-Lewiset al., 2006; Henderson et al., 2010; Swarzenski et al., 2006, 2007).

The acquisition of resistivity in aquatic environments can beconducted at the water surface with floating electrodes (e.g. Hatchet al., 2010; Kelly et al., 2009; Song and Cho, 2009) or submerged atthe floor (Toran et al., 2010), with both strategies typically used inconjunction with continuous resistivity profiling (CRP). CRP involvestowing a multi-cored cable behind a vessel and using one pair of cur-rent transmitting electrodes and multiple pairs of potential measure-ment electrodes. The rate at which data are acquired is usually slowerfor the submerged array (Corwin et al., 1985), but typical acquisitionrates may be 3–5 km/h (see Snyder andWightman, 2002). The choiceof floating or submerged electrodes depends on the depth of thewater column, with very thin or very thick water columns using sub-merged electrodes and the intermediate range opting for floatingelectrodes. Loke and Lane (2004) recommend that floating electrodesbe used when the water column is no greater than 25% of the totaldepth of investigation and Lagabrielle (1983) discusses the sensitivityof both cases. In extremely shallow situations involving streams, itmay be more advantageous to use fixed submerged arrays insteadof CRP to avoid damage to the cable.

Several commercial instruments are available for acquiring CRPdata for marine environments, which primarily need the ability to au-tomatically log voltage data for a given electrode array. For freshwa-ter environments, where the resistivity values of the water columnare relatively high, low powered systems (200–400 W) can be usedquite effectively. In highly conductive seawater, higher powered sys-tems (>1kW) are recommended to overcome the noise. Corwin et al.(1985) state that 10 A of current was sufficient to overcome noise inseawater for a submerged Schlumberger array with AB/2 = 316 m.Rucker et al. (2011b) used a low powered system in the resistive wa-ters of the Panama Canal with a floating dipole array and an electrodespacing of 15 m., while Kelly et al. (2009) used non-uniform spacings


ranging 0.5 to 128 m. in a survey along the Namoi River in Australia.Butler (2009) lists other examples of instrumentation power versus elec-trode geometry and environment. Most modern equipment also has theability to accept GPS data to help georeference each measurement.

Data processing of the resistivity profiles typically involvestwo-dimensional inversion and investigating conductive or resistivebodies along the transect. Constraints, such as the bathymetry andconductivity of the water, can be added to the inversion to helpresolve issues at the water–rock interface. Provided the data aresufficiently georeferenced, multiple individual profiles can be inter-polated at off-line locations to produce quasi 3-D volumes. Recently,true 3-D inversion models of marine resistivity data collected over aseries of parallel profiles have been generated and Mansoor andSlater (2007) demonstrate how repeated measurements over thesame grid can produce high quality time-lapse information of a shal-low wetland. Rucker and Noonan (in press) demonstrate for longsubparallel lines that 3-D resistivity modeling can still be conductedefficiently if the numerical grid generated for the forward modelis distinctly separate from the inverse model grid. A meandering

Fig. 8. A map showing the Panama Canal survey area (A), the model grid used for the Panamand general geology (B), and the inversion model (C). The first two layers in the inversion msponds to moderately soft tuff, intermediate resistivity to moderately hard agglomerate an

trajectory does not lend itself well to inverse models that strictly ad-here to grids generated from each unique electrode position. Fig. 8presents an example from the survey by Rucker and Noonan(in press) in the Panama canal. Fig. 8A shows the location of the sur-vey and Fig. 8B details the acquisition within the canal as well as thegrid used to conduct the 3D model. Cell sizes were a uniform30 m × 30 m with 2 m layering to a depth of 32 m. The water col-umn ranged from 15 to 19 m with a resistivity of 67 Ω.m. Fig. 8Cshows the results of the inverse 3-D modeling as horizontal slices.The upper two subplots represent the water column and the lowersubplots show the distribution of rock properties associated withhard (high resistivity) or soft (low resistivity) material.

