Seediscussions,stats,andauthorprofilesforthispublicationat:https://www.researchgate.net/publication/258794468

Electricalresistivitycharacterizationanddefectdetectiononageosyntheticclayliner(GCL)onanexperimentalsite

ARTICLEinJOURNALOFAPPLIEDGEOPHYSICS·MARCH2013

ImpactFactor:1.5·DOI:10.1016/j.jappgeo.2012.12.005

CITATIONS

4

READS

120

4AUTHORS,INCLUDING:

ColetteSirieix

UniversityofBordeaux

39PUBLICATIONS140CITATIONS

SEEPROFILE

JuanLuisFernándezMartínez

UniversityofOviedo

121PUBLICATIONS629CITATIONS

SEEPROFILE

RissJoëlle

UniversitéBordeaux1

74PUBLICATIONS365CITATIONS

SEEPROFILE

Allin-textreferencesunderlinedinbluearelinkedtopublicationsonResearchGate,

lettingyouaccessandreadthemimmediately.

Availablefrom:JuanLuisFernándezMartínez

Retrievedon:05February2016

Journal of Applied Geophysics 90 (2013) 19–26

Contents lists available at SciVerse ScienceDirect

Journal of Applied Geophysics

j ourna l homepage: www.e lsev ie r .com/ locate / jappgeo

Electrical resistivity characterization and defect detection on a geosynthetic clay liner(GCL) on an experimental site

C. Sirieix a, J.L. Fernández Martínez b,⁎, J. Riss a, F. Genelle a

a Univ. Bordeaux, I2M, UMR 5295, F-33400 Talence, Franceb Departamento de Matemáticas, Área de Matemática Aplicada, C/Calvo Sotelo S/N, 33006 Oviedo, Spain

⁎ Corresponding author at: Univ. Bordeaux, I2M, UMRFacultés, F-33400 Talence, France. Tel.: +33 5 40 00 87

E-mail addresses: colette.sirieix@u-bordeaux1.fr (C.(J.L. Fernández Martínez), joelle.riss@u-bordeaux1.fr (J.fanny.genelle@u-bordeaux1.fr (F. Genelle).

0926-9851/$ – see front matter © 2012 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.jappgeo.2012.12.005

a b s t r a c t

a r t i c l e i n f o

Article history:Received 22 May 2012Accepted 17 December 2012Available online 23 December 2012

Keywords:Electrical resistivityGCLLandfill coverTomographyVESParticle swarm optimization

In this paper we analyze the onsite characterization of a geosynthetic clay liner (GCL) that serves to ensurethe impermeability of a landfill cap by DC electrical methods. The imaging of the GCL geoelectrical propertiesis a challenging problem because it is a very thin (between 4 and 7 mm thick) and resistive layer (from100,000 to 2,000,000 Ω·m) depending on meteorological conditions and aging. We compare results obtainedusing electrical resistivity tomography (ERT) using two different kinds of arrays (dipole–dipole DD andWenner–Schlumberger) on an experimental site with engineered defects. To confirm these results and tofind the real onsite GCL resistivity we have performed sampling of the posterior distribution of this parameterusing vertical electrical sounding (VES) inversions. Different VES methods were extracted from ERT with DDarray and converted into a Schlumberger array.As a main conclusion the dipole–dipole array provides a better resistivity resolution of the defects than theWenner–Schlumberger array. On ERT images, the defect detection seems to be impossible if the GCL hasvery high resistivity, as it happened when it was put in place. Taking into account the equivalence rules,the inversions are in both cases (ERT and VES) compatible. The GCL resistivity estimated from PSO (particleswarm optimization) varies from 3.0 105 to 1.106 Ω·m depending on saturation conditions during the twentyfirst months of its placing. Then, the resistivity dropped to 4.104–9.104 Ω·m, indicating a probable chemicaldamage of the GCL due to aging. Finally the fact that the VES inversions are solved via PSO sampling allows forthe detection of a very thin and resistive layer and opens the possibility of performing micro VES surveysalong the landfill to detect possible GCL defects.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

Industrial activities in developed countries generate an increasingamount of waste. According to the United Nations EnvironmentProgramme (2005), wastes are materials that are not prime productsfor which the generator has no further use in terms of his own purposesof production, transformation or consumption, and of which hewants todispose. Waste is usually stored in landfills, that comprise several sec-tions or cells that are separated from the underlying soil by a passivesecurity barrier (lowpermeability clay) associatedwith an active barrier.At the end of the waste storage, the cells are protected with a cover, to

5295, Bat B 18, Avenue des97.Sirieix), jlfm@uniovi.esRiss),

rights reserved.

minimize water infiltration into the waste, and therefore to reduce thequantity of leachate.

According to the French Law (Journal Officiel de la RépubliqueFrançaise, 18th December, 1992) and recommendations (ComitéFrançais des Géosynthétiques, 2011), in the case of hazardous wastelandfills, called ISDD (Installation de Stockage de Dechets Dangereux),the superficial cover must contain a geomembrane or a geosyntheticclay liner (GCL), which is an important part of the cover impermeability.These materials are two kinds of geosynthetics: geomembranes andGCLs. Geomembranes are relatively impermeable sheets of polymericformulations (1 mm thick) while GCLs are composite materialsconsisting of a thin layer of bentonite bonded to two geotextiles(5–7 mm thick). Moreover, GCL has a very low hydraulic conductivityto water (kb1.10−10 m·s−1) when the bentonite is fully saturated(Bouazza, 2002). The resistivity of the GCL is very high probably due tothe insulating nature of the geotextiles, but a successful measurementhas not been made. Although ideally the barriers would never be dam-aged, such covers are often cracked and eroded due to mechanical,

Fig. 1. Plan overview a) and section b) of the landfill experimental site.

