Vadose Zone Journal 1:273-280 (2002)
© 2002 Soil Science Society of America
Electrical Potential Distributions in a Heterogeneous Subsurface in Response to Applied Current
Solution for Circular Inclusions
Alex Furman*,a,
A. W. Warrickb and
Ty P. A. Ferréc
a Hydrology and Water Resources Department, University of Arizona, Tucson, AZ 85721
b Soil, Water, and Environmental Sciences Department, University of Arizona, Tucson, AZ 85721
c Hydrology and Water Resources Department, University of Arizona, Tucson, AZ 85721
* Corresponding author (alex{at}hwr.arizona.edu)
Received 6 March 2002.
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ABSTRACT
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An analytic solution to the Laplace equation for potential distribution in response to current flow in a heterogeneous, two-dimensional semi-infinite domain is studied. Circular heterogeneities of varying sizes and electrical conductivities are considered. We investigate the response of the stream function, the potential field, and, in particular, the potential at the top boundary relative to the background as a function of the size, location, and electrical conductivity of circular inclusions taken singly or multiply. The analytic solution sets the basis for the application of sensitivity analysis to the electrical resistance tomography (ERT) method, as an initial step toward improving the application of the method to tracking rapid hydrological processes.
Abbreviations: AE, analytic element [method] • ERT, electrical resistance tomography
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INTRODUCTION
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ONE OF THE GREATEST LIMITATIONS to monitoring vadose zone processes is the effect of the measuring device on the hydrologic processes under investigation. Standard methods (gravimetric sampling, tensiometers, neutron probes, time domain reflectometry) are invasive and/or destructive, potentially disturbing geological bedding or flow regimes. Noninvasive geophysical methods provide a possible means to improve vadose zone monitoring.
In the electrical resistance tomography method, current is injected into the ground through two current electrodes and simultaneously measured with two potential electrodes. A series of apparent electrical resistance measurements are made using sets of four electrodes placed at the ground surface. By simultaneously interpreting the response of electrode arrays with differing electrode separations, it is possible to form a two- or three-dimensional image of the subsurface electrical conductivity distribution. This method is becoming popular in vadose zone hydrology because the strong dependence of the electrical conductivity on the volumetric water content and salinity allows for rapid, nonintrusive monitoring through electrical conductivity mapping. Instruments have developed rapidly, allowing for automated measurement of large numbers of electrode arrays with off-the-shelf systems. However, this ease of use has the potential for misapplication of the method if users do not fully appreciate the response of each ERT measurement to subsurface conditions or the manner in which multiple measurements are combined to form a single image of the subsurface electrical conductivity distribution.
Any single ERT measurement represents the steady-state response of the system to a fixed electrical source and sink. Therefore, the response of an array to a given subsurface electrical conductivity distribution can be determined numerically with readily available analysis packages. However, given the large number of measurements used in a typical ERT survey, the sensitivity of the system response to the domain size, and the high spatial resolution necessary to model small heterogeneities, numerical solutions can be cumbersome and time consuming. This is compounded greatly if ERT is to be applied to the study of transient processes.
We present a powerful analytical method that can be used to efficiently predict the response of a set of ERT measurements to isolated subsurface heterogeneities. The results can be presented as electrical flow nets, which lead to improved understanding of the sensitivity of ERT measurements to single and multiple subsurface bodies such as voids, ore bodies, clay lenses, and regions of relatively high water content or salinity. The solution can be used for the forward simulation of current flow in general, or as part of the inversion used to determine medium properties from ERT measurements. In addition, the method can be used to identify appropriate targets for ERT surveys and to optimize the ERT surveys for expected target properties.
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BACKGROUND
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The Electrical Resistance Tomography Method
Each single ERT measurement is based on a measurement of the electrical potential between two electrodes (P1 and P2 in Fig. 1, commonly referred as M and N in the geophysics literature) resulting from a constant current applied through two other electrodes (C1 and C2 in Fig. 1, A and B in the geophysics literature). The apparent electrical conductivity is calculated as the ratio of the applied current to the measured potential, with a correction applied for the geometrical effects of the electrode spacings. This apparent electrical conductivity is some weighted average of the electrical conductivities of the subsurface regions through which current flows. However, an infinite number of electrical conductivity distributions can give rise to the same apparent electrical conductivity. To resolve this nonuniqueness problem, a large number of overlapping measurements are made and interpreted simultaneously.