5. Special topics

In this section, a brief overview of more recent developments isgiven. All of the techniques have been tested in field surveys, whilemost are commercially available.

a Canal floating electrodes survey with 20 × 20 m cells together with the survey linesodel correspond to the water column. The lowest resistivity material generally corre-

d high resistivity to hard andesite.


5.1. Cross-hole ERT

In order to improve the resolution of resistivity images at depth,electrodes are often placed in boreholes (Perri et al., 2012). Measure-ments are then made with various quadripole combinations ofcurrent and potential electrodes, either in the same hole (Slater etal., 2000), between the hole and the surface (Tsourlos et al., 2011)or between pairs of holes (Zhou and Greenhalgh, 2000). The designof surveys for cross-hole imaging has traditionally not been as widelyresearched as for surface investigations, although recent studies havelargely redressed this imbalance. To achieve acceptable image resolu-tion between a pair of boreholes, the separation of the holes shouldnot be more than about 0.75 times the borehole array length(LaBrecque et al., 1996b). The layout of the boreholes can be regular(LaBrecque et al., 1996b; Wilkinson et al., 2006a) or irregular(Chambers et al., 2007; Tsokas et al., 2011) depending on groundconditions and the presence of buildings or infrastructure. Measure-ments are typically made between pairs of boreholes (“panels”) andeither inverted in 2-D for individual panels (Deceuster et al., 2006)or in 3-D for combinations of panels (Nimmer et al., 2008;Wilkinson et al., 2006a). The types and combinations of quadripolesused to measure the data have been studied in terms of maximizingsignal-to-noise and image resolution (al Hagrey, 2011; Goes andMeekes, 2004; Slater et al., 2000; Zhou and Greenhalgh, 2000).Automated survey design algorithms have also been applied to theborehole ERT problem (al Hagrey, 2012; Wilkinson et al., 2006a).Since it is more difficult to determine accurate positions for boreholeelectrodes than surface electrodes, consideration must also be givento the effects of random and systematic offsets in electrode positionsand deviations of the boreholes from their assumed locations anddirection (Oldenborger et al., 2005; Wilkinson et al., 2008; Yi et al.,2009).

Achieving and maintaining good galvanic contact with the groundare also more challenging in boreholes than with surface arrays. Inthe saturated zone, most forms of completion will permit contactwith the electrode, including native backfill (Tsokas et al., 2011),metal casing (Osiensky et al., 2004), and slotted plastic casing(Wilkinson et al., 2006a). In the vadose zone, backfill with bentoniteor similar conductive material is desirable to maintain electrical con-tact (Wilkinson et al., 2010b). However, most forms of completionchange the electrical properties of the ground in the near vicinity ofthe electrodes, and can therefore have a pronounced effect on themeasurements. The effects of metal cased boreholes (Schenkel andMorrison, 1994; Singer and Strack, 1998), grouted boreholes (Deniset al., 2002), and fluid-filled boreholes (Nimmer et al., 2008;Osiensky et al., 2004) have all been analyzed in detail, allowing theeffects of boreholes on the resulting inverted images to be understoodquantitatively. It is further possible to incorporate a borehole modeldirectly in the inversion to remove these effects in 2.5-D (Sugimoto,1999) and 3-D (Doetsch et al., 2010).

5.2. Mobile systems and capacitive resistivity imaging

Continuous data acquisition systems for waterborne surveys weredescribed in an earlier section of this paper. Similar systems for landsurveys have also been developed. Some systems use cylindricalsteel electrodes based on an in-line array geometry (Sørensen,1996), while others use spiked wheels to achieve continuous galvaniccontact with the soil (Dabas, 2009; Panissod et al., 1998). Capacitivelycoupled systems are used in areas with very resistive surfacematerials (e.g. dry or frozen ground) or paved surfaces. These instru-ments use an oscillating, non-grounded electric dipole to generatecurrent flow in the ground and a second similar dipole to measurethe resulting potential distribution at the ground surface (Kuraset al., 2006). There are two major configurations for this type of in-strument. The first configuration (line antenna type) uses cylindrical