20 C. Sirieix et al. / Journal of Applied Geophysics 90 (2013) 19–26

climatic and hydraulic stresses that act on their surface (i.e. aging pro-cesses), or they can even be damaged during its positioning. The tight-ness of the seal of the cover needs to be ensured over time to limit themaintenance costs of landfills and avoid possible health and environ-mental damages. Also, leachate treatment is very expensive and is pro-portional to its quantity. Thus, locating damaged cover areas is crucialto limit leachate, and predict and overcome future environmentalproblems.

In the frame of damage detection using electrical methods, papers aremainly devoted to the case where the geomembrane is placed under thewaste storage but there exist few papers concernedwith the whole land-fill cap system including the GCL. Forget et al. (2005) tested the efficiencyof several electrical methods for the monitoring of the geomembraneintegrity. Some of these methods need the installation of sensors underthe geomembrane before its placement. One of these methods consistsin applying an electrical current between one electrode placed abovethe geomembrane and a second electrode at a remote location outsidethe cells. The electrical potential is measured using a permanent grid ofelectrodes placed beneath the liner (White and Barker, 1997). These elec-trical potentialmeasurements can also be performedby the electrical leakimaging method, which uses two electrodes that move above thegeomembrane, either in the waste material (Colucci et al., 1999) or inthe drainage layer (Laine et al., 1997). As most synthetic geomembranesare effective electrical insulators, a leak creates a localized current path-way that perturbs the electrical potential field. All thesemethods are effi-cient if installed before the sealing of the waste storage.

At least, in the case of old landfills, themonitoring of theGCL integrityin a cost-effective way requires the use of non-invasive techniques.Anomalous areas, detected by these non-invasive methods that are pos-sibly linked with cover damage, then can be checked by a drilling surveyperformed at these discrete locations. Due to their non-destructive char-acter, geophysicalmethods represent very interesting tools for the detec-tion of cover damages. To date, these methods havemainly been used tocharacterize contaminated plumes (Chambers et al., 2006; Gallas et al.,2010; Naudet et al., 2004; Ogilvy et al., 2002), and also to define thenature of materials and waste onsite (Boudreault et al., 2010; Guérin etal., 2004; Leroux et al., 2007; Vaudelet et al., 2011). As leachate is electri-cally very conductive, it is a suitable target for electrical methods,especially those that are used to monitor leachate recirculation in biore-actors (Clément et al., 2011; Grellier et al., 2008). However, to the best ofour knowledge, there exist few published studies that have used geo-physical methods to characterize covers on landfills (Carpenter et al.,1991; Cassiani et al., 2008). The electrical leak imaging method hasbeen applied when the geomembrane is a part of the landfill cap system(Beck, 2011; Hansen and Beck, 2009). However, the detection of defectsin this case is only possible under particular conditions, which dependson the nature and thickness of the cover (soil). Some other studieshave been carried out on experimental sites under more controlled con-ditions to test the feasibility of geophysical methods in the detection ofcover heterogeneities (Genelle et al., 2011; Guyonnet et al., 2003).

In the present paper, we present the characterization of onsite GCLelectrical resistivity and defect detection in an experimental site thathas been built and designed for this purpose. Different surveys havebeen performed for three years to monitor the temporal evolution ofthe GCL resistivity. Imaging the GCL is a challenging problem due tothe electrical equivalence rules: a very thin resistive layer is imaged asthicker and less resistive. We show a comparison of the results obtainedusing electrical resistivity tomography (ERT) from dipole–dipole (DD)and Wenner–Schlumberger (WS) arrays and the vertical electricalsounding inversions. The vertical electrical soundings are extractedfrom ERT from the DD array, transformed into Schlumberger configura-tion and inverted via particle swarm optimization (PSO) and used asan approximate posterior sampler. This is possible due to the VES re-duced number of parameters, 2n−1, where n is the number of thegeoelectrical layers. Thus, in the case of the VES inversions, it is possibleto find posterior distribution of the GCL resistivity and thickness.

2. Site description

An experimental site has been built to characterize the onsite GCLresistivity and its detectability depending on ambient conditions(temperature, humidity, rainfall infiltration and soil saturation).Fig. 1 shows a plan overview and a section of the experimental site.The site is 1.5 m deep and has a surface of 12×11 m2. From the bot-tom to the surface it is composed of the following materials:

1. a layer of compacted clay, 1 m thick,2. a geosynthetic clay liner (GCL), 6 mm at the beginning of the cover's

installation, composed of activated calcium bentonite and needlepunched geotextiles,

3. a layer composed of artificial gravels, 30 cm thick, and finally,4. a top soil, 15 cm thick, needed to restore the site.

