an integrated geophysical investigation of the hydrogeology … · although interpretation was...

An integrated geophysical investigation of the hydrogeology of an

anisotropic unconfined aquifer

S.K. Sandberga,*, L.D. Slaterb,1, R. Versteegc,2

aDepartment of Geosciences, University of Southern Maine, 37 College Avenue, Gorham, ME 04038, USAbDepartment of Geosciences, University of Missouri—Kansas City, 5100 Rockhill Road, Kansas City, MO 64110, USA

cLamont Doherty Earth Observatory, Route 9W, Palisades, NY 10964-8000, USA

Abstract

The predictive capability of groundwater flow models is frequently restricted by insufficient characterisation of a typically

heterogeneous and anisotropic subsurface. Trace levels of volatile organic compounds have been detected at municipal water

supply wells in Gray, Maine. Groundwater flow modelling based on available hydrogeologic data defines a dominant W–E

transport vector that is inconsistent with the apparent N40E transport of a plume emanating from a road salt storage facility, as

mapped with a terrain conductivity instrument.

A local-scale geophysical study at an undisturbed site in the glacial-marine delta deposit aquifer was conducted to investigate

the possible influence of anisotropy on flow through these unconsolidated sediments. Ground penetrating radar and terrain

conductivity measurements reveal evidence for structural features that are likely to promote preferential transport paths

orientated in a general NE–SW direction. Two conductive tracers, one deep and one surficial, were injected and monitored for

direct evaluation of the groundwater flow vector using resistivity and self potential methods. Although interpretation was

limited by an incomplete resistivity dataset, the results and modelling of both methods supports a general N30E–N40E flow

vector. Furthermore, consistent flow velocity estimates (,0.15 m/day) are obtained from the two methods. Analysis of this

integrated dataset suggests that anisotropy exerts a significant control on flow in this unconsolidated aquifer. Predictions of

sources of groundwater contamination at municipal wells based on flow models not accounting for this anisotropy will likely be

in error. This study illustrates the potential value of an integrated geophysical study, which will aid the development of accurate

flow models for unconsolidated aquifers. q 2002 Elsevier Science B.V. All rights reserved.

Keywords: Hydrogeology; Anisotropy; Electrical resistivity; Self potential methods

1. Introduction

A complex problem in hydrology is the determi-

nation of fluid transport behaviour such as flow

pathways, flow velocity and hydraulic conductivity in

a heterogeneous and anisotropic subsurface. Infor-

mation on fluid flow behaviour (both present and

predicted) is needed to assess the contamination impact

of spills and plumes and the planning and assessment of

remediation efforts. For example, in the state of Maine

residential groundwater supply wells are particularly

vulnerable to groundwater contamination. Geophysical

methods may provide a relatively low-cost approach to

hydrogeologic characterisation. Numerous papers

0022-1694/02/$ - see front matter q 2002 Elsevier Science B.V. All rights reserved.

PII: S0 02 2 -1 69 4 (0 2) 00 1 53 -1

Journal of Hydrology 267 (2002) 227–243

www.elsevier.com/locate/jhydrol

1 Fax: þ1-816-235-5535.2 Fax: þ1-845-365-8150.

* Corresponding author. Present address: Department of Geology,

University of South Florida, 4202 East Fowler Avenue, SCA 528,

Tampa, FL 33620, USA. Fax: þ1-813-974-2654.

E-mail addresses: [email protected]

(S.K. Sandberg), [email protected] (L.D. Slater),

[email protected] (R. Versteeg).

http://www.elsevier.com/locate/jhydrol

illustrate the utility of geophysical methods in defining

subsurface heterogeneity in aquifer properties (Kelly

and Mares, 1993). Other methods, notably azimuthal

resistivity (Ritzi and Andolsek, 1992), or electromag-

netic azimuthal resistivity (Slater et al., 1998) provide

information on aquifer anisotropy.

Geophysical tracer tests can provide additional

information on flow directions and permit estimations

of groundwater flow velocity at a scale defined by the

experimental configuration. Forced-gradient tests

involve pumping to induce an artificial flow field in

the system. A natural-gradient tracer test may yield

more significant results since the tracer should move

through the system in a similar manner to the

undisturbed groundwater. This test permits estimation

of the natural groundwater flow vector. Most previous

work involves monitoring of conductive tracer tests

using variations of the electrical resistivity method.