transmitters and receivers that are towed behind an operator(Fig. 9A). It gives measurements with typical depths of investigationof 1 to 20 m that are comparable to the galvanically coupled in-linedipole–dipole array (Møller, 2001). However, line antenna data donot fulfill the point source assumption of DC resistivity theory, andparticular care must therefore be taken when interpreting suchdatasets (Neukirch and Klitzsch, 2010). The second type (electrostaticquadripole) uses flatmetallic conductors in an equatorial dipole–dipoleconfiguration (Panissod et al., 1998; Souffache et al., 2010) and hasmaximum survey depths of up to a few meters (Fig. 9B). 2-D and 3-Dsurveys with dense lateral coverage can be rapidly conducted withthese systems, and it has been shown that DC resistivity methodologyand inversion schemes can be usefully applied to such datasets (Kuraset al., 2007). The term “capacitive resistivity imaging (CRI)” has becomesynonymous with this approach. Mobile arrays have traditionallyemployed a limited number of dipoles and associated 2-D and 3-Dmodels are therefore restricted in terms of their spatial resolution.Moreover, these arrays operate primarily at the meter scale. In a morerecent development, Kuras et al. (2012) describe novel multi-sensorCRI instrumentation, which is applied to the 4-D monitoring ofrock-freezing experiments in the laboratory. This methodology holdspromise for applications of ERT in cryospheric science, such as themon-itoring of rocks and soils under permafrost conditions (De Pascale et al.,2008; Hauck and Kneisel, 2006; Krautblatter et al., 2010).

5.3. Unconventional electrodes

Metal stakes are commonly used as the electrodes in resistivitysurveys. While steel electrodes are widely used, other types of metals(and graphite) have also been used (LaBrecque and Daily, 2008; Luand Macnae, 1998). Aluminum foil covered with soil soaked with saltwater is used as current electrodeswhen a current (1 to 20 A) is needed(Ward, 1990). In areas where it is difficult to insert a stake electrode(such as paved ground), flat-base (or plate) electrodes have been used(Athanasiou et al., 2007; Tsokas et al., 2008). Galvanic contact withthe ground is achieved using an electrically conductive gel or mud atthe base of the electrode. Non-polarizable electrodes (Reynolds, 2011)are widely used as the potential electrodes in I.P. surveys to reduce SPnoise, although there has been recent progress in using conventionalmetal electrodes (Dahlin and Leroux, 2012) which aremore convenientto use with multi-electrode systems. In modeling the potentials, theelectrode is usually assumed to be an ideal point. The effect of the finitesize of the electrode is small if the ratio of the electrode length to theelectrode spacing is less than 0.2 (Rücker and Günther, 2011).

In some areas, the existence of widespread subsurface infrastruc-ture can make the detection of subsurface targets by normal surveyswith electrode on the ground surface difficult. The use of existingmetallic wells as long electrodes can significantly improve the detec-tion of targets in such areas (Rucker et al., 2010, 2011a, 2012). Longelectrodes are wells grounded to the earth, extend to the target ofinterest, are already in existence, and can continue to operate in anormal capacity during imaging. Logistically, the wells are connectedto the resistivity acquisition system through a dedicated wire, whichis connected to the inner most casing that extends the deepest intothe earth. Care must be taken to ensure (1) a secured contact betweenthe wire and well, which usually involves cleaning the steel casingwith a wire brush, and (2) the well is electrically isolated fromother metallic infrastructrure such as pipes that may be used tocarry fluid. The numerical models accommodate the long electrodeby assigning those mesh cells representing the well with very lowvalues. Synthetic studies, pilot scale field examples, and full scalefield examples have demonstrated that long electrodes can reproducethe lateral extents of conductive targets with reasonable fidelity, butwith significant loss of vertical resolution. Vertical resolution can beenhanced by combining long electrodes with a large number ofshort electrodes on the surface (Zhu and Feng, 2011), combining

Fig. 9. Capacitively coupled systems using (A) cylindrical transmitter and receiver (line antenna type) and (B) flat metallic plates.British Geological Survey (c) NERC 2013.


vertical long electrodes with horizontal long electrodes, i.e., pipes(Ramirez et al., 2003), by using inclined wells (Hatanaka et al.,2005), or through optimized arrays (Rucker, 2012).