The site has been monitored using humidity and temperature sen-sors located below the GCL (in the clay layer) at 0.7 meter depth. Theaim of these sensors is to control the influence of the humidity condi-tions on the resistivity inversions and to correct (if necessary) theeffect of temperature on the resistivity. Further analyses haveshown that the effect of temperature is very weak (around 12 Ω·mwhen the resistivity of the GCL is greater than 1·105 Ω·m). Thus,no correction is necessary in this particular case. A weather stationwas also set up near the site to record the meteorological conditions:precipitation, atmospheric temperature, etc.

Fig. 2 shows several stages in the site building and design. The GCLwas put on the clay material sprinkled just before its installation.Bentonite was also hydrated by precipitations that occurred afterthe experimental site building. Particularly, Fig. 2b and c shows twokinds of defects – overlap failure and GCL hole – that we would liketo detect on real waste disposals. Fig. 2d shows the gravel stage. Thesite, design and construction, was finished in September 2009. ERTwas carried out over some defects: not only overlap failure and GCLhole but also the overlap of the two liners (Fig. 1).

Several electrical resistivity tomography (ERT) surveys have beenconducted between February 2010 and January 2012 under differentmeteorological conditions to analyze the temporal evolution of theGCL resistivity and the detectability of the induced defects dependingon the surrounding conditions. The measurements were performedthrough the installation of 48 electrodes (rods of 30 cm and 1 cm indiameter) placed until a 10 cm depth and reinstalled for each survey,

Fig. 2. Some details of the site construction. a) GCL installation. b) Detail of overlap failure. c) GCL hole. d) Gravel installation.

21C. Sirieix et al. / Journal of Applied Geophysics 90 (2013) 19–26

with 0.25 m electrode spacing. Even if the hypothesis of punctual elec-trode is not strictly fulfilled, it has been shown that measurementswith larger spacing of 50 cm provide similar results. Moreover, theERT profile was pinpointed thanks to two stakes permanently placedat each end of the profile. The electrodes were connected to a fast resis-tivity meter (SYSCAL PRO) with a procedure described in Peter-Borieet al. (2011) using two different arrays: dipole–dipole and Wenner–Schlumberger. Table 1 summarizes by date the different surveys (arraytype) that have been conducted on this experimental site along withthe environmental conditions averaged or cumulated over a period ofseven days before each survey: the average atmospheric temperature,the cumulated rainfall and the effective cumulated rainfall taking outthe effect of evapotranspiration, and the soil temperature measured inthe site, thanks to sensors located at 0.7 m below the GCL. Here, the ef-fective rainfall is defined as the total rainfall minus the evapotranspira-tion known, thanks to the weather station. The evapotranspiration iscalculated using the Penman–Monteith equation (Allen et al., 1998)and the measurement of the solar radiation, thanks to a pyranometer.

Table 1Survey types and environmental conditions.

Date(D/M/Y)

Arraytype

Average atmospherictemperature(°C)

Soiltemperature(°C)

Weekcum.rainfall(mm)

Effectiverainfall(mm)

4/02/2010 WS, DD 2.8 6.6 11.4 6.528/09/2010 WS, DD 15.4 19.3 11.4 −7.520/05/2011 DD 16.7 16.7 9 −2422/07/2011 DD 17 19.2 48.8 29.228/07/2011 DD 18.3 19.4 6.2 −5.830/01/2012 WS, DD 6.3 9.2 8.8 5.8

It is possible to observe that half of the surveys were conductedunder dry/very dry conditions (28/09/2010, 20/05/2011, 28/07/2011)while for the rest (three other surveys) the water content is higherdue to an increase of the effective rainfall. Furthermore, the surveyconducted on28/09/2010was realized after a very hot and dry summer.These conditions are not abnormal since the site is located in the south-west of France. Also theWenner–Schlumberger ERT is available only forthree surveys (4/02/2010, 28/09/2010, 30/01/2012); because at thebeginning of the monitoring as no change in resistivity was measured,we decided to perform only dipole–dipole array which is the fastest.

3. Methodology

3.1. Electrical resistivity tomography (ERT)

To invert the ERT surveys, we have used the commercial softwareRES2Dinv© (Loke and Barker, 1995). For the forward modeling wehave used its finite difference module with model refinement to takeinto account the small near surface inhomogeneities, and in this partic-ular case, the GCL defects that have been induced on purpose in thedesign of the experimental site.

To perform the inversion we have tried first the Gauss–Newtonmethod with smoothness regularization. This algorithm uses an itera-tive linearization of the forward operator F on the last solution andis related to the use of the L2 norm in the data misfit and in the modelregularization. The L2 norm tends to smooth the earth resistivities,and thus, the obtained results are in this case less representative ofthe reality, since we are looking for abrupt transitions. The best resultswere obtained with the L1 norm smoothness-constrained optimi-zation method (Genelle, 2012; Loke et al., 2003). This method per-forms an iteratively reweighted least square algorithm to approximate

22 C. Sirieix et al. / Journal of Applied Geophysics 90 (2013) 19–26

numerically the solution in the L1 norm (Loke, 2004; Wolke andSchwetlick, 1988). The L1 norm minimization algorithm provides theresistivities a blocky character. Also, the use of the L1 norm in the pre-diction error provides the optimization a very robust character, reduc-ing the effect of the outliers in the apparent resistivities.