All combinations of four electrodes may be classified as one of four array types (Table 1). Typical ERT surveys use one of three classical array types: Wenner, Schlumberger, or double dipole. Schlumberger arrays center the potential electrode pair between the current electrodes. A Wenner array is a specific Schlumberger array with equal spacing between all adjacent electrodes. C-P-P-C arrays include the common Wenner and Schlumberger arrays as well as a large number of arrays that do not center the current electrodes and potential electrodes at the same location. We refer to these arrays as Wenner–Schlumberger type arrays. To form a double dipole array, the current and potential electrode pairs do not overlap. These arrays commonly have equal spacing between the current electrodes and the potential electrodes. P-P-C-C (or C-C-P-P) arrays include the common double dipole arrays as well as a large number of arrays that have different C-C and P-P separations. For simplicity, we refer to these as double dipole arrays. We classify P-C-C-P arrays as inverse in that the location of the current electrodes is opposite to the Wenner and Schlumberger arrays. P-C-P-C (and C-P-C-P) arrays are referred to here as partially overlapping. For a more detailed description of array types and their associated geometric factors, see Telford et al. (1990) and Loke (2000).
The uniqueness of the final interpretation of a set of ERT measurements depends upon the signal/noise ratio of the measurements, the properties of the inversion routine used, and the degree to which each region of the subsurface is sampled by multiple measurements. This last condition depends on the number of measurements, their locations and array types, and the size and location of the subsurface heterogeneities. The method presented below allows for a more precise investigation of the spatial sensitivity of any given ERT array, which is critical in designing a set of ERT measurements that will lead to a unique interpretation of the subsurface electrical conductivity distribution.
The Analytic Element Method
The analytic element (AE) method is based on the superposition of suitable closed-form analytic functions (Strack, 1989). It is essentially based on solution for each element in the flow regime, superposition of the components, and matching potential and flow at interfacial boundaries. Among the advantages of this method are the high solution accuracy near interfaces, stability, and automatic solution of the stream function that can be expressed through the use of complex variables. Among the disadvantages of the AE method are that the inclusion must be of relatively simple geometry (here we use circles), time dependence cannot be considered directly, and there is a very limited number of available models (e.g., Split; Jankovi
, 2002).
For a brief description of the AE method and a history of complex variable methods, see the introductory chapter of Strack (1989). Barnes and Jankovi (1999) and Jankovi and Barnes (1999) recently showed the use of this method for domains including a large number (105) of two- or three-dimensional inclusions. Among possible uses are the accurate simulation of water flow in the subsurface, and the tracking the exact route of particles through soil. Two major applications of the analytic element method to groundwater modeling are the Dutch (The Netherlands) National Groundwater Model (NAGROM), and the Metropolitan Groundwater Model of the Minneapolis/Saint Paul, Minnesota (De Lange and Strack, 1999).
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THEORY
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We present a two-dimensional analysis of the electrical potential distribution in response to a paired source and sink of electrical current applied at the ground surface, with electrodes located at [x, y] = [-s, 0], and [x, y] = [s, 0]. Figure 2 illustrates the solution domain, including heterogeneities and images. A detailed development of the analytic element approach is presented in the appendix. The current electrodes have a separation of 2 s.
Following Barnes and Jankovi (1999), who applied the analytic element method to the study of steady-state water flow, we define a complex electrical potential 
by
 | [1] |
where z = x + iy a complex variable and 
is the flux potential, (M L V-1 T-3), which is equal to the electric potential, V (V), multiplied by the electrical conductivity K (M L V-2 T-3). 
is the stream function (M L V-1 T-3). The complex potential distribution due to the applied current is
 | [2] |
where q (M L V-1 T-3) is the source strength, which is equal to twice the current applied to the soil through the source electrode, and the subscript B denotes the homogeneous background condition.
We now consider J isolated cylindrical heterogeneities with centers at x0,j, y0,j, with radii of r0,j, and with electrical conductivities, Kj, that contrast with the background electrical conductivity, K0. These localized heterogeneities may represent clay lenses, conduits, or roots, depending on the scale of investigation. The perturbation of the electric potential due to each of these heterogeneities, defined in terms of the local dependent variable zj = xj + iyj, is
 | [3] |
or
 | [4] |
where +j refers to the region within the heterogeneity, for which |zj| < r0,j, and -j applies to the region outside of the heterogeneity |zj| > r0,j. N is the number of components in the series; the solution is exact as N approaches infinity. Image cylinders are added symmetrically (with respect to the ground surface) above the ground surface to simulate the zero electrical conductivity air. The potential contribution due to each of these images is
 | [5] |
with zI,j measured from the center of the image. For our case, as images are above ground, we eliminate the need for separate equations for potentials within the images.