5.4. Automatic monitoring systems

Increased interest in monitoring instrumentation has accompaniedrapid developments in the application of geoelectrics to process relatedproblems in fields such as hydrogeophysics (e.g. Rubin and Hubburd,2005) and landslide and slope stability studies (e.g. Jongmans andGarambois, 2007). Automation of resistivity data acquisition, using PCor microprocessor controlled monitoring systems for remote deploy-ment with permanently installed electrode arrays, has facilitatedmonitoring at the spatial and temporal resolution required to capture

small-scale transient events associated with these types of applications.Specific applications include: landslides (Niesner, 2010; Supper et al.,2008; Wilkinson et al., 2010a); dams (Sjodahl et al., 2009, 2010); land-fills (Johansson et al., 2011); frozen ground (Hilbich et al., 2011); CO2storage (Kiessling et al., 2010); tracer tests (Coscia et al., 2011;Oldenborger et al., 2007a; Wilkinson et al., 2010b); and leak detection(Calendine et al., 2011; Daily et al., 2004).

Approaches to systemdevelopment have included both the adaptionof commercial resistivity survey instrumentation for monitoring pur-poses (Daily et al., 2004; Hilbich et al., 2011; Johansson et al., 2011;Kiessling et al., 2010; Sjodahl et al., 2009, 2010), and the developmentof purpose-built resistivity monitoring systems (Kuras et al., 2009;LaBrecque et al., 2004; Ogilvy et al., 2009; Rucker et al., in press;Supper et al., 2008; Wilkinson et al., 2010a,b). Monitoring installations


are becoming increasingly sophisticated, and now incorporate telemet-ric control and data transfer, and local power generation through wind,solar, and fuel cell technology (Hilbich et al., 2011; Supper et al., 2008;Wilkinson et al., 2010a). With the automation of data acquisition andthe generation of very large data volumes, systems are also being devel-oped to manage the entire workflow, including scheduling of data col-lection, retrieval and storage, quality assessment and inversion(Chambers et al., in press; LaBrecque et al., 2004; Ogilvy et al., 2009).

5.5. Optimized survey design

Most surveys are carried out with conventional arrays such asthe dipole–dipole, pole–dipole, Wenner, Wenner–Schlumberger andmultiple-gradient arrays (Dahlin and Zhou, 2004; see Fig. 3). In recentyears, there has been significant development of algorithms to auto-matically determine non-conventional electrode configurations thatwill produce better image resolution while using the same numberof data points (Stummer et al., 2004; Wilkinson et al., 2006b). Afield example comparing the results of a dipole–dipole and anoptimized array of the same size is shown in Fig. 10. The geologicalinterfaces are more clearly resolved in the optimized image.

Several different approaches have been proposed to design arraysthat maximize the image resolution including: reconstructing compre-hensive data sets from a linearly independent complete subset (Blomeet al., 2011; Lehmann, 1995; Zhe et al., 2007); maximizing a sum ofJacobian sensitivity matrix elements (Furman et al., 2004, 2007); max-imizing cumulative sensitivity while minimizing mutual current distri-butions (Nenna et al., 2011); maximizing a sum of model resolutionmatrix elements (al Hagrey, 2012; Loke et al., 2010a,b; Stummer et al.,2004; Wilkinson et al., 2006b, 2012a); minimizing an average measureof the point spread function (Loke et al., 2010b); and maximizing thedeterminant of the normal matrix (Coles and Morgan, 2009). Most ofthe optimization algorithms are based on sensitivity and current-flowdistributions for homogeneous resistivity models. Although this as-sumption might be thought to limit the applicability of the optimizedsurveys, it has been demonstrated that incorporating prior estimatesof the resistivity model into the design algorithms (Nenna et al., 2011)does not significantly improve the image resolution (Stummer et al.,2004; Wilkinson et al., 2012b).