The advantage of ERT is to provide an estimation of the overall twodimensional structure of the resistivity field along the survey assumingthat no lateral (perpendicular to the profile) change occurs. This situa-tion is obviously hypothetical since the resistivity anomalies are alwaysderived from real 3D structures. Conversely the ERT forward problemneeds higher computation resources, and the inverse problem uncer-tainty analysis is usually restricted to the use of linear techniques, thatare only valid in the neighborhood of the final solution that has beenfound (Fernández Martínez et al., 2012). Also, these local optimizationtechniques provide only a plausible solution, among others that belongto the nonlinear equivalent region and fit the observed data within thesame error bounds. The presence in this case of a very thin and resistivelayer (GCL) causes uncertainty analysis due to the crucial importance tostudy electrical equivalences (Maillet, 1947). Thus, alternative tech-niques for uncertainty analysismust be used tofind and assess the solu-tion that has been found.

3.2. Vertical electrical sounding (VES)

The vertical electrical sounding is a DC-resistivity geophysical tech-nique that is aimed at imaging the resistivities and thicknesses of a strat-ified earth. In this case, the geometry of the site (horizontally stratifiedearth) allows us to perform a set of 1D vertical electrical sounding inver-sions. To perform this second inversion procedure, a set of vertical electri-cal sounding (VES) in different areas of the experimental site wasextracted from the ERT survey to monitor the temporal evolution of theGCL bulk electrical resistivity. This is a challenging problem due to theprinciple of electrical equivalences (Maillet, 1947): a very thin layer(4 to 7·10−3 m) with a very high resistivity (around 2·106 Ω·m ondry conditions as it will be shown later) will be generally imaged (dueto the optimization process) as a thicker and much less resistive layer.This will be the case in the ERT sections. Previously the VES apparentresistivities were transformed to Schlumberger configuration if needed.This procedure allows sampling the equivalent GCL resistivity modelswithin a prescribed search space for both its resistivity and its thickness.In this case the GCL thickness is better known and varies between 4 and7 mm. The VES inverse problem is solved as a sampling problem usingPSO (Fernández Martínez and García Gonzalo, 2008, 2009; FernándezMartínez et al., 2010, 2011). The software we used is designed to invertthe VES acquired using the Schlumberger array, thus, in the case of thedipole–dipole array, we have transformed the apparent resistivity curveinto the Schlumberger compatible format using the methodology pub-lished by Patella (1974).

For the case of a parallel dipole–dipole array the relationshipbetween the apparent resistivities in both configurations is:

ρd sð Þ ¼ ρsch sð Þ− sαdρsch sð Þ

ds; ð1Þ

where ρd(s) is the dipole–dipole apparent resistivity; ρsch(s) is theSchlumberger counterpart, s is the distance between the centers ofboth dipoles (injection and measurements), and α is a real parameterthat depends on the type of dipole configuration. In the case of paralleldipole,α=2. Patella'smethod allows for the integration of this ordinarydifferential equation to find the Schlumberger apparent resistivity,ρsch(s). Also, other filtering methods have been proposed to performsuch transformation (Kumar and Das, 1977). Patella's method needs(in the case of α>0) the right asymptotic branch of the dipole–dipolecurve (i.e. for large electrode spacings) to be measured on the field orto carry out a proper extrapolation. In our case, no extrapolation ofthe right branch has been made.

Fig. 3 shows several Schlumberger apparent curves deduced from thecorresponding dipole–dipole surveys. The original dipole–dipole data cor-respond to the same common pseudo center (middle point) as it appearsin the dipole–dipole pseudosection. The transformed Schlumberger curveis compared to theWenner–Schlumberger curvemeasured in the field atthe same location for the cases where this curve is available. It is possibleto observe that both curves (measured and transformed) show a verygood fitting. This procedure allowed us to determine and invert theSchlumberger transformed curve from the dipole–dipole counterpart insurveys where no Wenner–Schlumberger survey has been conducted(see Table 1).

4. Results

In this section wewill first show the results obtained with the electri-cal resistivity tomography (ERT), both for the dipole–dipole and theWenner–Schlumberger arrays. Finally, these results will be compared tothe 1D inversions performed single vertical electrical soundings (VESs).

4.1. ERT dipole–dipole

Figs. 4 and 5 show the inversions found with the L1 robust normand model refinement for different datasets (Table 1).

It is possible to conclude the following:

1) Due to the issue of electrical equivalences (Maillet, 1947) the GCLis always detected as a thicker (from 0.2 to 0.75 m) and a lessresistive layer. Applying the principle of equivalence we havefound the following approximate resistivity (Table 2) for a GCLthickness of 7·10−3 m.

2) Applying this criterion, the GCL resistivity varies from 3·105 to7.8·105 Ω·m, depending on environmental conditions. The highestvalue of the estimated GCL resistivity coincides with a very dry andhot summer season (28/09/2010).

3) The GCL resistivity decreases (around 105 Ω·m) with time. Thiscan be due to the saturation of GCL and/or also to aging damage.

4) The GCL defects can be detected. For example, the hole located at7.5 m can be seen on all images in Fig. 5. Fig. 5b (28/07/2011)shows the overlap failure at 2.5 m; GCL overlap located at 8.2 m;and the boundary of excavation at 10.5 m. Defects are not so easilydetected on Fig. 5a and c and they are clearly undetectable inFig. 4. The detection is possible mainly when the GCL+gravel resis-tivity decreases. This detection of defects happens after July 2011.

