IP interpretation in environmental investigations

Transcription

1 GEOPHYSICS, VOL. 67, NO. 1 (JANUARY-FEBRUARY 2002); P , 10 FIGS / IP interpretation in environmental investigations Lee D. Slater and David Lesmes ABSTRACT The induced polarization (IP) response of rocks and soils is a function of lithology and fluid conductivity. IP measurements are sensitive to the low-frequency capacitive properties of rocks and soils, which are controlled by diffusion polarization mechanisms operating at the grain-fluid interface. IP interpretation typically is in terms of the conventional field IP parameters: chargeability, percentage frequency effect, and phase angle. These parameters are dependent upon both surface polarization mechanisms and bulk (volumetric) conduction mechanisms. Consequently, they afford a poor quantification of surface polarization processes of interest to the field geophysicist. A parameter that quantifies the magnitude of surface polarization is the normalized chargeability, defined as the chargeability divided by the resistivity magnitude. This parameter is proportional to the quadrature conductivity measured in the complex resistivity method. For nonmetallic minerals, the quadrature conductivity and normalized chargeability are closely related to lithology (through the specific surface area) and surface chemistry. Laboratory and field experiments were performed to determine the dependence of the standard IP parameters and the normalized chargeability on two important environmental parameters: salinity and clay content. The laboratory experiments illustrate that the chargeability is strongly correlated with the sample resistivity, which depends on salinity, porosity, saturation, and clay content. The normalized chargeability is shown to be independent of the sample resistivity and it is proportional to the quadrature conductivity, which is directly related to the surface polarization processes. Laboratory-derived relationships between conductivity and salinity, and normalized chargeability and clay content, are extended to the interpretation of 1-D and 2-D field-ip surveys. In the 2-D survey, the apparent conductivity and normalized chargeability data are used to segment the images into relatively clay-free and clay-rich zones. A similar approach can eventually be used to predict relative variations in the subsurface clay content, salinity and, perhaps, contaminant concentrations. INTRODUCTION In recent years, the induced polarization (IP) method has seen increasing use in environmental applications (Ward et al., 1995). IP measurements are sensitive to the low-frequency capacitive properties of rocks and soils that result from diffusioncontrolled polarization processes at the interface between mineral grains and the pore fluid. Previous workers have shown that these surface polarization mechanisms can be very sensitive to changes in the lithology and pore fluid chemistry (Vacquier et al., 1957; Marshall and Madden, 1959; Bodmer et al., 1968; Ogilvy and Kuzmina, 1972; Pelton et al., 1978; Klien and Sill, 1982). Environmental examples of the successful use of IP include saline intrusion mapping (Seara and Granda, 1987), the detection of clay units (Iliceto et al., 1982; Vinegar and Waxman, 1984), the detection of both inorganic contaminants (Cahyna et al., 1990; Ruhlow et al., 1999) and organic contaminants (Olhoeft, 1984, 1985, 1992; Vanhala et al., 1992), and permeability estimation (Knoll et al., 1994; Borner et al., 1996; Sturrock et al., 1999). Although IP methods are being used more frequently in environmental investigations, they are not as widely used as other electrical methods [e.g., dc resistivity, electromagnetic (EM) methods, and ground-penetrating radar], and their full potential has yet to be realized. A number of factors have contributed to the underutilization of IP methods in environmental investigations. First, IP data acquisition is difficult and time consuming, requiring sensitive nonpolarizing electrodes and careful layout of wiring in order to prevent unwanted EM coupling effects. Second, 2-D and 3-D inversion codes have not been available to facilitate Manuscript received by the Editor November 29, 1999; revised manuscript received April 4, Formerly University of Southern Maine, Department of Geosciences, Gorham Maine 04038; presently University of Missouri Kansas City, Department of Geosciences, 5100 Rockhill Road, Kansas City, Missouri SlaterL@umkc.edu. Boston College, Department of Geology and Geophysics, 140 Commonwealth Avenue, Chestnut Hill, Massachusetts lesmes@bc.edu. c 2002 Society of Exploration Geophysicists. All rights reserved. 77