White (1988) described an effective study using a

surface resistivity array in a radial pattern away from the

injection well. White (1994) compared the effectiveness

of six surface resistivity arrays for tracer detection.

Other workers employ a modified mise-a-la-masse

method, incorporating an electrode placed within a

borehole (Bevc and Morrison, 1991; Osiensky, 1997).

This paper presents results of an integrated geophy-

sical investigation of the hydrogeology of an unconso-

lidated, unconfined aquifer system in the municipality

of Gray, Maine. Multiple geophysical methods were

used to investigate aquifer heterogeneity and aniso-

tropy. A localised geophysical tracer test was conducted

to estimate the groundwater flow vector. The paper first

introduces the regional hydrogeology of the aquifer and

further details the motivation for the study. The

geophysical characterisation of the hydrogeological

framework is then described and followed by the

geophysical tracer test. The likely significance of the

geophysical results to regional groundwater flow is

discussed, given recognised limitations of the geophy-

sical study. Finally, implications for groundwater

contamination of residential water supply wells in the

surrounding area are considered.

2. Study site and regional hydrogeology

The field site investigated in this study is a glacial-

marine delta deposit comprised of fine- to coarse-

grained sand in the municipality of Gray, Maine. Fig.

1 presents a map of the Gray project area. Key

elements in this study are a landfill, sand/salt storage

pile and municipal water supply wells. In recent years

trace levels of volatile organic contaminants have

been detected in monitoring wells between the landfill

and the municipal water supply wells. The landfill is

approximately 900 m from the wellfield. Hydrogeo-

logical studies have been undertaken over the course

of several years to determine whether the landfill is

the source of this contamination.

A 3D numerical flow model was constructed for

this area and designed to simulate head and flow

patterns in the vicinity of the Gray Water District

springs and the Gray landfill. Observations of head

and estimates of hydraulic conductivity from slug

tests (selected wells only) were obtained from

numerous wells in the study area and used to constrain

the flow model (C. Fitts, University of Southern

Maine, pers. comm., 1998). The model extended

horizontally to known surface water boundaries in

some areas and to mapped margins of the glacio-

fluvial sand aquifer in other areas. A five-layer model

was constructed consistent with observations of

sediment stratigraphy and bedrock surface topogra-

phy. This model indicates that the regional flow is

from west to east between the landfill and municipal

supply wells (Fig. 1). This result suggests that the

landfill is a likely source of groundwater contami-

nation observed in the supply wells. However, model

uncertainty arises close to the landfill due to boundary

condition definition, adding to uncertainty regarding

whether flow from the landfill is captured by the Gray

Water District wells.

Also within the study area, an EM34 terrain

conductivity survey was conducted in the vicinity of a

Health Clinic and an uncovered sand salt storage pile

nearby (Fig. 1). Electromagnetic data were collected in

the horizontal coplanar (vertical dipole) configuration

with a 10 m coil separation. Fig. 2 shows the terrain

conductivity contour map from that study. High terrain

conductivity values trend N40E, between the salt pile

and the health clinic, indicating the direction of

groundwater flow in that area. This geophysical estimate

of groundwater flow direction conflicts with the west–

east flow vector identified in the hydrogeological

modelling. This result casts doubt upon the validity of

the flow model and the interpretation that the landfill is

S.K. Sandberg et al. / Journal of Hydrology 267 (2002) 227–243228

the site of groundwater contamination at the Gray

municipal supply wells.

An integrated geophysical study of this aquifer was

conducted to further investigate this discrepancy. A

sandpit between the landfill and municipal supply wells

was identified as a suitable undisturbed environment for

geophysical study (Fig. 1). The study had two primary

aims: first to identify possible evidence for anisotropy in

the aquifer material that could cause the flow vector to

deviate from that predicted by groundwater flow

modelling; secondly to directly determine the ground-

water flow vector using a geophysical tracer test.

3. Geophysical site characterisation

The geophysical grid established in the sandpit

consisted of 26 parallel lines placed 2 m apart, each

Fig. 1. Site map showing the locations of the water supply wells, the landfill, the sand/salt storage pile, and the sand pit where the tracer study

occurred. The regional hydraulic gradient as determined from hydrogeological modelling is approximately west to east.