5) No distinction between GCL and gravels can be made due to the prin-ciple of suppression, that is, the ERT images treat GCL and gravels as awhole. This situation worsens under dry/very dry and hot conditions.Under these conditions, it can be observed in Fig. 4b that the absoluteerror (5.1%) is higher compared to other similar inversions (between1 and 3%). The higher absolute error is maybe due to a higher noiselevel in the apparent resistivities induced by higher electrode contactresistances, as it has been noted from the RS check of theresistivimeter.

6) The underlying clay is clearly detected from 20/05/2011, with aresistivity lower than 40 Ω·m, and this fact is as it has alreadybeen shown by Genelle et al. (2012). It is hypothesized that this de-tection is related to a possible chemical damage of theGCLwhich oc-curred over time that induces a decreasing of its resistivity.Saturated conditions do also play an important role, but it is notthe case on 20/05/2011 (see Table 1). Based on that reason we sup-pose a possible damage of the GCL.

4.2. ERT Wenner–Schlumberger

Fig. 6 shows the three Wenner–Schlumberger inversions performedon the three dates shown in Table 1. Wenner–Schlumberger surveyshave an investigation depth of 0.9 m which is lower than in the

Fig. 3. Transformation for different surveys of the VES apparent resistivity dipole–di-pole curves to Schlumberger array. Comparison of the Schlumberger transformedcurves to the Wenner–Schlumberger measured apparent resistivity curves.

Fig. 4. Surveys from 04/02/2010 to 20/05/2011. L1 inversions using the dipole–dipolearray. (a) 04/02/2010 survey. (b) 28/09/2010 survey. (c) 20/05/2011 survey.

Fig. 5. Surveys from 22/07/2011 to 30/01/2012. L1 inversions using the dipole–dipolearray: (a) 22/07/2011 survey. (b) 28/07/2011 survey. (c) 30/01/2012 survey.

23C. Sirieix et al. / Journal of Applied Geophysics 90 (2013) 19–26

dipole–dipole case (around 1.1 m). Also, apparent resistivity variationsin theWenner–Schlumberger array are smaller than in the dipole–dipoleone because the investigated volume is larger with the Wenner–Schlumberger array than with the dipole–dipole array.

Table 2Estimation from ERT dipole–dipole inversions of the GCL equivalent resistivity.

Date GCL invertedresistivity (Ω·m)

Thicknessinverted(meters)

GCL estimated resistivity (Ω·m) for aGCL thickness of 7·10−3 m

04/02/2010

5500 0.5 390·103

28/09/2010

13,000 0.7 780·103

20/05/2011

6000 0.4 340·103

22/07/2011

2300 0.3 98·103

28/07/2011

1300 0.6 111·103

30/01/2012

1900 0.45 122·103

24 C. Sirieix et al. / Journal of Applied Geophysics 90 (2013) 19–26

Dipole–dipole surveys are known to be better than Wenner–Schlumberger for detecting vertical structures (Dahlin and Zhou,2004). The reason is that the dipole–dipole array is very sensitive to hor-izontal changes in the resistivity, but relatively insensitive to vertical var-iations. The dipole–dipole array is good in mapping vertical structuressuch as fractures and dykes (see Loke, 2004). Also one possible disadvan-tage of the dipole–dipole array compared to the Wenner–Schlumbergeris the signal to noise ratio, that decreases for large values of the n-factor,that is, the real number relating to the spacing between injection andmeasurement electrodes (Loke, 2004; Peter-Borie et al., 2011).

Compared to the dipole–dipole inversions, theWenner–Schlumbergermethod (Fig. 6) provides in all the cases a lower resistivity distributionover the whole profile (Figs. 4 and 5). This has been confirmed byinverting the dipole–dipole apparent resistivity data with the samedepth of investigation by filtering out deeper apparent resistivity levels

Fig. 6. L1 inversions using the Wenner–Schlumberger array: (a) 4/02/2010 survey.(b) 28/09/2010 survey. (c) 30/01/2012 survey.

in the dipole–dipole case (Fig. 7). The result shown in Fig. 7 needs to becompared with the inversion shown in Fig. 6a.

Although the Wenner–Schlumberger inverted resistivities are lowerthan the dipole–dipole counterparts, the overall resistivity structureover the different profiles and their temporal evolution are similar inboth cases. Finally, with respect to the hole detection in the GCL cover,it can be only seen at the 30/01/2012 survey under higher saturated con-ditions (see rainfall in Table 1), but its detection is harder than in the di-pole–dipole case. The other defects could not be detected with theWenner Schlumberger array.

4.3. VES inversions

To confirm the results obtained via the ERT inversion, we haveperformed theVES inversion of the dipole–dipole transformed apparentresistivity curvemeasured at a common center (ormiddle point), locat-ed at 4 m from the beginning of the ERT profile. The measurements atthis location are supposed to be not affected by any GCL defect. To per-form the inversionwe have used particle swarm optimization followingthe procedure shown in Fernández Martínez et al. (2010). In this casethe inversion problem is solved as a sampling problem using particleswarm optimization. Particle swarm provides a proxy of the posteriordistribution of the inverse model parameters if it is used in its explor-ative form. We have used the CP (centered-progressive) version of thePSO family (Fernández Martínez and García Gonzalo, 2009) due to itsexploratory character.

