detection of landfill cover damage using geophysical methods
TRANSCRIPT
© 2014 European Association of Geoscientists & Engineers 599
Near Surface Geophysics, 2014, 12, 599-611 doi:10.3997/1873-0604.2014018
Detection of landfill cover damage using geophysical methods
Fanny Genelle, Colette Sirieix, Joëlle Riss, Véronique Naudet, Michel Dabas
and Philippe Bégassat
Received February 2012, revision accepted December 2013
ABSTRACTIn closed hazardous waste (i.e. industrial) landfills, impermeable covers are used to seal in the waste so as to
minimise water infiltration and the accumulation of leachate inside the waste. In this paper, we present a geo-
physical study performed on a French landfill where the cover was designed using impermeable (clay) and
drainage (sand) layers along with a Geosynthetic Clay Liner (GCL) in between. As observed, the quantity of
leachate increases after rain events making the leachate treatment more expensive. This could be due to areas of
weakness in the cover that have developed over time or which date back to its installation. Three different geo-
physical methods were employed and confronted to detect and assess such damage, and, if necessary, to help
choose the most suitable remediation of the cover: Automatic Resistivity Profiling (ARP) which enables a rapid
mapping of the entire landfill; the self-potential method (SP) as one of its sources is water drainage; and electri-
cal resistivity tomography (ERT) to obtain 2D resistivity models. Based on the ARP results revealing high lat-
eral heterogeneities of the superficial cover materials, the SP and ERT measurements were conducted on a
limited area. A negative SP anomaly was observed at the top of the landfill, where the cover is the thinnest and
the GCL the most damaged, suggesting preferential infiltration under the GCL. The 2D ERT profile showed both
vertical and horizontal variations. Comparison with manual auger holes showed that the alternating clayey and
sandy layers did not conform to plans for the original cover when it was installed at this site about twenty years
ago. Variations in electrical resistivity noticed at the depth of the GCL were attributed to possible damage to the
GCL and a greater than expected permeability since the GCL is normally very resistive. The results obtained in
this study were important to understand the damage to the cover due to the use of different types and thick-
nesses of cover materials compared to what was originally planned, and deterioration of the GCL.
• the use of different cover materials (variations in type and thickness) from those of the cover as it was originally intended.
As leachate treatment is very expensive and proportional to quan-tity, the sealing of the cover needs to be ensured over time to limit maintenance costs of landfills. Thus, locating areas where the cover is damaged (heterogeneities, thickness variations in the clay materials, tears and chemical ageing in the GCL...) is crucial in limiting the increase of leachate within a landfill, which is why landfill cover monitoring is a topical subject.
Among the studies undertaken on landfills, few of them have focused on covers. Most of them were concerned with detecting defects below the waste on the geomembrane or the GCL.
Following geomembrane installation at the bottom of the cells, several electrical methods have been shown to be efficient when used to monitor geomembrane integrity (Forget et al. 2005; ASTM D6747). As most synthetic geomembranes are effective electrical insulators, a leak creates a localised passage of current that disturbs the electrical potential field in a distinctive way. One electrical monitoring method consists of first applying an electric current between one electrode placed above the geomembrane and a second electrode at a remote location outside the cells. Then the electrical potential is measured using a permanent grid of electrodes placed beneath the liner (White and Barker 1997). The
INTRODUCTIONEngineered landfills generally comprise several waste deposit cells that are filled with waste and separated from the underlying soil by a passive security barrier (essentially low permeability clay) along with an active barrier (e.g., a geomembrane). When the landfill site is full, the waste deposit cells are usually pro-tected with a clayey cover to minimise water infiltration into the waste, thereby limiting the quantity of leachate produced. In the case of hazardous waste (i.e., industrial) landfills, the clayey cover must also contain a geomembrane or a Geosynthetic Clay Liner (GCL), which forms a part of the impermeable cover (in accordance with French Law published in the Journal Officiel de la République Française, 1993).
While in an ideal world such covers would never become damaged, they often become cracked and eroded. Three factors can cause this kind of damage:• the action of mechanical, climatic and hydraulic stresses on
their surface (i.e., ageing processes; Melchior 1997; Egloffstein 2001; Gourc et al. 2010),
• the incorrect preparation and installation of the cover materials (sand, clay, GCL or geomembrane (Touze-Foltz 2001)),
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anomalous zones previously detected by ARP. In these zones it is possible to compare the ARP dataset to those of the more com-monly used SP and ERT methods. This was a good opportunity to analyse how each method could contribute to our knowledge of the landfill cover, and specifically in terms of the location and estimated extent of any possible damage. Geophysical surveys can also help site managers to choose what type of remediation is most suitable for the cover (e.g. a total rebuild or the repair of a specific area). Before that, it is necessary to carry out drilling surveys to check if the variations in the properties of the cover materials detected by geophysics correspond to genuine damage to the cover.
SITE DESCRIPTION AND METHODSThe landfillThe study site is an old hazardous waste landfill in a former clay quarry (of the Lower Permian age) in France. During the exploi-tation of the landfill (1978 to 1988), around 4.105 tonnes of hazardous (industrial) waste were deposited over an area of 42,000 m2. Twelve waste deposit cells were filled with alterna-ting 1 metre thick layers of waste (previously mixed with clay) and 0.3-m-thick layers of clay, producing a waste dome of around 13 m in height. In 1992, all of the waste deposit cells were levelled and reshaped so that they became indistinguishable from each other. After this remodelling phase, an impermeable cover was used to seal the entire waste and to limit vertical water seepage. The cover was composed of impermeable layers (clay) and drainage layers (sand) 2.3 m thick (see the original plans for the cover on Fig. 1). To increase the waterproofing properties of the cover, a GCL was placed in the middle of the cover at a depth of approximately 1.1 m. This was composed of a thin layer of bentonite bonded to two layers of geotextiles (Fig. 2a). This 7-mm-thick material has a very low hydraulic conductivity to water (k< 1.10-10m.s-1) when new and the bentonite is fully satu-rated (Bouazza 2002). In addition to the cover, a vertical cement-bentonite cut-off wall enclosed the landfill to prevent water entry
electrical potential can also be measured using two electrodes that move above the geomembrane, in the waste material (Colucci et al. 1999) or in the drainage layer (Laine et al. 1997). The latter method (called the electrical leak imaging method) can be applied when the geomembrane is part of the landfill cap system, although it is then more difficult to detect defects. Hansen and Beck (2009), Beck (2011) showed that it depends on the nature and thickness of the soil. As these methods are much more effi-cient if they are carried out before the waste is sealed, they are not applicable to old closed landfills. That is why the monitoring of the integrity of the cover on these sites requires the use of non-invasive techniques. Geophysical methods represent very interest-ing tools for the initial characterization of covers in a cost effec-tive way. Subsequently, a drilling survey must be performed to discover if the anomalous areas detected by geophysics are genu-inely the result of damage to the cover.To date, geophysical methods have mainly been used to:• characterise contaminated plumes (Chambers et al. 2002;
Ogilvy et al. 2002; Naudet et al. 2004; Gallas et al. 2010; Naudet et al. 2014),
• define the nature of materials and waste in a site (Guérin et al. 2004; Leroux et al. 2007; Boudreault et al. 2010; Vaudelet et al. 2011; Ramalho et al. 2013),
• monitor leachate recirculation in bioreactors (Grellier et al. 2008; Clément et al. 2011).
However, to our knowledge, few published studies have used geophysical methods to characterise covers on landfills:• seismic refraction and electrical resistivity from an azimuthal
array to identify cracks in a clayey cover (Carpenter et al. 1991),
• ground penetrating radar in boreholes to detect preferential infiltration areas in a clayey cover (Cassiani et al. 2008).
Other studies have been carried out on experimental sites under more controlled conditions to test the feasibility of geophysical methods in the detection of cover heterogeneities:• ground penetrating radar and EM38 in a clayey cover
(Guyonnet et al. 2003),• Automatic Resistivity Profiling, the self-potential and electrical
resistivity tomography in two clayey covers, including one with a GCL (Genelle et al. 2011).
In this particular study we tested three geophysical methods on an old hazardous waste landfill located in France: Automatic Resistivity Profiling (ARP - Dabas 2009), the self-potential (SP) method, and electrical resistivity tomography (ERT). These methods have already been used on an experimental site which reproduced two kinds of cover with engineered defects that had been made intentionally (Genelle 2012). These methods were chosen as they are theoretically sensitive to several parameters such as lithology contrasts (clay content), water content, water flow, and compaction. Because of its layout and its continuous acquisition system, ARP was used to provide the apparent elec-trical resistivity distribution at three different depths over the entire landfill. SP and ERT methods were also used on selected
FIGURE 1
The original plans for the cover, put in place in 1992, on the hazardous
waste landfill in France.
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dipole and the electrical potential dipoles (0.5, 1.0 and 2.0 m) allows simultaneous acquisition of apparent electrical resistivity data on three channels. The ARP system includes a differential global positioning system and is towed by a quad bike for rapid data acquisition.
ARP has already been used in archaeological surveys (Papadopoulos et al. 2009; Campana and Dabas 2011), viticul-tural farming (Costantini et al. 2009; Ghinassi et al. 2010), and for the geology of an alluvial landscape (Tye et al. 2011). Because all the necessary data must be acquired as quickly as possible (for cost-effective reasons), ARP appears to be a good tool for landfill managers, hence we tested it on the landfill.