Fig. 10. Comparison of dipole–dipole and optimized array images of a landslide. Bothimages show the geological structure of the landslide, but in the optimized imagethe lower sandstone/mudstone interface is better resolved.British Geological Survey (c) NERC 2013.

For optimized arrays to be useful in practice, other design constraintsand goalsmust often be considered in addition tomaximizing the imageresolution. These include incorporating error estimates in the resolutioncalculation; minimizing electrode polarization errors; and makingefficient use of multiple measurement channels for both normal andreciprocal data acquisition. Recent studies have addressed many ofthese issues: Blome et al. (2011) for complete pole–dipole data sets,and Nenna et al. (2011) and Wilkinson et al. (2012a) for optimizedfour-pole data sets. Although the optimization algorithms developedso far have focussed on the 2-D problem, in principle the extension to3-D is straightforward and limited only by computational resources.Preliminary investigations using general purpose GPUs to acceleratethe calculations (Loke et al., 2010b) have shown that 3-D optimizationis feasible for subsets of measurement types (Rucker and Loke, 2012).

6. Limitations of the resistivity method

6.1. Non-uniqueness and resolution

The inverse resistivity problem has a unique solution for 1-D, 2-Dand 3-D resistivity distributions within a boundary (Friedel, 2003),but only under strict conditionswhere the voltage and current distribu-tions are known continuously and precisely over the boundary for acomplete set of current injection patterns. In practice, resistivity inver-sion is ill-posed and non-unique due to only being able to use a finitenumber of electrodes covering part of the surface. This implies thatmore than one resistivity model will produce responses consistentwith the observed data to the limits of the data accuracy (Hoffmannand Dietrich, 2004). Regularization is used, often in the form of asmoothing matrix (Loke et al., 2003), to enforce uniqueness withoutsacrificing too much resolution. However regions of the invertedimage where the model resolution is lower (see Optimized surveydesign section) will depend more strongly on the type of constraintused (Oldenborger et al., 2007b; Oldenburg and Li, 1999). The inverseimages will therefore only accurately reflect the true subsurface re-sistivity if the regularization constraints are realistic. Typically theimage resolution diminishes exponentially with the distance fromthe electrodes. If the resolution needs to be maintained with depthfor surface surveys, the surface measurements can be supplementedby surface-to-hole or cross-hole measurements (see Cross-hole ERTsection).

6.2. Data quality

Measured data, and the resulting resistivity images (LaBrecqueet al., 1996a), are subject to error from a variety of sources includingthat introduced by the measurement device, poor electrode contact(usually identified through high contact resistances) or electrodepolarization, and other indeterminate external effects (Dahlin, 2000;Merriam, 2005; Wilkinson et al., 2012a). These issues are addressedthrough the appropriate selection and conditioning of electrodesto reduce contact resistance (also see Unconventional electrodessection), by using appropriate filters (including reciprocal error anal-ysis) prior to inversion (Ferahtia et al., 2009; Zhou and Dahlin, 2003),and through employing measurement sequences that reduce theinfluence of electrode polarization. In practice, polarization can bereduced by ensuring adequate time between using an electrode topass current and measure potential (Wilkinson et al., 2012a).

6.3. Electrode position

Electrode position is usually assumed to be known and fixed for thepurposes of geoelectrical survey and monitoring. However, for difficultground conditions such as steep or heavily vegetated areas, it can bedifficult to accurately position electrodes. Moreover monitoring unsta-ble ground from landsliding, electrode positions can shift resulting in


systematic data error which cannot be reduced through reciprocal errorfiltering (Oldenborger et al., 2005; Wilkinson et al., 2008, 2010a; Zhouand Dahlin, 2003). Approaches to reduce the impact of these effectsinclude the selection of measurement array geometries that are lesssensitive to positional errors (Wilkinson et al., 2008), and in the caseof moving electrodes, to estimate electrode position using a positioninversion routine (Wilkinson et al., 2010a).