2 78 Slater and Lesmes interpretation. Finally, and most fundamental, the physiochemical interpretation of IP parameters is not well established or fully understood. Recent developments of sophisticated and automated IP field instruments, and powerful 2-D and 3-D inversion codes have significantly improved our capabilities for performing high-quality IP field surveys (LaBrecque, 1991; Oldenburg and Li, 1994; Weller et al., 1996; Shi et al., 1998). However, a definitive physiochemical model or explanation for the IP response of rocks and soils has not been established (Ward et al., 1995). Therefore, interpretation of the various IP parameters is still qualitative and, to a large degree, subjective. A better understanding of the relation between the IP parameters and subsurface lithological and fluid properties is required in order to increase the effectiveness of IP interpretation in environmental investigations. A number of field parameters were adopted during the development of IP for mineral exploration. These include the time domain chargeability M, percentage frequency effect (PFE), and the phase angle φ (all defined below). These parameters were developed as a result of instrumentation limitations and the way the IP effect was traditionally measured. The field IP parameters (φ, PFE, M) are sensitive to both the bulk conduction and surface polarization properties of a material. Weighting the field IP parameters by the measured conductivity (or dividing by resistivity) yields the following normalized IP parameters: quadrature conductivity (σ ), metal factor (MF), and a normalized chargeability (MN), which was called a specific capacity by Keller (1959). These normalized IP parameters are independent of the bulk conduction effects and they are much more sensitive to the surface chemical properties of the material (Lesmes and Frye, 2001). Since the surface chemical properties are very sensitive to changes in geochemical and microstructural parameters, the use of normalized IP parameters should improve the effectiveness of IP surveys in environmental investigations. The objective of this paper is to use laboratory and field data to demonstrate pore-fluid and lithologic controls on the IP parameters. We also discuss the implications of these laboratory and field studies for the interpretation of IP data in environmental investigations. Our laboratory results substantiate the findings of Lesmes and Frye (2001). We find that that the normalized chargeability is closely related to lithologic factors and helps to distinguish IP effects due to lithology from IP effects due to salinity. The field studies show that the normalized chargeability, as proposed by Keller (1959) and adopted by Lesmes and Frye (2001), is a valuable parameter in the interpretation of field-scale IP surveys for environmental purposes. The format of this paper is as follows. First, the IP method is reviewed and the significance of the various IP parameters is discussed in terms of volumetric conduction and surface polarization mechanisms. Next, laboratory results illustrating the IP dependence on fluid conductivity and structural properties are presented. The laboratory studies are then used to interpret field IP surveys in terms of the effective salinity and clay content. Finally, the broader significance of our findings is discussed in terms of potential applications of the IP method in environmental applications. IP MODEL AND RELATION TO FIELD IP PARAMETERS The IP effect manifests itself as a residual voltage following termination of an applied current (time-domain measurement) or as a frequency-dependent resistivity (frequency-domain measurement). A measure of the magnitude of the IP effect in the time domain is the chargeability M (e.g., Schön, 1996, 458), M = t f ts V s dt V p 1 t, (1) where V s is a residual voltage integrated over a time window defined between times t s and t f after termination of an applied current, V p is the measured voltage at some time during application of the current, and t equals the length of the integrated time window. Units of chargeability are typically quoted as millivolts per volt (mv/v). In the frequency domain, the IP effect can be measured by the percentage frequency effect (PFE), PFE = σ (ω 1) σ (ω 0 ) σ (ω 0 ) 100, (2) where σ (ω 1 ) and σ (ω 0 ) are the formation conductivity magnitude measured at frequencies ω 1 and ω 0 (ω 1 >ω 0 ), respectively. In complex resistivity surveys, the phase angle (φ) at a particular frequency is typically used to express the IP properties of the formation. For small phase angles, which are typically observed in nonmetallic environments, the phase is equal to the ratio of the imaginary conductivity (σ ) to the real conductivity (σ ), ( σ φ(ω) = tan 1 ) (ω) = σ (ω) σ (ω) σ (ω). (3) Nonmetallic polarization results from diffusion controlled polarization processes at the interface between the mineral surfaces and the pore solution. This surface-controlled polarization can be represented by a complex surface conductivity, σsurf (ω) = σ surf (ω) + iσ surf (ω). (4) The large surface area associated with clay minerals enhances the magnitude of polarization in sediments and rocks. However, polarization effects are also significant and measurable