S.K. Sandberg et al. / Journal of Hydrology 267 (2002) 227–243 229

40 m in length, along a centreline oriented N40E

through existing monitoring well MW-201 (Fig. 3).

The long-axis of the grid is thus oriented consistent

with the trend of the terrain conductivity plume

migrating from the sand/salt storage pile (Fig. 2). Also

shown in Fig. 3 is a fan of resistivity radial array data-

collection lines (108 increments) used for tracer

monitoring, passing through MW-20l with a centre-

line orientation of N40E. Three shallow well points

were used to determine the water table gradient within

the sandpit, which was calculated as N60E. In the

absence of horizontal anisotropy, the horizontal

component of shallow groundwater flow would be

in this direction.

Resistivity imaging, terrain conductivity and

ground penetrating radar (GPR) were used to

investigate lithologic and stratigraphic variability at

the study site. Lithologic constraints were provided

from well cuttings recorded for MW-201. The 2D

electrical structure was investigated using dipole–

dipole resistivity measurements made with a 2 m

electrode spacing and a maximum dipole spacing (n )

of six. A resistivity model was obtained using an

algorithm combining finite element forward model-

Fig. 2. Terrain conductivity contour map of sand/salt storage pile area. Values are in mS/m, and station locations are shown as þ symbols. Data

were obtained with a Geonics EM34 system, using a 10 m coil separation in the horizontal coplanar (vertical dipole) orientation.


ling with regularised, weighted, least-squares iterative

inversion, as described in LaBrecque et al. (1996).

The model along Line 11 (Fig. 3) is shown in Fig. 4.

The lithologic interpretation from well cuttings at

MW-201 is shown for comparison. The resistivity

image depicts a two layer structure, with an upper

resistive layer thinning to the southeast. The boundary

correlates with the distinct interface between brown

coarse sand and underlying gray fine sand identified

from well cuttings. Note that the resistivity inversion

provides a smooth model of subsurface resistivity,

such that the boundary is not uniquely defined.

Terrain conductivity measurements were con-

ducted to investigate lateral heterogeneity within the

sandpit. EM31 data were collected in the horizontal

coplanar (vertical dipole) configuration, using a 4 m

line spacing and a 1 m station spacing. A contour plot

of terrain conductivity across the grid is shown in Fig.

5. These data show a general increase in terrain

conductivity toward the south. The data are consistent

with thinning of the high-resistivity surficial layer

(presumably coarser-grained sediments) toward the

southeast, as already discussed on Line 11 for the

resistivity image in Fig. 4. One explanation relates to

the regional model of glacial outwash known to

originate from the north and flow generally to the

south, with deposition of progressively finer grain

sizes. Such a model would presumably yield higher

conductivity values towards the distal (southern) end

of the deposit. Given this, Fig. 5 may indicate a plan

view of thinning toward the southeast. Preferential

groundwater flow (induced by such macro-aniso-

Fig. 3. Plan of the geophysical grid used in the tracer test. Also shown is well MW-201. The orientation of the axis of the grid is N40E. For

reference, the water table gradient based upon three wellpoints is oriented N60E. Key lines used in data interpretation are highlighted.


Fig. 4. Two dimensional dipole–dipole resistivity model (Line 11) determined using smoothness-constrained inversion. Correlation with a

primary boundary determined from well cuttings obtained at MW-201 shown for comparison.

Fig. 5. Terrain conductivity map over geophysical grid obtained using a Geonics EM31 instrument in the vertical dipole orientation. Station

locations are shown as þ symbols.


tropy) may be generally toward the north–northeast,

where the coarser-grained sediment thickness

increases. However, other factors (e.g. variability in

fluid chemistry) could be responsible for the varia-

bility observed on this local-scale grid.