The only prior information that is used in the sampling procedure isthe model search space. We have adopted a 4 layer model with the fol-lowing lower and upper limits, both, for the resistivities and thicknesses:

Resistivity ¼ 20–100;500–4500;5⋅103–1⋅106

;10–100h i

Ω⋅m;

Thickness ¼ 0:1–0:2;0:25–0:35;4⋅10−3–7⋅10−3

h im:

The very dry conditions observed for the survey conducted in Sep-tember 2010, obliged us to modify the resistivity search space (con-serving the thicknesses) as follows:

Resistivity ¼ 20–400;500–500⋅103;5⋅103

–2⋅106;10–100

h iΩ⋅m:

Fig. 8 shows the different survey cumulative probability curves ofthe GCL resistivity parameter. As mentioned above, this curve hasbeen deduced from the posteriori sampling of this layer using particleswarm optimization under explorative conditions on the region of rela-tive misfit lower than 10%. From the temporal evolution of this curve, itcan be observed that we can cluster these cumulative probability curvesinto three groups:

Fig. 7. Dipole–dipole survey conducted on 04/02/2010. Inversion is obtained from di-pole–dipole apparent resistivity filtering out the deeper levels to achieve the samedepth of investigation as that in the Wenner–Schlumberger array.

25C. Sirieix et al. / Journal of Applied Geophysics 90 (2013) 19–26

1. The first group is characterized by a low median value (between4·104 and 9·104 Ω·m), and corresponds to the surveys conductedfrom July 2011. Other statistical descriptors follow a similar pattern.

2. The second group having a median resistivity of around 3·105 Ω·mcorresponds to February 2010 and May 2011.

3. Finally, the curve corresponding to September 2010 has a medianof 106 Ω·m and does correspond to very dry conditions as it wasregistered in the meteorological data.

A similar analysis was conducted near the border of the experimen-tal site where the gravels are not present. GCL resistivities varied from8·104 to 4·106 Ω·mon the different surveys and corresponding dates.

The VES inversions provided resistivity values for the different layerswhich were in accord to the dipole–dipole ERT inversions. The gravellayer showed a resistivity between 850 and 2100 Ω·m in saturated con-ditions, and 4500 and 10,000 Ω·m for dry/very dry conditions. This isan important knowledge due to the suppression issue.

Concerning the GCL resistivity, it varies from 3·105 to 4·106 Ω·m(under dry conditions) during the first 22 months. This resistivitydrops from 4·104 to 9·104 Ω·m after the first 22 months. Due tothe fact that the first survey was conducted 3 months after the GCLsetting, and taking into account that the total GCL hydration occursduring several weeks after its placement (Rayhani et al., 2011), wethink that this decrease in GCL resistivity, appearing later than oneyear and a half, cannot be only attributed to a higher water contentof the GCL. Thus, the hypothesis of chemical damage due to aging ismore likely. Chemical damage may be due to the calcium exchangethat occurred over time between bentonite and the water infiltration.The calcium exchange is a well-known phenomenon that induces adecrease of the GCL's permeability of one order of magnitude in loga-rithmic scale over time after its installation (Bouazza et al., 2007;Egloffstein, 2001).

5. Conclussion and discussion

In this paper, we have presented the electrical resistivity tomographyanalysis of the cover of a landfill experimental site. The cover was com-posed of top soil, gravels, a geosynthetic clay liner (GCL) and clays, mim-icking the design that it is found in real waste disposals. We haveattempted to image possible defects found in the GCL layer in real sitesusing ERT and VES techniques. These defects include GCL tears, GCLholes, overlapping defects and temporal damaging of the GCL properties

Fig. 8. Temporal evolution of the cumulative probability curve of the GCL resistivity,deduced from the posterior sampling of this parameter via CP-PSO in the region of rel-ative misfit lower than 10%.

due to aging process. The main challenge found was to characterizeonsite the GCL resistivity, depending on weather and saturation condi-tions. Up to our knowledge no values of this property are given in theliterature.

Imaging techniques (ERT) were unable to model properly this verythin and resistive layer due to the principle of equivalence. Nevertheless,the results obtained using dipole–dipole surveys, taking into account thisissue, are in accordwith themedian resistivity found using 1DVES inver-sions via PSO sampling in the region of relative misfit lower than 10%.Minimum misfits vary from 1 to 5% depending on the survey andon the middle point where the VES apparent resistivity curve wascalculated.

Wenner–Schlumberger surveys seem to provide systematically, in ourstudy, lower resistivities than the dipole–dipole configuration. Neverthe-less, their overall structure and temporal evolution are in the same way.However, defect detectability is easier with the dipole–dipole array thanwith using the Wenner–Schlumberger array. GCL resistivities vary from3·105 to 4·106 Ω·m (depending on saturation conditions) during thefirst twenty two months. After, the GCL resistivity suddenly dropped to4·104 to 105 Ω·m (one or two orders of magnitude less) indicating aprobable chemical damage of this cover due to aging. The defect detectionseems to be impossible if the GCL has a very high resistivity, as it hap-penedwhen itwas originally put in place. In this case the overlying graveldoes not help either to its detection. Finally the fact that VES solved (viaPSO sampling) the detection problem of a very thin and resistive layer,opens the possibility of performing a micro VES survey along the site todetect possible defects.