The combined potential is
 | [6] |
The above solution satisfies stream (current) continuity everywhere because is continuous. However, at the interface of each inclusion, there is a discontinuity in the flux potential . For the entire domain, is related to the electric potential 
by = K, with K corresponding to the local electrical conductivity.
For computational purposes, it is convenient to compute in a dimensionless form of /q and to use the coordinates X = x/s, Y = y/s. For these coordinates, we also define R0 = r0/s. The dimensionless stream function /q will vary from 0 to 
, assuming is defined to be 0 along y = 0 and -s 
x s. With x0,j and y0,j the global coordinates of the center of each inclusion, relative coordinates x1,j and y1,j are related to global coordinates x and y by
Further details of the solution are given in the Appendix.
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RESULTS AND EXAMPLES
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Potential Distribution
Our goal is to describe the effects of the location, relative electrical conductivity, and size of cylindrical subsurface inhomogeneities on the electric potential measured at the ground surface. We consider cylinders that are oriented such that they can be represented as circles in a two-dimensional cross-section. Understanding of the voltage distribution in the presence of those heterogeneities represents the first step toward identifying preferred electrode arrays to locate subsurface objects of contrasting electrical conductivity.
All computations were conducted using Matlab (Mathworks, 1998), with M (number of matching points) set to 100, and N (number of terms in series) set to 20. Iterations were continued until no parameter was changed by more than 10-10.
We first examine the flow net formed by lines of equal and created by application of our analytic element method to a single inhomogeneity. For ease of comparison, values of , , and are normalized as /q, /q, and /q. The electrical conductivity of the background domain, K0, is one. Figure 3 shows flow nets for a portion of the homogeneous domain (0), and for the same portion of the domain for two cases of single circular inhomogeneities. Figures 3d and 3e give more detailed views of the regions immediately surrounding the heterogeneities. The properties of the two heterogeneities are identical except for the relative electrical conductivity, which is set to K = 10 for the first case (I), and to K = 0.1 for the second (II). The properties for these perturbations and for those used in later examples are listed in Table 2.
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Fig. 3. Electrical flow nets for: (a) a homogeneous medium (0), (b) a single circular inclusion at [0.3, -0.3] with K1 = 10 and R0 = 0.1 (I), and (c) a single circular inclusion at [0.3, -0.3] with K1 = 0.1 and R0 = 0.1 (II). Current is applied at [-1, 0] and [1, 0]. Electrical flow nets for: (d) a detailed view of Inclusion I and (e) a detailed view of Inclusion II.
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As expected, a conductive heterogeneity (I, Fig. 3b and 3d) causes convergence of flow, while a resistive body (II, Fig. 3c and 3e) causes flow divergence. That is, the conductive body shows reduced potential gradients within the inhomogeneity and an increased gradient in the upstream (left) and downstream (right) directions. There are decreased gradients above and below the body as flow is diverted from these regions into the inhomogeneity. The effect is reversed for the low conductivity body. To better visualize the effects of the perturbation throughout the domain, the potential change due to the addition of the heterogeneity is plotted in Fig. 4 for the two heterogeneities shown above. Positive changes show increased potential at a given location compared with the homogeneous background case; negative changes show a decreased potential.
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Fig. 4. Change of potential from that in a homogeneous domain due to a single inclusion at [0.3, -0.3] with R0 = 0.1 for (a) K1 = 10 (I) and (b) K1 = 0.1 (II). Current is applied at [-1, 0] and [1, 0]. The streamline passing through each inhomogeneity illustrates the direction of flow.
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Although the responses to the two heterogeneities differ slightly, they have many common features. For both cases, the potential is perturbed throughout the domain as a result of an isolated heterogeneity. The line of zero change runs through the center of the heterogeneity, but is not normal to the ground surface. Finally, comparison with Fig. 3b and 3d shows that the regions of greatest impact are located along a flow line that initially runs through the location of the center of the inclusion. These features are seen for all single perturbations regardless of their location, size, or relative electrical conductivity, as shown in Fig. 5. In general, the impact of a body is determined by the fraction of the total flux that flows through the body. For example, a large, deep, conductive perturbation shows a very high increase in the potential adjacent to the heterogeneity, but due to its depth, the potential change at the ground surface is quite small. Similarly, a conductive perturbation that is not located between the source and sink (e.g., Body V, Fig. 5c) has a relatively small impact on the electrical potential at the ground surface because a small fraction of the total flow passes through the body.