1. b) AcquisitionThe ARP measurements were performed in November 2009 over the entire landfill. Data acquisition was made along paral-lel survey lines with 1-m spacing over a six-hour period. With the quad bike at a speed of 10 km/h, measurements were taken every 0.1 m. The data are plotted as apparent resistivity maps for each channel, called ARP0.5m, ARP1.0m and ARP2.0m respec-tively. The data processing involved spline interpolation on a 1 m regular mesh.
SP2. a) MethodThe SP method consists of measuring the natural electrical potential at the ground surface. The SP signals are associated with different polarisation mechanisms that occur naturally in the ground (e.g., Jouniaux et al. 2009). The main sources are water fluxes in the vadose zone (Thony et al. 1997; Doussan et al. 2002; Linde et al. 2011), and/or in the saturated zone (Rizzo et al. 2004; Maineult et al. 2008; Bolève et al. 2009) through the electrokinetic coupling, diffusion potentials resulting from gradi-
into the site (Fig. 3). However, despite this cover and the wall, the volume of leachate drained away was greater than the volume of effective rainfall (confidential data). This abnormal amount of leachate inside the landfill was mainly observed after rainfall, which pointed to water infiltration.
Geophysical methodsARP1. a) MethodARP uses a patented multi-electrode device that is connected to wheel-based electrodes that roll over the ground surface (Dabas 2009). The rolling electrodes are arranged in a trapezoidal pat-tern. Electric current is injected into the ground by a pair of electrodes, and the resistance is measured by three other pairs of electrodes that act as electrical potential dipoles (Fig. 4). The different unit of electrodes spacing between the current injection
FIGURE 2
Photos of a 7-mm-thick undamaged GCL composed of a thin layer of
bentonite bonded to two layers of geotextiles (a) and a damaged GCL
with separation of bentonite from the geotextiles (b).
FIGURE 3
(a) Map of the landfill with topo-
graphic lines and the location of
the geophysical measurements.
(b) Topography along the ERT
profile (IJ in (a)), with the loca-
tions of the manual auger holes.
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pathways through cracks in the cover that facilitate surface water infiltration, leachate circulation inside the saturated waste body and diffusion and redox processes due to waste biodegradation, even though industrial waste has a lower biodegrability com-pared to domestic waste.
2. b) AcquisitionThe equipment used for SP measurements on the landfill included an MX20 Metrix voltmeter with a high input imped-ance (~108 Ω), one cable reel, and two non-polarisable Pb/PbCl2 electrodes (Petiau 2000). The SP survey was carried out in November 2009 just after the ARP measurements. The area chosen for the SP survey was characterised by a high electrical resistivity contrast on the slope regarding the ARP data (black rectangle on Fig. 5). The measurements were performed between a roving electrode and a fixed electrode at a base sta-tion. The measurement mesh was 5 m square, with the profiles oriented from West North-West to East South-East. To ensure uniform ground contact between the electrodes, small holes were dug at each station and filled with bentonite mud. As the top soil was grassy and contained no stones, the SP signal at each measurement point was stable, with a maximum variation estimated at ~1 mV over roughly one minute. During the map-ping, several SP measurements were made with the two elec-trodes placed in the base station hole to provide the linear drift correction for the data.
ERT3. a) MethodERT is based on the injection of electric current into the ground followed by the measurement of resulting electrical potential differences between a set of electrodes. This method has already been used to locate seepage and erosion in dams (Johansson and Dahlin 1996; Sjödhal et al. 2008), and to map landfill geometry (Reynolds and Taylor 1996; Bernstone et al. 2000). ERT has also been used to estimate soil water content (Michot et al. 2003; Schwartz et al. 2008; Brunet et al. 2010) and to characterise soil heterogeneity (Samouëlian et al. 2003; Besson et al. 2004) and its compaction (Abu-Hassanein et al. 1996).
3. b) AcquisitionAs it would have been time consuming to perform ERT measure-ments over the entire landfill cover, a specific zone covered by ARP was selected for the ERT profile (called IJ profile in Fig. 3). The measurements were performed in June 2010 using stainless steel electrodes (rods 30 cm long and 12 mm in diameter) con-nected to a Syscal Pro resistivimeter. The acquisition parameters were a 0.5 m unit of electrode spacing (ERT0.5m) and the dipole-dipole array. They were chosen according to measurements car-ried out earlier on the experimental site (Genelle 2012, Genelle et al. 2012). The measurements were implemented on a 154 m-long profile with a roll-along technique (24 electrodes overlap between each panel of 72 electrodes). Moreover, the
ents of the chemical potentials of ions and redox processes when a biogeobattery effect is present (Maineult et al. 2004; Naudet and Revil 2005, Revil et al. 2010).