GPR has been used extensively to define the

hydrogeological framework of sites e.g. (Beres and

Haeni, 1991; Bridge et al., 1995). A pseudo 3D survey

was collected at the Gray site (consisting of tightly

spaced profiles of single offset 2D lines) using an

unshielded Ramac system. Data were collected using

both 100 and 200 MHz antennas. Velocity structure

was determined from common midpoint surveys

conducted at the site. Full results of this study are

reported in Versteeg and Birken (1998). Depositional

structures possibly promoting anisotropy within the

delta deposit were the primary target. Representative

cuts out of the 3D datasets are shown in Fig. 6(a) and

(b). Note that the main reflectors along Line 6 are

parallel (Fig. 6(a)), while there is a significant dip in

the reflectors in the line run perpendicular i.e. SW–

NE (Fig. 6(b)). These dipping reflectors are con-

sidered evidence of depositional foreset structures

(formed during delta deposition), probably dipping

towards the south as a result of general N–S glacial

transport (Fig. 6(b)). These imaged structures are

considered further evidence that macro-anisotropy

may be significantly influencing flow in the study

area. Given this, a tracer experiment was designed to

further investigate the relationship between the water

table vector and the groundwater flow vector at this

site.

4. Geophysical tracer test

Multiple geophysical methods are applicable for

detecting conductive tracers. In this study, the initial

intention was to concentrate on EM and GPR

methods. Consequently, pre-injection datasets were

obtained for these methods. Measurements obtained

immediately after tracer injection cast doubt on the

ability of the GPR and EM methods to resolve the

tracer in this environment. Consequently, resistivity

(radial array and modified pole–pole using a down-

hole electrode) and self potential (SP) surveys were

designed and implemented shortly after tracer injec-

tion. Interpretation is hence complicated by the

absence of pre-injection measurements. Although

this limits the dataset, the analysis described here

shows that information on tracer movement is still

resolvable. The study also serves to test the value of

geophysical methods in real world environments

when a pre-injection dataset is not available but

information on the flow vector is required. This is

more often than not the case when investigating

contaminant plumes migrating from old landfills and

industrial facilities.

Resistivity methods are appropriate for monitoring

tracer transport as the bulk resistivity of the earth is a

function of the fluid resistivity (the inverse of fluid

conductivity). In environments where matrix conduc-

tion is insignificant, fluid resistivity and bulk resis-

tivity are directly proportional and scaled by the

formation factor. Consequently, the replacement of in

situ fluid by conductive tracer is identifiable from

resistivity measurements. Numerous studies illustrate

the general applicability of the resistivity method for

detecting tracer transport (see for example, White,

1988, 1994). The SP method measures natural

electrical voltage potentials in the earth that arise

from small amounts of electrical current flow

associated with localised subsurface chemical,

hydraulic or heat gradients (Telford et al., 1990;

Sprunt et al., 1994). The concept of tracer transport

detection using SP is based upon the electrochemical

potential: if the concentration of electrolytes in the

ground varies locally, potential differences are set up

due to the difference in mobility of anions and cations

in solutions of different concentrations. Mapping of

this electrochemical potential is not extensively

recorded in the literature. One example is the

detection of a strong SP signal caused by the ionic

imbalance in concentrations at the boundary of a

landfill (Coleman, 1991). In the study reported here,

the electrochemical potential resulting from ionic

concentration gradients between tracer and in situ

water is used to identify tracer movement.

The choice of tracer concentration is an important

parameter in experiment design. A high salt concen-

tration optimises the success of geophysical detection.

However, this may also promote density driven flow

that is not typically representative of the flow regime

for in situ ground water. In this study, a relatively low

tracer concentration was used to prevent significant

unwanted density effects. In order to investigate


possible deviation in the groundwater flow vector

with depth, two tracers were introduced at

different depths in the aquifer. First, 0.66 m3 of

water (conductivity 48 mS/cm) was extracted from

MW-201, mixed with 2.27 kg of ordinary table

salt, and re-injected (conductivity 7.1 mS/cm).

MW-201 is a 6.4 m deep 10 cm PVC-cased well,

with PVC slotted screen from 3.4 to 6.4 m depth

(Fig. 3). Second, a surficial tracer was introduced

on Line 5 of the geophysical grid, 10 m northwest

of MW-201. Table salt was placed and mixed in

standing water in the bottom of an excavated

0.5 m deep pit, and then buried. The water

conductivity before adding salt was 22 mS/cm,

and after adding salt, 35 mS/cm. The two tracers

were placed significantly far apart to permit

individual resolution in the geophysical data.