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Fig. 5. Change of potential, compared with a homogeneous domain, resulting from (a) Inclusion III, (b) Inclusion IV, and (c) Inclusion V. The streamline passing through each inhomogeneity illustrates the direction of flow. See Table 1 for inclusion properties.
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Given that a single perturbation impacts the potential distribution throughout the domain, we expected that the combined effect of multiple heterogeneities would give rise to a complex potential distribution. To examine this, we begin with Anomalies I (Fig. 3b, 3d, and 4a) and III (Fig. 5a). The combined effect of these perturbations is a region of large decrease of potential between the bodies (Fig. 6a). Physically, this is due to the focusing effect of Body I combined with the diversion caused by Body III. The potential changes outside of the two bodies are reduced. The further addition of Body IV essentially "captures" flow from Body III (Fig. 6b). As a result, the increased potential difference to the right of Body III is restored, and the location of the most negative potential change moves to the region between Bodies III and IV. This is a good demonstration of the advantage of the analytic element method. Solution of such a problem using finite difference or finite element methods would require a very large number of nodes or elements to avoid numerical problems resulting from the locally steep gradients. For the analytic element method, the only cost is a slight increase in the number of iterations needed for convergence.
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Fig. 6. Change of potential, compared with a homogeneous domain, resulting from (a) two inclusions (I, III) and (b) three inclusions (I, III, and IV). See Table 1 for inclusion properties.
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Sensitivity
To use the analytic element method to examine the performance of an ERT system, we define the sensitivity, S, of a single ERT array to a conductivity perturbation, as the absolute change in measured potential between the two potential electrodes:
 | [7] |
Single perturbations were centered on square grid nodes, with the grid spacing equal to the electrode separation, and with the electrodes located above grid nodes. We use a 21-electrode linear system (numbered and located at X = -10 through X = 10, at the ground surface). A total of 35 910 different four-electrode arrays can be formed from these electrodes; only a small fraction of these are standard Wenner, Schlumberger, or double dipole arrays. Our perturbation location grid extends to twice the width of the electrode system (X = -20 to X = 20. The maximum perturbation depth considered is 20. In all calculations the perturbation has a relative conductivity of K = 2, and a radius of r0 = 0.5, such that the perturbation diameter is equal to the minimal electrode separation.
Table 3 lists the sensitivities calculated for some arrays to a single perturbation located at [X, Y] = [0, -2]. For this anomaly, most of the arrays that have high sensitivities are not one of the classic array types (i.e., Wenner, Schlumberger, or double dipole).
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Table 3. Performance of most sensitive arrays and some standard arrays in response to a perturbation centered at [0, -2].
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The method presented here can be used to identify the array that has the highest relative sensitivity to any given perturbation. However, in practice, the ability to sense a perturbation depends on the signal/noise ratio of the ERT system. Assuming that the system noise is constant, the absolute value of the sensitivity of the most sensitive array can be used as an indicator of the signal/noise ratio. Therefore, before considering the characteristics of the most sensitive arrays, we present the absolute sensitivity of the most sensitive arrays (Fig. 7). The results show that even if the most sensitive array is selected, ERT systems will always be better able to characterize shallow subsurface properties within the span of the electrodes. The results presented in Fig. 7 can be used as a measure of the confidence of the conclusions made below. That is, the analysis of the optimal array is more applicable in regions of higher absolute sensitivity of the most sensitive array.
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Fig. 7. The absolute sensitivity of the most sensitive arrays to a conductive perturbation plotted at the location of the perturbation.
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Figure 8 describes the characteristics of the most sensitive array for a single perturbation located at each grid node. Figure 8a shows the C-C separation of the array, and Fig. 8b shows the P-P separation. Figure 8c shows the offset, defined here as the horizontal distance between the array center (i.e., average location of all electrodes) and the perturbation (where a positive offset indicates that the center of the array is to the right of the perturbation). Figure 8d shows the opening, defined here as the distance between the center of the current electrodes and the center of the potential electrodes. For example, for a perturbation located at [0, -1], the most sensitive array has C-C separation of 3, a P-P separation of 3, an offset of 0, and an opening of 1. This is a partially overlapping array with the current electrodes located at [-1, 0] and [2, 0] the potential electrodes placed at [-2, 0] and [1, 0]. The C-C separations of the most sensitive arrays tend to be equal to or slightly larger than the P-P separation. In comparison with the classic arrays, double dipole arrays have equal C-C and P-P separations, Wenner arrays have a C-C separation that is three times their P-P separation, and Schlumberger arrays typically have even larger C-C/P-P ratios. For the region beneath the electrodes, the most sensitive array tends to be centered directly above the investigated perturbation. However, for deeper perturbations and perturbations located outside of the span of the electrodes, wider arrays are required to deliver current to the perturbation. Given the limited span of electrodes available, it was not possible to form a wide array that is centered above the perturbation. As a result, the offset is larger. With few exceptions, the opening tends to be minimal. That is partially overlapping arrays are preferred over double-dipole arrays.