The SP signal distribution can also be influenced by electrical resistivity contrasts in the subsurface. This implies that it is nec-essary to know the precise electrical resistivity distribution in order to accurately interpret the SP responses (Jardani and Revil 2009; Slob et al. 2010). As many different processes can contrib-ute to the measured response, the number of sources is the main limitation of the SP method.
Each of the aforementioned sources corresponds to different kinds of anomaly. Regarding electrokinetic potentials, positive anomalies are usually the signature of groundwater flow in the saturated zone while negative anomalies are associated with infiltration in the vadose zone. In the case of contaminant plumes downstream of the landfill, lab and field experiments have de monstrated that strong negative SP signals (|SP|> 100 mV) seem to be associated with a biogeobattery effect due to biode-gradation. In the absence of strong negative signals, anomalies are explained as diffusion potentials resulting from gradients of the chemical potential of the ions. When the location of water tables is known, it is possible to correct the self-potential data for groundwater flow leading to a residual SP signal that can there-fore be compared directly with biogeochemical data (Naudet et al. 2003, 2004, Revil et al. 2010).
We found few publications dealing with SP measurements directly performed over landfills. Ramalho et al. (2013) have associated their SP signal to groundwater movement and prefer-ential pathways inside the landfill due to the large leachate vo lume. Naudet et al. (2012) (and results unfortunately not yet published) have observed negative SP anomalies over more bio-degradable zones on domestic landfills.
In this particular study, we expect to detect fluids preferential
FIGURE 4
Electrode configuration used in the ARP system. C1, C2: current injection
electrodes, and P1, P2, P'1, P'2, P''1, P''2: potential electrodes.
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the GCL was pierced without any resistance, textile fibres and/or bentonite were seen in samples extracted from the auger (Fig. 2b). In this case, the GCL is defined as ‘very damaged’ as it is known to be very resistant mechanically when in pristine condition. This means that there is a local decrease in the physi-cal and mechanical properties of the GCL. On the other hand, when the GCL was not pierced, we assume that it was either undamaged or less damaged.
measurements were also performed using 1.0 m electrode spac-ing (ERT1.0m), without moving the electrodes (Fig. 6). This ena-bled us to obtain a greater investigation depth, namely 5.7 m compared to 2.3 m with ERT0.5m.
As we did not perform reciprocal measurements, we based our estimation of the data quality on the contact resistance and the “quality factor” (Peter-Borie et al. 2011). As the contact resistances measured just before the data acquisition were less than 1 kΩ and the “quality factor” less than 0.2 % we estimate that our data are reliable.
Data inversion was carried out with the RES2DINV software (Loke 2010), using the two-dimensional regularised least-squares optimisation method. The inversion was constrained by robust (L1-norm) model (Loke et al. 2003) combined with model refinement. We considered that the L1-norm would produce a more accurate resistivity model than L2-norm due to the high electrical resistivity contrasts on site.
Manual auger holesSixteen manual auger holes were bored along the ERT profile to improve our knowledge of the cover in this part of the landfill (Fig. 3). Their locations were chosen based on the variation of the interpreted electrical resistivity. The cover materials were described macroscopically. For each of them, a qualitative esti-mation of the rough ratio of sand and clay was carried out by experienced geologists. Moreover, the condition of GCL (wheth-er damaged or otherwise) was qualitatively assessed by the pe netration resistance of the manual auger. We noticed that when
FIGURE 5
The three apparent electrical resistivity maps obtained with ARP with 0.5 m unit of electrode spacing (a), 1.0 m unit of electrode spacing (b) and
2.0 m unit of electrode spacing (c). The SP measurement area is delimited by the black rectangle. The location of the leachate drains are given by the
white lines.
FIGURE 6
Electrode configuration for the dipole-dipole array for ERT measure-
ments. C1, C2, current injection electrodes, and P1, P2, potential elec-
trodes.
a n
ERT0.5m
0.5 1 to 10
1.0 1 to 10
ERT1.0m
0.5 0
1.0 1 to 10
2.0 1 to 6
3.0 1 to 7
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• zone B characterised by a rather stable signal (about 70% of the values are contained between –10 and +10 mV),
• and zone C, located at the top of the landfill where the signal shows a sharp decrease followed by an increase of the SP values.
From 90 to 10 m along SP profiles, the SP signal is slowly increasing. We tried to compare this signal with the topography in order to determine a possible correlation observed in the field, known as a topographic effect and due to water flow along slopes (Finizola et al. 2004; Lénat 2007). Even though the correlation between SP and topography was not obvious (not shown here) perhaps because many other sources are superposed, this small positive SP increase from zone B to zone A could be explained by water flow under the GCL following topography and an upwelling at the foot of the landfill (zone A). Moreover, we know, thanks to auger holes bored at 12, 18 and 29 m along the profile, that at the foot of the landfill materials just above the GCL are more saturated than elsewhere and that the GCL is less damaged in zone B.