4.1. Radial array resistivity

In order to identify migration of the borehole

tracer, radial array resistivity data were collected on

radii 108 apart and centred on MW-201 (Fig. 3). This

electrode geometry conforms to a modified pole–

dipole array, with a current electrode at 4.6 m in the

well, a 5 m potential electrode a-spacing (dipole

length) and N (number of dipole distances away from

the well) ranging from 1 to 5. The lack of a pre-

Fig. 6. Representative radar images obtained along, and perpendicular to, the geophysical grid lines. (a) Line 6 radar result showing correlation

with lithologic log at MW-201. Note that the reflectors are primarily horizontal in this NW–SE direction. (b) Radar result run perpendicular to

geophysical grid lines (i.e. NE–SW). Note the dip in the reflectors. See Fig. 3 for line orientations and location.


injection dataset complicates interpretation of tracer

transport direction. However, the transport direction is

discernible from analysis of the post injection

datasets. Apparent resistivities were calculated based

on the measured resistance and the known geometric

conversion factor for the array. Consider first the plan

view of apparent resistivity for azimuthal angles

northeast of MW-201 on August 13 (Fig. 7(a)). In the

absence of significant subsurface heterogeneity, the

contours of apparent resistivity would be radially

symmetrical about the current electrode in the well. A

significant deviation from symmetry is observed and

highlighted by the boxed area in Fig. 7(a). The result

indicates a heterogeneity that is most developed along

the N30E radius. Apparent resistivity at N ¼ 3 as a

function of azimuth is expanded in Fig. 7(b). A wide

zone of high apparent resistivity with a slight decrease

in the middle of the zone, centred at N30E is flanked

on either side by lower apparent resistivity, which is

the expected pattern for a conductive subsurface body.

Current flow lines are drawn into the conductor,

causing perpendicular isopotential lines to converge

on either side of the conductive region. This causes an

increased number of isopotentials per radial distance,

resulting in a measured higher voltage, and a higher

apparent resistivity calculation on either side of the

conductor.

Analysis of temporal changes in the radial array

dataset suggests that this conductor is indeed the

tracer, rather than some other subsurface heterogen-

eity at the site. To illustrate, apparent resistivity as a

function of N separation along N30E is shown for data

collected on August 8, August 13, September 20, and

November 8. The general trend from high values at

small N separation to lower values at larger N

separation reflects the increasing penetration depth

obtained with increasing N separation. Data at low N

separation are more influenced by the upper resistive

layer (Fig. 7(c)). Temporal changes in the apparent

resistivity profile are evident in Fig. 7(c). Apparent

resistivity increases with time are indicative of a

conductive slug moving away from the well and away

from potential electrodes. This effect is most obvious

in the N ¼ 1 data. Apparent resistivity at N ¼ 2 for

August 8 and 13 are near identical, indicating that the

tracer had not migrated far enough to influence the

data at this time. However, recovery to higher

apparent resistivity at N ¼ 2 for September 20 and

November 8 is indicative of tracer moving away from

these electrodes between these times. Temporal shifts

in apparent resistivity are also observed along other

azimuths for N ¼ 1 data. However, the apparent

resistivity recovery is slower over time for the N30E

orientation than that for all other azimuths (i.e.

measured apparent resistivity is perturbed for a longer

time on the electrodes orientated N30E). This further

indicates that the elongation and transport of the

saltwater slug centred on this azimuth.

4.2. Self potential

Self potential (SP) data were collected on the

geophysical grid, at approximately 5–9 day intervals,

commencing 3 days after tracer introduction.

Measurements were obtained using Cu/CuSO4 non-

polarizable electrodes, and a high-impedance digital

voltmeter. Data were collected using a fixed reference

electrode, and a roving electrode at stations on the

geophysical grid. The reference electrode was located

east–northeast of MW-201, as shown in Fig. 3. Base

station readings near this reference electrode were

reoccupied in order to account for thermal drift in the

signal.

Clearly detectable SP anomalies are associated

with tracer addition and subsequent transport. SP data

obtained on Line 6 (Fig. 3) are shown in Fig. 8(a).

These data illustrate the development of a low SP

anomaly at 210 m, consistent with the surface

injection location. The sharp anomaly in the August

7 dataset increases in amplitude and expands laterally

in the August 16 dataset, then narrows with decreased

amplitude in the August 21 dataset. An SP anomaly

near the well, at Station 0, appears offset toward the

northeast. The process of borehole injection may have

forced tracer up-gradient and in preferential direc-

tions, in part controlled by local heterogeneity near

the well screen. Consequently, the interpretation of

borehole tracer movement is based on Line 8 away

from the well and these localised complicating effects.