Acknowledgments

The authors acknowledge the University of Bordeaux 1 for thegrant given to Prof. Fernández Martínez, which allowed us to accom-plish this research work. We would like to thank Stéphane Rénié(Hydro Invest) and Bruno Dubearnes for their help in setting up theexperimental site, and also Fabien Naessens for the electrical resistiv-ity tomography measurements. We also thank the Agence del'Environnement et de la Maîtrise de l'Energie (ADEME) and mostparticularly Philippe Bégassat for his collaboration in this project.

References

Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evaporatranspiration: guidelinesfor computing crop water requirements. F.A.O. irrigation and drainage. (326 pp.).

Beck, A., 2011. Technical improvements in dipole geoelectric survey methods. Sympo-sium on Geosynthetics (GEOFRONTIERS), Dallas, USA. http://dx.doi.org/10.1061/41165(397)290.

Bouazza, A., 2002. Geosynthetic clay liners. Geotextiles and Geomembranes 20, 3–17.Bouazza, A., Jefferis, S., Vangpaisal, T., 2007. Investigation of the effects and degree of

calcium exchange on the Atterberg limits and swelling of geosynthetic clay linerswhen subjected to wet–dry cycles. Geotextiles and Geomembranes 25, 170–185.

Boudreault, J.P., Dube, J.S., Chouteau, M., Winiarski, T., Hardy, E., 2010. Geophysicalcharacterization of contaminated urban fills. Engineering Geology 116, 196–206.

Carpenter, P.J., Calkin, S.F., Kaufmann, R.S., 1991. Assessing a fractured landfill cover usingelectrical resistivity and seismic refraction techniques. Geophysics 56, 1896–1904.

Cassiani, G., Fusi, N., Susanni, D., Deiana, R., 2008. Vertical radar profiling for the assess-ment of landfill capping effectiveness. Near Surface Geophysics 6, 133–142.

Chambers, J.E., Kuras, O., Meldrum, P.I., Ogilvy, R.D., Hollands, J., 2006. Electrical resis-tivity tomography applied to geologic, hydrogeologic, and engineering investiga-tions at a former waste-disposal site. Geophysics 71, 231–239.

Clément, R., Oxarango, L., Descloitres, M., 2011. Contribution of 3-D time-lapse ERT tothe study of leachate recirculation in a landfill. Waste Management 31, 457–467.

Colucci, P., Darilek, G.T., Laine, D.L., Binley, A., 1999. Locating landfill leaks covered withwaste. Symposium on Waste Management and Landfill, Cagliari, Italy.

Comité Français des Géosynthétiques, 2011. Recommandations pour l'utilisation desgéosynthétiques bentonitiques en installations de stockage de déchets . (84 pp.).

Dahlin, T., Zhou, B., 2004. A numerical comparison of 2D resistivity imaging with 10electrode arrays. Geophysical Prospecting 52 (5), 379–398.

Egloffstein, T.A., 2001. Natural bentonites—influence of the ion exchange and partialdessication on permeability and self-healing capacity of bentonites used in GCL.Geotextiles and Geomembranes 19, 427–444.

Fernández Martínez, J.L., García Gonzalo, E., 2008. The generalized PSO: a new door toPSO evolution. Journal for Artificial Evolution and Applications. http://dx.doi.org/10.1155/2008/861275 (Special Issue on Particle swarms: the second decade. Arti-cle ID 861275, 15 pages).

26 C. Sirieix et al. / Journal of Applied Geophysics 90 (2013) 19–26

Fernández Martínez, J.L., García Gonzalo, E., 2009. The PSO family: deduction, stochasticanalysis and comparison, swarm intelligence. Special Issue on Particle Swarm Opti-mization 3–4, 245–273.

Fernández Martínez, J.L., García Gonzalo, E., Fernández Alvarez, J.P., Kuzma, H., MenéndezPérez, C.O., 2010. PSO: a powerful algorithm to solve geophysical inverse problems.Application to a 1D-DC resistivity case. Journal of Applied Geophysics 71-1, 13–15.

Fernández Martínez, J.L., Fernández Muñiz, Z., Tompkins, Michael J., 2012. On the to-pography of the cost functional in linear and nonlinear inverse problems. Geophys-ics 77 (W1). http://dx.doi.org/10.1190/geo2011-0341.1.

Fernández-Martínez, J., García-Gonzalo, L., Mukerji, T., 2011. How to design a powerfulfamily of particle swarm optimizers for inverse modelling. Transactions of the Instituteof Measurement and Control 1–15. http://dx.doi.org/10.1177/0142331211402900.

Forget, B., Rollin, A.L., Jacquelin, T., 2005. Lessons learned from 10 years of leak detection sur-veys on geomembrane. Symposium onWaste Management and Landfill, Sardinia, Italy.

Gallas, J.D.F., Taioli, F., Filho,W.M., 2010. Induced polarization, resistivity, and self-potential: acase history of a contamination evaluation due to landfill leakage. Environmental EarthSciences 63, 251–261 (ISSN: 1866-6280).