even in clay-free unconsolidated material (e.g., Keller, 1959; Marshall and Madden, 1959; Vinegar and Waxman, 1984; Vanhala, 1997). In simple terms, the IP response reflects the degree to which the subsurface is able to store electrical charge, analogous to a capacitor. At high frequencies, the intrinsic capacity of the material is primarily determined by the high-frequency dielectric constant (k ). The high-frequency imaginary conductivity response is given by σ = ωk ε 0, where ε 0 is the permittivity of vacuum. At low frequencies (f 1000 khz), where IP phenomenon are typically measured in the field, ωκ ε 0 σ surf (ω), and the low-frequency complex conductivity response of the sample is given by, σ = (σ bulk + σ surf (ω)) + iσ surf (ω), (5) where σ bulk is a bulk conduction term. In this frequency range, the imaginary component of the conductivity can be considered a function of the surface conductivity, whereas the real component of the conductivity is a function of both the bulk and surface conductivity mechanisms. The simple equivalent circuit model in Figure 1, which represents the low-frequency electrical properties of the sample, contains a purely conductive

3 IP Interpretation in Environmental Investigations 79 flow pathway in parallel with a frequency dependent complexconductivity element (σsurf ). The complex surface-conductivity element represents a diffusion-controlled electrochemical polarization pathway at the grain surface fluid interface. An important aspect of this model is that low-frequency capacitive properties of the sample depend on the electrochemical surface phase, whereas the low-frequency conductive properties of the sample depend on the bulk conduction and surface conduction mechanisms: and, σ rock (ω) = σ surf (ω), (6) σ rock (ω) = σ bulk + σ surf (ω). (7) Both the real and imaginary parts of the complex surface conductivity are functions of the specific surface area, the surface charge density, and the surface ionic mobility (Lesmes and Morgan, 2000; Lesmes and Frye, 2001). In general, the surface charge density and the surface ionic mobility will depend upon the pore fluid composition and concentration. The bulk conductivity can be expressed by Archie s Law (Archie, 1942), σ bulk = σ w m S n, (8) where σ w is the solution conductivity, is the porosity, S is the saturation, and m and n are the cementation and saturation exponents, respectively. Based upon the parallel circuit model, the phase response of the rock will be given by σ surf φ(ω) = (ω) σ bulk + σ surf (9) (ω). If the bulk conductivity is much greater than the surface conductivity, typically the case at high salinity, then, φ(ω) = σ surf (ω) σ bulk (10) (ω). As bulk conduction increases with increasing solution conductivity, porosity, or saturation, the phase response will decrease in magnitude. Therefore, in terms of the interpretation of complex resistivity (CR) surveys, it is advantageous to separate the field data into the real and imaginary conductivity components. The real component will be primarily indicative of conduction processes, which, in the case of low clay content, can be modeled using Archie s Law. The imaginary component will be primarily indicative of the surface polarization mechanisms at the grain-solution interface. The proportionality between φ, PFE, and M is both theoretically and experimentally well established (Marshall and Madden, 1959; Seigel, 1959; Madden and Cantwell, 1967; Collett and Katsube, 1973; Shuey and Johnson, 1973; Van Voorhis et al., 1973; Vinegar and Waxman, 1984; Wait, 1984). Similar to the phase response, PFE and M describe the strength of the polarization process relative to ohmic conduction. The PFE is the ratio of the conductivity dispersion to the formation conductivity, which is primarily determined by the bulk rock properties. Since the low-frequency conductivity dispersion is only a function of the surface properties, the PFE response can be written as, PFE = σ surf (ω 1) σ surf (ω 0) σ bulk 100. (11) Pelton et al. (1978) defined the chargeability in terms of two parallel conduction paths, which can be thought of as a bulk conductivity (σ bulk ) and a surface conductivity (σ surf ). In the case that the bulk conductivity is much greater than the surface conductivity, the chargeability is proportional to the ratio of the surface conductivity to the bulk conductivity effects: σ surf M σ bulk + σ = σ surf surf σ bulk. (12) Equations (8), (9), and (10) show that the field IP parameters φ, PFE, and M are relative measures of the surface polarization, which tend to decrease with increasing sample conductivity. NORMALIZED IP PARAMETERS Weighting of the field IP parameters φ, PFE, and M by the measured conductivity (or dividing by resistivity) yields the following normalized IP parameters: quadrature conductivity (σ rock ), metal factor (MF), and normalized chargeability (MN). The quadrature conductivity is given by σ rock = σ rock tan(φ) = σ rock φ. (13) The metal factor, as defined by Marshall and Madden (1959), is given by MF = aσ (ω 0 )PFE = a(σ (ω 1 ) σ (ω 0 )), (14) where a is an arbitrary unitless constant, taken to be equal to 2π 10 5 by Madden and Marshall (1959). The normalized chargeability is given by MN = σ rock M. (15) FIG. 1. Simple equivalent circuit model for low-frequency ( 1000 khz) electrical current flow in nonmetallic rocks and sediments; σ bulk is a bulk (volume) conduction term, and σsurf is a complex surface conduction term. Equations (10) (15) show that the normalized IP parameters are directly related to the complex surface-conductivity parameter σsurf (ω). We note that normalization of chargeability as in equation (13) is not a new concept. Keller (1959) formulated this parameter and referred to it as the specific capacity. However, despite its direct relation to the surface polarization mechanisms as outlined above, we see little evidence of its use as a parameter in IP interpretation. Figure 2 illustrates the relationships between the field IP parameters (φ, PFE, and M) and the normalized IP parameters (σ, MF, and MN) for Berea sandstone. In this figure, adopted

4 80 Slater and Lesmes from Lesmes and Frye (2001), the field and normalized IP parameters are plotted as a function of the solution conductivity. In these experiments, the salinity of the saturating solution was varied from 24 to 8000 ms/m NaCl (ph = 8). The field IP parameters decrease with increasing solution conductivity, as predicted by equations (8) (10). The normalized IP parameters increase with increasing salinity, up to a solution concentration of 1000 ms/m, at which point they then decrease with increasing salinity. Similar responses were observed in the complex conductivity measurements of shaly sands made by Vinegar and Waxman (1984). They interpreted the maximum in the quadrature conductivity versus salinity response as a tradeoff between increasing surface charge density and decreasing surface ionic mobility with increasing solution concentration. These results illustrate the sensitivity of the normalized IP parameters to the surface chemical properties of porous media. Instrumentation LABORATORY MEASUREMENTS Time-domain IP measurements were made using the Phoenix V5 geophysical receiver (Figure 3). A ±5 V test signal is used to generate a low-current time-domain IP waveform suitable for laboratory measurements. Current injection into the sample is via stainless steel mesh electrodes at either end. The variable shunt resistor R 1 controls the current, and the current density in the sample is prevented from exceeding 10 ma/m 2. The resistivity and chargeability are measured across two nonpolarizing Ag-AgCl electrodes and referenced to the signal received across the precision resistor R 2. An AD620 pre-amplifier is used to boost the input impedance on the sample channel to 80 M-ohm, preventing any current leakage through the instrumentation. The potential electrodes are placed out of the immediate current flow path, to minimize unwanted electrode polarization effects (Vinegar and Waxman, 1984; Vanhala and Soininen, 1995). Time-domain measurements were made using an 8-s waveform. Chargeability was measured on ten consecutive windows of 100-ms length, the first window starting 100 ms after the current is switched off. The total chargeability as determined in this work is taken as the average of the values measured on these ten time windows. Accuracy of the instrumentation, as determined from measurements on capacitor-resistor networks with a known IP response, is of the order of 1 mv/v. Measurements on soil and sediment samples repeat to within 1 2 mv/v, depending on the quality of the nonpolarizing electrodes. Effect of fluid conductivity As previously discussed, Lesmes and Frye (2001) investigated the influence of pore fluid chemistry on the spectral IP response of Berea sandstone cores. These well-constrained experiments are not representative of typical field geophysical investigations. In the current study, therefore, a simple laboratory experiment was performed to illustrate the effect of fluid conductivity as expected from a saline intrusion or industrial contamination problem, reflecting the use of the IP method in previous environmental studies (Seara and Granda, 1987; Ruhlow et al., 1999). A simple flow-through experiment was designed in which a sediment sample was saturated with saline water and then flushed with clean water. The objective was to observe the dependence of the IP parameters on fluid conductivity in a dynamic hydrological environment. For this experiment a sand sample was initially de-aired and saturated with distilled water (σ w = 1 ms/m). Structural properties and sample dimensions are summarized in Figure 4b. FIG. 2. Dependence of field IP parameters (left axes) and normalized IP parameters (right axes) on fluid conductivity (σ w ) for a Berea sandstone core (after Lesmes and Frye, 2001).