SP data obtained on Line 8 are shown in Fig. 8(b).

Since Line 8 is 6 m down gradient from the tracer

injection points, the August 2 dataset was used as a

background reference. The assumption is that the

tracers have not influenced data on Line 8 by this time,

and the August 2 data are indeed background values.

Natural voltage variability exists throughout the


survey area, as evidenced by noisy raw voltage

readings as shown in Fig. 8(a) for Line 6. A direct

subtraction of background voltages (in this case, data

from August 2) accounts for this variability. In

addition, a three-point running average filter was

applied to these data, further reducing noisy

responses. Lastly, base station readings obtained

near the reference electrode, were used to level

datasets from different days. The data in Fig. 8(b)

appears smoother than that in Fig. 8(a) due to this

processing.

For data from Line 8, the temporal variation,

indicating movement, is evident for both surficial and

well tracers, shown at Stations 29.5, and 0,

respectively. These data, collected 6 m down grid

from tracer introduction (Line 8 in Fig. 3), clearly

show that tracer migration is almost directly down the

axis of the geophysical grid, at N40E, as any other

flow direction would have significantly shifted the

anomaly centres from Stations 210 and 0.

The SP signal at 210 m on Line 6 is plotted as a

function of sample day in Fig. 9. By assuming that the

peak anomaly correlates with the condition of

maximum tracer concentration at this point, it is

possible to derive a simple estimate for a 1D seepage

velocity. A smooth curve was fit to the sparse SP

readings in Fig. 9, allowing an estimate of the time of

peak concentration (tpeak) on Line 6, 2 m down grid

from the surficial injection point (Fig. 9). Using

tpeak < 13 days, the estimated flow velocity is 2 m/13

days < 0.15 m/day. A similar calculation for the SP

data on Line 8 (Fig. 8(b)) for the well tracer (Station

0) yields a flow velocity of 0.17 m/day.

4.3. Resistivity modelling

Modified pole–pole data were obtained 3 and 10

days after tracer injection. One current electrode was

placed at 4.6 m in MW-201, with the other at 35.1 m

on Line 30. Voltages on the grid were measured

relative to a reference electrode at 12 m on Line 15.

Three-dimensional finite-difference modelling of this

dataset was attempted using MODFLOW (McDonald

and Harbaugh, 1988). As the governing equations of

electrical flow and groundwater flow are directly

analogous, it is possible to substitute electrical

current, electrical potential and electrical conduc-

tivity, for discharge, hydraulic head and hydraulic

conductivity (Jansen and Taylor, 1995). Examples of

the use of MODFLOW for 3D modelling of resistivity

data include Osiensky and Williams (1996) and

Osiensky (1997).

The 2D resistivity model obtained from inversion

of the dipole–dipole dataset (Fig. 4) was used to

define a pre-injection MODFLOW model. This model

implies a constant resistivity structure in the N40E–

S40W direction. Terrain conductivity data (Fig. 5)

indicate variation across the grid, although variability

is minimal within the region bounded by the tracer

injection and Lines 6 and 8. It was necessary to define

sharp model boundaries from the smooth inverse

model. The lithologic log from MW-201 was used to

constrain these boundaries. The major boundary is at

3.1 m, corresponding to the interface between med-

ium coarse sand and underlying silty sand. These

layers were assigned resistivities of 1000 and

525 V m, respectively, based on 1D and 2D resistivity

modelling results. To optimise the fit of data away

from the injection site, it was necessary to include an

upper 0.2 m resistive layer (4000 V m likely to

correspond with unsaturated sand). Although this

layer is not evident in Fig. 4, it was resolved with a

Schlumberger sounding (utilising smaller electrode

spacings) at the site.