At the top of the landfill (zone C), ERT and auger holes have revealed that the waste is closer to the surface than elsewhere along the profile, that the cover is not as thick in this zone and that the GCL is damaged. The negative SP signal observed in this zone can then be explained by preferential water infiltration and/or diffusion potentials resulting from gradients of the chemical potential of the ions or a biogeobattery. But, the hypothesis of biogeochemical sources seems unlikely as industrial waste is less biodegradable than domestic waste and the negative anomaly is not exactly centred over the more superficial waste. The best explanation seems to be an infiltration under the GCL in this zone. But it is quite difficult to determine the exact source or the contribution of each one and therefore to make a rigorous inter-
RESULTSAutomatic Resistivity Profiling measurementsThe three maps obtained from ARP0.5m, ARP1.0m and ARP2.0m
(Fig. 5) show apparent electrical resistivity variations between 10 and 200 Ωm. Each map can be divided into three areas:• zone 1, in the northern part of the landfill. Apparent electrical
resistivity (>70 Ωm) is high whatever the electrode spacing,• zone 2, in the southern part of the landfill. Apparent electrical
resistivity decreases as electrode spacing increases.• zone 3, pinpointed on the North and East boundaries of the
landfill. Apparent electrical resistivity is the lowest for each electrode spacing.
A few explanations can be offered at first sight without reference to additional information. The different electrical resistivity va lues in the three aforementioned zones could be due to differ-ent lithologies and/or variation in the water content of the cover materials. For example, the lower values of apparent electrical resistivity in zone 2 compared to those in zone 1 on the ARP2.0m could be associated with a higher clay content in the cover mate-rials. In zone 3, located at the foot of the slope, the low apparent electrical resistivity values could be explained by a high water content, possibly due to the rainfall accumulation before the survey. This zone is outside the waste cells and corresponds to the area between the leachate drains and the cut-off wall (Fig. 3).
Self-potential measurementsFigure 7 shows the results of the five SP profiles. The SP signal ranges from –81 to +42 mV with different dynamics delimited by three different zones:• zone A, at the foot of the landfill, where SP values are positive
and vary from +2 to +42 mV,
FIGURE 7
(a) The East South-East to West
North-West SP profiles. (b)
(Inset) The location of the SP
profiles (black lines) and the ERT
profile (red line, IJ).
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perhaps less damaged than elsewhere along the IJ profile.Secondly, in the other parts of the IJ profile there is no overall
increase in electrical resistivity at the depth of the GCL. At the location of the auger holes drilled in zones A and C (especially in the areas before 5 m and after 105 m) the GCL was present but pierced without any resistance. That means that the GCL is more damaged here than it is in zone B. Besides, the area located between 5 and 25 m is unusual: the electrical resistivity layout up to a depth of 2.3 m is not the same on ERT0.5m and ERT1.0m. The interpreted model resistivity ERT1.0m displays two cover layers while the interpreted model resistivity ERT0.5m shows three layers (Fig. 8b). The auger holes enable us to determine which of the two electrical resistivity layout (ERT0.5m and ERT1.0m) corres-ponds to the actual cover. The auger hole drilled at 12 m had revealed the presence of three layers (clayey sand, brown clay and GCL – Fig. 9) whereas the auger holes drilled at –15.5 m and 3.5 m (Genelle 2012) have shown only two layers (brown clay and GCL). Thanks to these auger holes, we can conclude that the interpreted model resistivity ERT0.5m corresponds to the actual cover.
Thirdly, zones A and C differ from zone B by the presence of low resistivity values (< 4 Ωm), which can be clearly seen on the interpreted resistivity model ERT1.0m (Fig. 8a). These values corres pond to the waste. Thus, we can conclude that in these areas, where the GCL is more damaged, we can see the waste, whereas in zone B, waste is not detected in the ERT1.0m. The same thing has been observed during the electrical resistivity monitoring on the experimental site (Sirieix et al. 2013): the underlying gravelly clay material is detected since the GCL becomes da maged (that is to say the electrical resistivity of the GCL decreases).
pretation of SP signals as no moisture content, leachate head or biogeochemical analyses have been performed on the site.
Even though the behaviour is similar along each profile, measurements along the profile y = 5 m show the most variations in the SP signal (until –81 mV). That is why we performed an ERT profile on the same location.
2D Electrical resistivity tomography measurementsThe two ERT profiles, ERT0.5m and ERT1.0m, highlight interpreted electrical resistivity variations between 0.5 and 400 Ωm, not only laterally, but also vertically (Fig. 8).