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Fig. 8. The C-C (A) and P-P (B) separations for the arrays showing the highest sensitivity to a conductive perturbation plotted at the location of the perturbation. The offset (C) and opening (D) of the arrays showing the highest sensitivity to a conductive perturbation plotted at the location of the perturbation.
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DISCUSSION
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In Fig. 9, the arrays showing the highest sensitivity to a single perturbation are identified according to the descriptions in Table 1. There is no region in which an inverse array shows the highest sensitivity to a perturbation. Most of the domain is dominated by partially overlapping or Wenner–Schlumberger arrays, with a limited region in which double dipole arrays show the highest sensitivity. Typically, electrodes are placed such that the subsurface region of interest lies within a triangle or trapezoid located directly beneath the electrode set (e.g., Loke, 2000). This area coincides with the region of greatest absolute sensitivity, as shown in Fig. 7. Surprisingly, this region is dominated by the seldom-used partially overlapping arrays, with the margins preferring the more commonly used Wenner–Schlumberget arrays.
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Fig. 9. The array type (see Table 1 for definitions) of the array showing the highest sensitivity to a conductive perturbation plotted at the location of the perturbation.
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The following general rules describe the array that is most sensitive to a single perturbation:- For perturbations located under the electrode set in the area of greatest absolute method sensitivity, the array tends to be a partially overlapping array centered above the perturbation with equal current and potential electrode separations. Generally, the optimal separation (C-C and P-P) can be determined as Electrode Separation = 3 + 2 (Perturbation Depth - 1). The opening in this case would be minimal (one).
- For perturbations located outside of the electrode set, both the current and potential electrode separations tend to be as large as possible (the maximal C-C and P-P separations possible) provided both separations are equal. This results in a partially overlapping array with an opening of one. In this case, the array tends to be centered about the electrode-set center (X = 0), such that the maximal current density is generated near the perturbation. The absolute sensitivity to these perturbations is small. As a result, measurement made with these arrays will have a relatively low signal/noise ratio.
- A large transitional zone (Fig. 9) exists between the previous two regions, in which the optimal array is characterized by a type A (Wenner–Schlumberger like) configuration for most cases. The P-P separation tends to be as large as possible, but smaller that the C-C separation. The opening in this region tends to be zero, making the array symmetric (i.e., a wide Schlumberger).
- The small transitional zone between Regions 2 and 3 is characterized by double dipole like arrays. The arrays are asymmetric and the C-C and P-P separations increase with the depth of the perturbation.
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SUMMARY AND CONCLUSIONS
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An analytical solution to the Laplace equation is applied for a semi-infinite isotropic domain containing circular, nonoverlapping inhomogeneities in an otherwise homogeneous background. Electric flow in a vertical plane is considered. The circular inhomogeneities may represent roots, gopher holes, conduits, or, in large number, the heterogeneous composition of the subsurface. The analytical solution is better suited to analysis of this problem than numerical methods because it offers higher accuracy that is not resolution dependent (Jankovi and Barnes, 1999), greater ease of solution, and a lack of boundary effects. Using this solution, we investigate the effects of a local subsurface inhomogeneity on potential distributions throughout the domain. The highly accurate solution and the simultaneous solution for stream functions allow for better insight into the effects of strongly contrasting bodies and multiple heterogeneities. Initial investigation of the sensitivity of ERT arrays identifies the subsurface region in which the signal/noise ratio is expected to be relatively high. The results show that the seldom-used partially overlapping array has the highest sensitivity to perturbation in this region. A general rule for choosing the optimal array in this region is defined. This rule can be used directly to choose optimal arrays for ERT investigations of isolated bodies such as for archeological, infrastructure, and geological surveying. Furthermore, this represents a first step toward improving the ERT method for quantitative applications to subsurface hydrology.