The primary objective was to obtain a second

geophysical estimate of transport velocity for com-

parison with that obtained using the SP method. This

was achieved by modelling resistivity perturbations

caused by tracer addition in terms of tracer location at

3 and 10 days after tracer injection. This modelling

focuses on the surface tracer, as the SP data provide

evidence for complexity in the geometry of the

borehole-applied tracer at early times after injection

(discussed in the previous section). The fit of the

Fig. 7. Resistivity radial array results showing: (a) apparent resistivity contour map where values are plotted at the midpoint of the receiver

dipole. The dashed box indicates anomalous apparent resistivity, signified by non-radial symmetry. (b) An expansion of N ¼ 3 apparent

resistivity plotted versus azimuthal angle. (c) Apparent resistivity as a function of N separation showing variation over time for an azimuthal

angle of N40E.


dataset to the estimated model 3 days after injection is

shown in Fig. 10. Data between 220 and 25 m were

excluded as the model overestimates the measured

apparent resistivity, probably due to the thinning of

the upper resistive layer in this direction (Fig. 4).

Otherwise, the model provides a close fit to the

measured data. Perturbations of measured apparent

resistivity from the model curve around 210 m and

0 m are considered the response of the injected

tracers. The modelling exercise focuses on the larger

perturbation associated with the near-surface

injection.

In order to model the tracer at 3 days after

injection, a conductive body was added to the

model. Thirty model perturbations (varying the

location, dimensions and resistivity of the body)

were analysed. The range of models was constrained

by consideration of the known initial tracer conduc-

tivity, tracer volume and estimated formation factor

(0.35). As a consequence of the low tracer concen-

tration, density effects were avoided and therefore not

considered in the modelling. The best fit to the

measured data, as shown in Fig. 11(a), was obtained

for Model B in Fig. 11(b). Although the fit is not

perfect, the shape of the model curve matches the

shape of the field data, supporting the presence of a

small volume of tracer. To enhance the perturbations,

Fig. 11(b) also shows the difference between (1)

measured data and the initial model, and (2) model B

and the initial model. Data on Line 8 (6 m north of the

injection point) show no significant deviation from the

initial model, indicating that, at this time, tracer had

not migrated far enough for detection.

Data collected 10 days after injection show a

suppressed perturbation (relative to 3 days after

injection) on Line 6 at 210 m and tentative evidence

of a response on Line 8 (Fig. 11(b)). This response is

consistent with tracer displacement along N40E.

Thirty model perturbations were again analysed,

again constrained by the N30E–N40E flow direction.

The best fit to the measured data, as shown in Fig.

11(a), was obtained for Model C in Fig. 11(b). The

differences between (1) measured data and the initial

model, and (2) model B and the initial model, are also

shown. Again, the fit to Line 6 is imperfect but

consistent with further tracer movement N40E. The

Fig. 8. Self potential voltage versus distance along Lines 6 and 8. (a) Line 6 voltage data points are plotted without smoothing. (b) SP data from

Line 8 are plotted after smoothing (three-point running average filter), levelling (using base station readings), and the 2 August data has been

subtracted (assuming that August 2 is background for this Line).

Fig. 9. Self potential voltage versus days after injection for Line 6 Station 210 m.


subtle response on Line 8 is not resolved in the model.

A simple estimation of tracer seepage velocity was

obtained by calculating the modelled displacement in

the centre of mass of the tracer between the two

datasets (Fig. 11(b)). The result is a seepage velocity

of 0.14 m/day in a direction N40E, consistent with the

calculation from the SP data. There is considerable

uncertainty in this calculation as the simple model

does not completely reproduce the field data. In

addition, the tracer is likely to have a diffuse interface,

rather than the sharp interface modelled here.

However, the agreement with the SP calculation

supports the geophysical estimate of seepage velocity.

At the very least, the SP and resistivity calculations

are likely to provide a credible order of magnitude

estimate of seepage velocity at this site.

5. Discussion

The geophysical results described here indicate

that groundwater flow within the study area is not well

described by existing models based on available

hydrogeological data. The geophysical data and

interpretation support the argument that anisotropy

within the delta deposit exerts a significant control on

flow, such that the resultant flow vector does not

parallel the resultant hydraulic gradient vector. The

work has significant implications, as municipal supply

wells within the Gray project area are threatened by

groundwater contamination. It is necessary to define

the likely sources of contamination if these water

resources are to be adequately protected.