Firstly, it is clear that the two interpreted resistivity models show the same variations of electrical resistivity between 30 and 90 m (zone B) up to a depth of 2.3 m. In this zone, we can see a thick layer characterised by high interpreted electrical resistivity values (140 < ρ < 400 Ωm). The upper boundary of this layer is 1.1 m below the surface. As it corresponds to the depth of the GCL in the original plans for the cover (Fig. 1), this highly resistive layer must be the signature of the GCL. However, we know that the true thickness of the GCL is only 7 mm and not 3.9 m (Fig. 8a). To try to understand this appa-rently abnormal layer thickness, we performed measurements on the experimental site (Genelle et al. 2011) on a cover made of an undamaged and unsaturated GCL (7 mm thick). These revealed very high interpreted electrical resistivity (~4,000 Ωm), associated with an overestimated thickness (in excess of 1 m) on the resistivity model at the location of the GCL (Sirieix et al. 2013). As the electrical resistivity values on the landfill are lower than those on the experimental site, we can assume that the GCL is probably damaged on the landfill. In zone B it is
FIGURE 8
Interpreted model resistivity of
the ERT profile and location of
the manual auger holes. (a)
ERT1.0m: dipole-dipole array,
a = 1.0 m (iteration 5: absolute
error, 1.80%). (b) ERT0.5m: dipole-
dipole array, a = 0.5 m (iteration
5: absolute error, 0.94%).
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forward models which differ in the electrical resistivity value of the GCL (Table 1):• in the model M
b, ρ
GCL = 8400 Ωm, to reproduce the effect of an
unsaturated and undamaged GCL,• in the model M
c, ρ
GCL = 840 Ωm, to reproduce the effect of a
more saturated GCL. Moreover, a tear (0.125 m wide) was crea ted to simulate a defect.
In the forward model the GCL (Fig. 10a) has a thickness of 0.1 m because it cannot be modelled with its true thickness (7 mm). Applying the equivalence principle (Maillet 1947), we obtain a “true” electrical resistivity value of the GCL equal to 120,000 Ωm for the model M
b and 12,000 Ωm for the model M
c.
DISCUSSIONInfluence of the GCL on the interpreted resistivity modelTo explain the GCL’s electrical behaviour (see the 2D ERT results), we computed several forward models with RES2DMOD. We have reproduced the cover (lithology, thickness) found at the auger hole located at 12 m (Fig. 9). Its structure is similar to the original plans for the cover (Fig. 1). Here we only present two
FIGURE 9
(a) Vertical electrical resistivity at 12 m extracted from the ERT0.5m inter-
preted model resistivity. (b) Profile of the auger hole drilled at 12 m
along the ERT profile.
TABLE 1
Electrical resistivity and thickness of the cover material layers chosen for
the forward modeling.
Layer thickness (m)
Forward model
Mb
Forward model
Mc
0 – 0.6 Clay sand (102 Ωm)
0.6 – 1.1 Clay (33 Ωm)1.1 – 1.2 GCL (8400 Ωm)
Equivalent to 120,000 Ωmfor 7 mm thick
GCL (840 Ωm)Equivalent to 12,000 Ωm
for 7 mm thick1.2 – 1.6 Saturated clay sand (50 Ωm)
>1.6 Waste (4 Ωm)
FIGURE 10
(a) Forward model with bulk
electrical resistivity of each layer
(b) Interpreted model resistivity
of forward model Mb (unda maged
and unsaturated GCL); iteration
5: absolute error, 2.2%. (c)
Interpreted model resistivity of
forward model Mc (saturated
GCL and a 0.125-m-wide tear at
the vertical of point E); iteration
5: absolute error, 2.8%.
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and large higher than 80 Ωm from the beginning of the profile to 60 m, while they are lower than 60 Ωm in the second part of the profile. However, it is important to recognise that the same varia-tions in apparent electrical resistivity can be found on the ERT pseudosection, up to a depth of 1.1 m (Fig. 11b). In the deeper part of the pseudosection, apparent electrical resistivity values are lower than those on the ARP data. Moreover, at each end of the profile, ERT values are lower than 20 Ωm while this is not the case with the ARP2.0m.
To further explore the relationship between the ARP and ERT data, we computed the Pearson correlation coefficients. This provides a measure of the correlation between these two data sets. To do this, we extracted the apparent electrical resistivity (ARP and ERT) every 5 m between –15 and 125 m along the IJ profile. The Pearson correlation coefficients were calculated for 23 variables:• three for ARP: ARP0.5m, ARP1.0m and ARP2.0m (Fig. 4),• twenty for ERTL corresponding to the different distances L of
the dipole-dipole array (Fig. 6).The results are shown in the correlation matrix in Table 2. The higher the values, the closer the relationship is between the two variables. The highest correlations are shown in bold and under-lined in the table. We can conclude that:• ARP0.5m is linked to ERT1.5 and ERT2.0,
• ARP1.0m is linked to ERT2.0,
• ARP2.0m is linked to the ERT4.0 (r = 0.87; Table 2). This correla-tion coefficient corresponds to a pseudodepth of about 0.87 m (Edwards 1977).