6.4. Survey design

For very long 2-D survey lines, and for 3-D imaging grids, it can beimpractical to undertake measurements in a single deployment dueto the long cable lengths required. Consequently, very long 2D linesare often surveyed using overlapping sections (e.g. Donohue et al.,2012), and 3-D surface surveys are often undertaken using a networkof lines, where a single line is incrementally migrated across the sur-face to build up a measurement set comprising data from multiplelines (e.g. Bentley and Gharibi, 2004; Dahlin et al., 2002; Ruckeret al., 2009a). Where a single line orientation is used, linear featuresparallel to the line direction can be poorly resolved (Chamberset al., 2002), and banding or herring-bone effects can be present inthe model (Loke and Dahlin, 2010). Mitigation measures include:roll-along (or multiple line) data acquisition methodologies (Dahlinet al., 2002); orthogonal line directions (Chambers et al., 2002;Gharibi and Bentley, 2005); line separations of no more than two elec-trode spacings (Chambers et al., 2002; Gharibi and Bentley, 2005); andappropriate inversion settings (e.g. horizontal diagonal roughnessfilters) (Farquharson, 2008; Loke and Dahlin, 2010).

6.5. Smoothing

Smoothness constrained inversion, which dominates the field ofgeoelectrical imaging, typically produces smoothly varying resistivitydistributions, and so the precise location of geological or engineering in-terfaces can sometimes be difficult to determine in the absence of apriori subsurface information. In addition to robust (L1-norm) inversion(Loke et al., 2003), a range of other interface detection approaches havebeen applied. These include image analysis using gradient-based edgedetectors (Chambers et al., 2012; Elwaseif and Slater, 2010; Hsu et al.,2010), and alternative inversion approaches, such as laterallyconstrained inversion (Wisen et al., 2005), inclusion of sharp bound-aries (Chen et al., 2012; Smith et al., 1999) or joint inversionwith other geophysical data (Bouchedda et al., 2012). In addition,high-contrast heterogeneities in the subsurface that are small com-pared to the model cell-size cannot be accurately modeled, and so canhinder convergence between measured data and the resistivity modelduring the inversion process. However reducing the model cell sizeincreases the computer memory and time required for the data inver-sion, as well as possibly requiring a higher damping constraint to stabi-lize the inversion model (Loke and Dahlin, 2010; Loke and Lane, 2004).From numerical tests with 2-D models, Sasaki (1992) and Loke (2012)showed that using a model cell size of half the unit electrode spacingseems to provide the optimum balance.

6.6. Three-dimensional structures

Off-line three-dimensional structures (including topography) can-not be accurately modeled by 2-D inversion, and so can distortresistivity models (Bentley and Gharibi, 2004; Chambers et al.,2002; Nimmer et al., 2008). These effects can be reduced by ensuringlines are oriented perpendicular to the strike of elongated structuresand by using a 3-D survey approach in complex settings. Although itshould be noted that the edges of 3-D models will also be influencedby 3-D structures outside of the survey area.