Geophysical studies at different scales generally

support a dominant flow vector orientated N30E–

N40E, which contrasts with the dominant W–E flow

predicted from hydrogeologic modelling of the

aquifer system. At a local scale, geophysical moni-

toring of a tracer injected within a sandpit resolved a

N30E–N40E flow vector, despite a N60E local

resultant hydraulic gradient vector in the vicinity of

the injection. Site characterisation using GPR and EM

methods identified structures within the delta deposit

that are consistent with the interpretation of a

principal axis of anisotropy orientated approximately

NE–SW. The argument is strengthened by the terrain

Fig. 10. Fit of pole–pole resistivity data to background finite difference (MODFLOW) model. (a) Background model superimposed on the

measured data for August 2, 4 days after tracer injection. The influence of the tracer mass injected at 210 m is apparent in Line 2 m north but

absent in Line 6 m north. (b) Model parameters (not to scale) based on the results of 1D inversion of dipole–dipole data in Fig. 4 used for the fit

shown in (a).


Fig. 11. Results from finite difference (MODFLOW) resistivity modelling. (a) Lines 6 and 8 modelling results for the August 2 dataset. The

tracer dimensions are shown as B in (b). (b) Lines 6 and 8 modelling results for the August 9 dataset. The tracer dimensions are shown as C in

(b). (c) Schematic showing a plan view of the modelled tracer migration for the surficial tracer injected at Station 210 m on Line 0.


conductivity mapping of a larger scale conductive

anomaly apparently emanating from a road salt

storage pile. Plume transport again follows a pre-

dominantly N40E orientation. This suggests that the

results of the local geophysical study in the sandpit are

applicable at some sub-regional scale within the

project area.

Interpretation of the geophysical tracer monitoring

was limited by the relatively sparse temporal

sampling applied. The absence of a complete pre-

tracer injection dataset was an unfortunate compli-

cation. In addition, the low tracer concentration

employed in this study resulted in a subtle resistivity

response. However, modelling of two different

geophysical datasets, electrical resistivity and SP,

provided estimates of tracer velocity in close agree-

ment. Although there is significant uncertainty in the

modelling of both datasets, it is likely that the result at

least represents an order of magnitude estimate of flow

velocity at the study site. The degree to which this

estimate is applicable at a regional scale remains

unclear.

One important aspect of this study is the successful

detection of a clear electrochemical SP anomaly

associated with the tracer. The temporal change in

shape of this anomaly resolved a groundwater flow

vector in close agreement with that obtained from

resistivity modelling. Whereas the response of the

resistivity methods to the small tracer volume and

concentration added is subtle, the SP signal is quite

clear. As far as the authors are aware, such use of the

SP method is not reported in the literature. The results

obtained here support further studies on this promis-

ing hydrogeological application of the SP method.

6. Conclusions

This study demonstrates the value of geophysics in

hydrogeological investigations of unconsolidated

aquifers. Hydrogeological models frequently offer

limited prediction performance due to insufficient

characterisation of subsurface hydraulic properties. In

this case, a geophysical study provided evidence that

anisotropy within a delta system is exerting a strong

control on the direction of groundwater flow. This

statement is supported by multiple methods at

different scales. Geophysical characterisation of

deltaic structures, as well as monitoring of an injected

tracer, support the argument. Current models of

groundwater flow developed for this study area do

not account for this anisotropy. It is likely that

predictions of the sources of contamination at affected

wells within the study area will be in error.

Geophysical methods have the potential to constrain

hydraulic parameters and improve groundwater flow

model prediction. In this case, for example, multiple

geophysical tests within the region could assess

spatial variability in anisotropy, flow direction and

flow rate across the groundwater model domain.

Acknowledgements

S.K. Sandberg and L. Slater were supported by the

Maine Science and Technology Foundation under

cooperative agreement 95-06N. R. Versteeg was

supported by the Schlumberger Foundation as the

Schlumberger Junior Scholar. A USM student intern

(Evan Sanborn) was funded by the Gray Water

District and assisted with groundwater modelling.

We thank Jim Foster of the Gray Water District for

access to the water supply wells and springs, as well

as other logistical and moral support. Dr C Fitts

(University of Southern Maine) gave valuable advice

on the hydrogeology of the Gray area. Dr A. Binley

(Lancaster University, UK) provided the 2D resis-

tivity inversion algorithm used to process dipole–

dipole data. We thank the following students who

assisted with geophysical and hydrogeologic field-

work: Jon Drasdis, Stacy Towne, Matthew Doughty,

Kelly Rust, and Evan Sanborn.

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