Genelle, F. 2012. Les méthodes géophysiques pour la caractérisation des couverturesd'installation de stockage de déchets. Thèse de l'Université Bordeaux 1. 366 pp.http://www.theses.fr/16370595X.

Genelle, F., Sirieix, C., Naudet, V., Riss, J., Naessens, F., Renié, S., Dubearnes, B., Bégassat,P., Trillaud, S., Dabas, M., 2011. Geophysical methods applied to characterize land-fill covers with geocomposite. Symposium on Geosynthetics (GEOFRONTIERS),Dallas, USA. http://dx.doi.org/10.1061/41165(397)199.

Genelle, F., Sirieix, C., Riss, Naudet, V., 2012. Monitoring landfill cover by electrical re-sistivity tomography on an experimental site. Engineering Geology 145, 18–29.

Grellier, S., Guérin, R., Robain, H., Bobachev, A., Vermeersch, F., Tabbagh, A., 2008. Mon-itoring of leachate recirculation in a bioreactor landfill by 2D electrical resistivityimaging. Journal of Environmental and Engineering Geophysics 13, 351–359.

Guérin, R., Bégassat, P., Benderitter, Y., David, J., Tabbagh, A., Thiry, M., 2004. Geophys-ical study of the industrial waste land in Mortagne-du-Nord (France) using electri-cal resistivity. Near Surface Geophysics 3, 137–143.

Guyonnet, D., Gourry, J.-C., Bertrand, L., Amraoui, N., 2003. Heterogeneity detection inan experimental clay liner. Canadian Geotechnical Journal 40 (1), 149–160.

Hansen, R., Beck, A., 2009. Electrical leak location surveys for landfill caps. Symposiumon the Perspective on Environmental and Water Resources (Environmental andWater Resources Institute Conference), Bangkok, Thailand.

Journal Officiel de la République Française, 1993. Arrêté du 18 décembre 1992 relatif austockage de certains déchets industriels spéciaux ultimes et stabilisés pour les in-stallations existantes . (30 mars).

Kumar, R., Das, U.C., 1977. Transformation of dipole to Schlumberger sounding curvesby means of digital linear filters. Geophysical Prospecting 25, 780–789.

Laine, D.L., Binley, A.M., Darilek, G.T., 1997. Locating geomembrane liner leaks underwaste in a landfill. Symposium on Geosynthetics, Long Beach California, USA.

Leroux, V., Dahlin, T., Svensson, M., 2007. Dense resistivity and induced polarizationprofiling for a landfill restoration project at Härlöv, Southern Sweden. Waste Man-agement & Research 25, 49–60.

Loke, M.H., 2004. Tutorial: 2-D and 3-D Electrical Imaging Surveys . Pdf document.Loke, M.H., Barker, R.D., 1995. Least-squares deconvolution of apparent resistivity

pseudosections. Geophysics 60 (6), 1682–1690.Loke, M.H., Acworth, Ian, Dahlin, T., 2003. A comparison of smooth and blocky inversion

methods in 2D electrical imaging surveys. Exploration Geophysics 34, 182–187.Maillet, R., 1947. The fundamental equations of electrical prospecting. Geophysics 12,

529–556.Naudet, V., Revil, A., Rizzo, E., Bottero, J.-Y., Bégassat, P., 2004. Groundwater redox con-

ditions and conductivity in a contaminant plume from geoelectrical investigations.Hydrology and Earth System Sciences 8, 8–22.

Ogilvy, R.D., Meldrum, P.I., Chambers, J.E., Williams, G., 2002. The use of 3D electrical resis-tivity tomography to characterise waste and leachate distributions within a closedlandfill, Thriplow, UK. Journal of Environmental and Engineering Geophysics 7, 11–18.

Patella, D., 1974. On the transformation of dipole to Schlumberger sounding curves.Geophysical Prospecting 22, 315–329.

Peter-Borie, M., Sirieix, C., Naudet, V., Riss, J., 2011. Electrical resistivity monitoringwith buried electrodes and cables: noise estimation with repeatability test. NearSurface Geophysics 9, 369–380.

Rayhani, M.T., Rowe, R.K., Brachman, R.W.I., Take, W.A., Siemens, G., 2011. Factors affect-ing GCL hydration under isothermal conditions. Geotextiles and Geomembranes 29,525–533.

United Nations Environment Programme, 2005. Solid waste management. Chapter III:Waste Quantities and Characteristics, pp. 31–38 (http://www.unep.or.jp/Ietc/Publications/spc/Solid_Waste_Management/index.asp).

Vaudelet, P., Schmutz, M., Pessel, M., Franceschi, M., Guérin, R., Atteia, O., Blondel, A.,Ngomseu, C., Galaup, S., Rejiba, F., Bégassat, P., 2011. Mapping of contaminant plumeswith geoelectrical methods. A case study in urban context. Journal of Applied Geophys-ics. http://dx.doi.org/10.1016/j.jappgeo.2011.09.023.

White, C.C., Barker, R.D., 1997. Electrical leak detection system for landfill liners: a casehistory. Ground Water Monitoring and Remediation 17 (3), 153–159.

Wolke, R., Schwetlick, H., 1988. Iteratively reweighted least squares algorithms conver-gence analysis and numerical comparisons. SIAM Journal of Scientific and Statisti-cal Computations 9, 907–921.