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APPENDIX
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Further Details on the Analytic Element Model for Electrical Flow through a Circular Inhomogeneity in a Vertical Half Space
Consider an isolated cylindrical heterogeneity with center at x0,1, y0,1, and with an electrical conductivity, K1, that contrasts with the background electrical conductivity, K0. As mentioned above, this localized heterogeneity may represent a clay lens, conduit, or root, depending on the scale of investigation. The perturbation of the electric potential component resulting from this heterogeneity, defined in terms of the local dependent variable z1 = x1 + iy1, is
 | [A1] |
or
 | [A2] |
where +1 refers to the region within the heterogeneity, for which |z1| < r0,1, and -1 applies to the region outside of the heterogeneity |z1| > r0,1. N is the number of components in the series; the solution is exact as N approaches infinity. An image cylinder is added above the ground surface to simulate the zero electrical conductivity air. The contribution due to this image is
 | [A3] |
with zI,1 measured from the center of the image.
Additional heterogeneities may be taken with centers at [x0,j, y0,j], with radii r0,j and with electrical conductivities of Kj. For this preliminary analysis, we assume the inclusions are not overlapping. A second heterogeneity is shown in Fig. 2 along with its image centered at [x0,2, y0,2]. The contribution due to the jth heterogeneity is by Eq. [3] and [4]. The contribution from the image for the jth inclusion is by Eq. [5], with the combined potential by Eq. [6]. The solution satisfies stream (current) continuity everywhere because is continuous. However, at the interface of each inclusion, there is a discontinuity in the flux potential . For the entire domain, is related to the electrical potential by = K, with K corresponding to the local electrical conductivity. Therefore, to assure continuity of the potential at each interface must satisfy
 | [A4] |
with K0 the background electrical conductivity and Kj the electrical conductivity within the inclusion, j = 1,2,...J. Combining Eq. [6] and [A4] leads to
 | [A5] |
with a remainder R,j given by
 | [A6] |
Substitution for the relative electrical conductivity of the inclusion, kj, defined as Kj/K0, gives
 | [A7] |
This expression is comparable to Eq. [12] of Barnes and Jankovi (1999) for steady-state water flow.
Solution for an,j
The coefficients that satisfy Eq. [A13] can, in principle, be found directly. However, it is more simple to find them by sequential approximation. This is accomplished by first considering (following Barnes and Jankovi, 1999; their Eq. [17], and [18])
 | [A8] |
 | [A9] |
where u denotes iteration number, and i = 
.
The values of 
m are control points on |zj| = r0,j and may be chosen by m = 2m/M, m = 0,...M - 1. The last two equations may be repeated with u = 1, 2, ..., with 0R,j = 0, and iR,j computed using "old" values of ap,j. The iteration is continued until the an,j do not change significantly between iterations. For convenience, we choose m to be the same for all of the inclusions.
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ACKNOWLEDGMENTS
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This research was funded in part by SAHRA (Sustainability of Semi-Arid Hydrology and Riparian Areas) under the STC program of the National Science Foundation, agreement EAR-9876800. This research was also supported in part by Western Research Project W-155.
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REFERENCES
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- Barnes, R., and I. Jankovi. 1999. Two-dimensional flow through large number of circular inhomogeneities. J. Hydrol. 226:204–210.
- De Lange, W.J., and O.D.L. Strack. 1999. Introductory comments. J. Hydrol. 226:127.
- Jankovi, I. 2002. Split. v.2.4. Available at www.groundwater.buffalo.edu (cited June 2002; verified 6 Sept. 2002). University of Buffalo Groundwater Research Group, Buffalo, NY.
- Jankovi, I., and R. Barnes. 1999. Three-dimensional flow through large number of spheroidal inhomogeneities. J. Hydrol. 226:224–233.
- Loke, M.H. 2000. Electrical imaging surveys for environmental and engineering studies: A practical guide to 2-D and 3-D surveys [Online]. Available at www.agiusa.com (modified 30 July 2002; verified 6 Sept. 2002). Advanced Geosciences, Austin, TX.
- MathWorks. 1998. Matlab. The MathWorks, Inc., Natick, MA.
- Strack, O.D.L. 1989. Groundwater mechanics. Prentice Hall, Upper Saddle River, NJ.
- Telford, W.M., L.P. Geldart, R.E. Sheriff. 1990. Applied geophysics. 2nd ed. Cambridge Univ. Press, Cambridge, UK.
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ABSTRACT
INTRODUCTION