It should be noticed that Gebbers et al. (2009) reached the same conclusion, using another statistical method (Kendall non-para-metric rank correlation; Saporta 1990).
According to the previous analysis, we can conclude that the ARP2.0m is not able to detect the GCL at a depth of 1.1 m. This method is mainly influenced by the cover materials close to the surface.
To compare the data from the two forward models with the on-site measurements, the same kind of inversion (i.e., robust model constraints combined with model refinement) was used. Moreover, a 3% random noise level was added on the apparent electrical resistivity values. As shown in Fig. 10, we can make a number of observations. The GCL has an overestimated thick-ness on both interpreted resistivity models but the models give different electrical resistivity values at the depth of the GCL:• in the model Mb, interpreted electrical resistivity values are
higher than 200 Ωm (Fig. 10b),• in the model Mc, interpreted electrical resistivity values are
about 140 Ωm. In this case, an underlying conductive layer is displayed at the bottom. Materials below the GCL are detected. Moreover, a decrease in resistivity (ρ ~80 Ωm) occurs at the location of the tear which was created by modelling (point E on Fig. 10c).
However, one should bear in mind that the electrical resistivity values in the interpreted models are those defined for a 0.1 m-thick GCL. Those for the true GCL (7 mm-thick) are higher.
To conclude, the interpreted model resistivity of the model Mc, obtained with the lowest electrical resistivity of the GCL and the presence of a defect, corresponds closely to that of the landfill.
Comparison of the geophysical methodsFirst of all we compare the ARP and ERT measurements and then the ARP, SP and ERT measurements along the IJ profile although they were not carried out simultaneously because of landfill access reasons. Only the ERT0.5m data will be used for the comparison because they have been defined as more reliable than the ERT1.0m data regarding the actual cover (see Results 2D ERT).
ARP and ERTFirstly, we can observe the same trend on the three ARP data sets, ARP0.5m, ARP1.0m and ARP2.0m (Fig. 11a). ARP values are by
FIGURE 11
(a) Apparent electrical resistivity
extracted from the ARP measure-
ments along the IJ profile. (b)
Apparent electrical resistivity
pseudosection of the ERT0.5m pro-
file (dipole-dipole array, a = 0.5
m).
F. Genelle et al.608
© 2014 European Association of Geoscientists & Engineers, Near Surface Geophysics, 2014, 12, 599-611
SP seems to be preferentially due to water infiltration as biogeo-chemical sources seem unlikely in the case of industrial waste.
CONCLUSIONIn this paper we have presented a geophysical characterization of landfill cover in France using ARP, SP and ERT methods on the site. The aim of this study was to determine the capacity of each method to detect defects in the cover. The cover, put on twenty years ago, was composed primarily of sand, clay and a GCL at a depth of 1.1 m. Damage was defined as lateral and vertical hete-rogeneities compared to the original plans for the structure, vari-ations in the thickness of the cover, tears in the GCL or its chemical ageing. Geophysical measurements of the cover were most sensitive to the GCL and the clay layer thickness.
The ARP highlighted lateral variations in the clayey and sandy materials above the GCL on the overall landfill. The heterogene-ous cover prevented the detection of deeper variations (the resis-tivity of the GCL and waste). ERT measurements made with 0.5 m electrode spacing detected fine lateral and vertical changes in the cover with higher accuracy and a greater investigation depth (2.3 and 5.7 m respectively, called ERT0.5m and ERT1.0m). Given what we know from the auger holes that were bored manu-ally, ERT0.5m more closely tallies with the actual cover.
The ERT0.5m also revealed high electrical resistivity variations at the level of the GCL. It was shown by numerical modelling that the decrease in interpreted electrical resistivity is linked to the water content of the GCL and/or the presence of possible defects (tears, chemical ageing, and separation of the textile fibres from the bentonite).
The SP measurements displayed a sharp negative SP anomaly at the top of the landfill where the cover is thinner and GCL da maged and a positive value at the foot of the landfill where water accumulates. This suggests an upwelling at the top and a downwelling under the GCL following topography. However, as
ARP, ERT and SPTo compare these three data sets more easily, we sampled ERT and ARP data every 5 m between 10 and 120 m along the IJ profile in order to have the same spacing for each data set. In Fig. 12a, we plotted:• the apparent electrical resistivity values of ARP2.0m,• the interpreted electrical resistivity of the ERT0.5m between 1.4
and 2.0 m (depths that best characterised the GCL – Fig. 9),• the SP data of the y = 5 m profile.Different behaviours can be detected along the IJ profile in rela-tion to zones A, B and C. Zone A is characterized by rather stable interpreted electrical resistivity values with values below 140 Ωm (Fig. 12a), while the SP signal decreases from +42 to +2 mV with topography. Auger holes in this zone have shown that the material above the GCL corresponds to the cover as it was origi-nally intended when it was put in place in 1992 (Fig. 2), but that the brown clay just above the GCL is particularly wet suggesting water accumulation in this zone at the foot of the landfill. This could explain the positive SP signal measured in this area.