6.7. Calibration and interpretation

ERT provides only indirect information of subsurface physical prop-erties such as lithology, moisture content, or temperature, which are ofprincipal interest to geologists, engineers and hydrologists. Due to theoverlapping resistivity ranges of many common earth materials, andthe influence of varying salinity, moisture and temperature, robustinterpretation of resistivity images requires other sources of groundtruth data. For ground investigation, resistivity imaging is routinelyused alongside intrusive methods such as drilling and cone penetrationtesting (CPT) (Wisen et al., 2005). In-situ measurement of resistivity(e.g. CPT resistivity profiles) or laboratory testing of subsurface mate-rials (e.g. Jackson et al., 2006; Russell and Barker, 2010) can be used todirectly constrain the inversion of resistivity data by fixing the resistiv-ities of known regions of the model (e.g. Günther and Rücker, 2009;Pidlisecky et al., 2006). For in-situ or laboratory measurements of resis-tivity to be suitable for constraining inversion, they must provide infor-mation at a scale appropriate to the resolution of the ERTmodel. This isparticularly crucial in highly heterogeneous subsurface conditions. Inthe case of CPT derived resistivitymeasurements, along-profile variabil-ity can be used to determine spatial averaging requirements. Likewise,for laboratory measurements of resistivity, sample volumes and distri-butions representative of the ERT survey resolution and materialheterogeneity are required. Investigation of the relationships betweenresistivity and temperature (Hayley et al., 2007, 2010; Musgrave andBinley, 2011), pore fluid salinity (Hayley et al., 2009; Lekmine et al.,2012; Monego et al., 2010), moisture content (Brunet et al., 2010;Cassiani et al., 2006), and lithology (e.g. Russell and Barker, 2010;Shevnin et al., 2007) are increasingly being undertaken to aid the inter-pretation of ERT sections and time-lapse resistivity monitoring data.

6.8. Anisotropy

The influence of evenmoderately anisotropic media on the results ofresistivity inversion that assumes isotropic conditions can producesignificant distortions in electrical tomograms (e.g. Greenhalgh et al.,2010;Herwanger et al., 2004). Typical geological causes of anisotropy in-clude fracturing, jointing, layering and rock fabric (e.g. strong alignmentof grains slates and shales). Detection of anisotropy has typically beenundertaken using azimuthal (Busby, 2000) and square array (Matias,2008) techniques. However, the most effective approach is perhapssurface to borehole imaging arrays, which unlike surface arrays,are sensitive to anisotropy associated with vertical or sub-vertical axesof symmetry, such as horizontal layering (Greenhalgh et al., 2010). Toaddress the problem of anisotropy, anisotropic resistivity inversionschemes have been developed (Greenhalgh et al., 2009; Herwangeret al., 2004; Kim et al., 2006; Pain et al., 2003); however, these arelimited by number of factors, including the requirement to solve foradditional parameters that exacerbates the problem of non-uniqueness,and the need for subsurface electrodes to effectively detect anisotropyassociated with horizontal layering (Greenhalgh et al., 2010).

7. Conclusions and future trends

There has been tremendous progress in the geoelectrical methodover the last 25 years. The images of the subsurface the methodprovides have become more geologically realistic, from simple 1-Dlayered models to complex 3-D structures varying with time. Therehave been parallel developments in field instrumentation, data inver-sion techniques, and automatic interpretation software that havemade this possible. It is now an established technique widely usedin mineral exploration, environmental, engineering, and hydrologicalsurveys not only on land but also in aquatic regions as well. In thenear future, we foresee an increasingly wider use of 3-D surveys incomplex areas in place of 2-D surveys, particularly with the wideravailability of multi-channel instruments. New research in practical


implementation of I.P. measurements using conventional electrodesand cables with the less powerful systems commonly used in engi-neering and environmental surveys (Dahlin and Leroux, 2012) willprovide useful additional information without increasing the surveytime and cost. Measurement of resistivity variations in both spaceand time with automatic monitoring systems will become increasinglyimportant in detecting incipient problems such as seepage from dams,landslides and gas accumulation in landfills; as well as in assistingthe optimal extraction of resources. To fully exploit the data frommulti-dimensional geoelectrical surveys, an understanding of the limi-tations of the technique and the proper use of regularization constraintsis important. The greatest limitation of the electricalmethod is the rapidreduction of resolution with distance from the electrodes. This limita-tion can be partly reduced by the use of subsurface electrodes locatednearer to the region of interest, and the use of other geophysical andgeotechnical data (if available) to constrain the inversion model.

Acknowledgments

We would like to thank two reviewers for their constructive com-ments that have helped to improve this paper. This paper is publishedwith permission of the Executive Director of the British GeologicalSurvey (NERC).

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