In zone B, the SP signal is relatively stable with a slight increase from –10 to +10 mV and the ERT data vary from 80 to 400 Ωm. The ARP data show a similar but less pronounced variation. The two auger holes in this zone have shown that the GCL is less da maged than elsewhere along the profile, explaining the high resistive values measured. This could also explain the slight SP increase if the GCL screens SP signal coming from leachate flow above.
In zone C, the SP signal drastically decreases to –70 mV as well as the ERT data, especially for those between 1.7 to 2.0 m in depth (values <10 Ωm). The same trend can be seen on the ARP data but only from 110 m, and the trend is weaker. Two things can explain this decrease: the thinner cover and the dam-aged GCL (as indicated by the manual auger holes). Thus, it could be assumed that the decrease in electrical resistivity is due to the waste being closer to the surface and that the decrease in
TABLE 2
Correlation matrix computed between the apparent electrical resistivity obtained with ARP and ERT. Bold and underlined numbers indicate the highest
correlations between ARP0.5 m, ARP1.0m and ARP2.0 m and ERT.
Variable N° variable; variable1 2 3 4 5 6 7 8 9-21 22 23
ARP (0.5)
ARP (1.0)
ARP (2.0)
ERT (1.5)
ERT (2.0)
ERT (2.5)
ERT (3.5)
ERT (4.0)
…ERT (9.0)
ERT (9.5)
ARP (0.5) 1.00 0.98 0.74 0.84 0.84 0.76 0.67 0.57 … … -0.04 -0.04
ARP (1.0) 0.98 1.00 0.80 0.79 0.80 0.75 0.72 0.65 … … 0.06 0.07
ARP (2.0) 0.74 0.80 1.00 0.49 0.52 0.55 0.81 0.87 … … 0.52 0.53
ERT (1.5) 0.84 0.79 0.49 1.00 0.92 0.75 0.46 0.40 … … -0.20 -0.20
ERT (2.0) 0.84 0.80 0.52 0.92 1.00 0.86 … … … … -0.26 -0.26
ERT (2.5) 0.76 0.75 0.55 0.75 0.86 1.00 … … … … -0.25 -0.25
…… … … … … … … … … … 0.02 0.01
… … … … … … … … … … 0.99 0.98
ERT (9.0) -0.04 0.06 0.52 -0.20 -0.26 -0.25 … … … … 1.00 0.99
ERT (9.5) -0.04 0.07 0.53 -0.20 -0.26 -0.25 … … … … 0.99 1.00
Detection of landfill cover damage using geophysical methods 609
© 2014 European Association of Geoscientists & Engineers, Near Surface Geophysics, 2014, 12, 599-611
ASTM Standard. D6747. 2004. Standard Guide for Selection of Techniques for Electrical Detection of Potential Leak Paths in Geomembranes.
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many different processes can contribute to the measured SP response, without biogeochemical and hydrogeological knowl-edge of the landfill, in this instance the SP method is not really suited to the cover characterisation.
Even though the 2D ERT gave good results, we propose that future developments (3D survey) should be more relevant to take into account the 3D effects of the cover heterogeneities. However, surveys utilising this method will be too time consu-ming given the large area, so ARP with longer arrays (>2.0 m) would be a better solution. Consequently, the addition of poten-tial dipoles with several electrode spacings to the present con-figuration of the ARP would allow the acquisition of data deeper down, data that should be inverted, thus providing a more relia-ble result. However, to date no device capable of doing this is available. So, assuming that the cover materials above the GCL are homogeneous, the apparent electrical resistivity of the ARP should increase as electrode spacing increases. Thus, areas where this is not the case could indicate that the GCL is da maged. In the latter case, we would suggest carrying out ERT measurements with 0.5 m electrode spacing. But in cases where the materials above the GCL are not homogeneous, it remains an open question as to whether a survey of the entire landfill is required given the time and cost involved.
Finally, both ARP and ERT geophysical methods can be re commended to landfill managers with a view to optimising any eventual remediation of the cover.
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FIGURE 12
Comparison of the three geoelec-
trical methods. (a) Profiles sho-
wing the apparent electrical resis-
tivity extracted from the ARP2.0 m,
the interpreted electrical resistiv-
ity extracted from the ERT0.5 m
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resistivity ERT0.5 m of the IJ pro-
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