5 IP Interpretation in Environmental Investigations 81 FIG. 3. Laboratory time-domain IP system based on the Phoenix V5 receiver. FIG. 4. (a) Dependence of resistivity ( ρ ), chargeability (M), and normalized chargeability (MN) during tracer injection into a sediment column. (b) Sediment physical properties. (c) M and MN versus σ.

6 82 Slater and Lesmes The sample was flushed with 33 pore volumes of a NaCl solution (σ w = 220 ms/m), followed by reintroduction of 37 pore volumes of distilled water. Measurements of resistivity and chargeability were primarily performed during reintroduction of the distilled water (Figure 4a). As expected, the bulk resistivity decreases with the introduction of the saline solution and recovers upon flushing with the distilled water. The chargeability increases with fluid resistivity and, hence, bulk resistivity. In fact, the chargeability curve is a near perfect expression of the resistivity curve, illustrating the overwhelming influence of bulk conduction on chargeability. However, the normalized chargeability shows a significant decrease with increasing resistivity (i.e., MN increases with increasing conductivity). The data are replotted as MN against bulk conductivity σ, to compare with the plot of MN versus σ w (Figure 2c) plotted by Lesmes and Frye (2001). Given the formation factor of 6.3, which is the ratio σ w /σ, our equivalent range of fluid conductivity is from 2 to 230 ms/m. The shape of our data set agrees with that of Lesmes and Frye (2001) over the investigated range. The significant clay content in the investigated sandstone core causes the larger values of MN in Figure 2c. The increase in MN is considered the response of an increased surface charge density with increasing solute concentration, as postulated by Lesmes and Frye (2001). Vinegar and Waxman (1984) observed the same surface conductivity response for measurements of complex conductivity on shaly sandstones. Effect of lithology (clay content) The value of the normalized chargeability parameter MN in the interpretation of IP data in environmental investigations is further demonstrated in the results of a simple laboratory experiment designed to determine the IP response of sand/clay mixtures with varying percentages by weight of bentonite clay. Sand/clay mixtures were prepared and placed in a laboratory sampler for resistivity and IP measurements. Sample properties and dimensions are given in Figure 5b. In each case, the sample was saturated with tap water (σ w = 9.0 ms/m). Figure 5a shows the ρ, M, and MN parameters plotted versus the clay content. The ρ data show no clear correlation with clay content. The M data closely follow the ρ data, again showing no clear correlation with clay content. Given the expected relationship between cation exchange capacity (CEC) and IP effect (Vinegar and Waxman, 1984) a positive correlation between clay content and MN would be expected. The normalized chargeability does display such a relationship to clay content with R 2 (the goodness of fit of the line) equal to 0.97 (Figure 5a). In this experiment, in-situ fluid conductivity (σ w ) varied after saturation with tap water. Fluid conductivity was affected by ion dissolution, particularly from the bentonite. The amount of dissolution varied probably as a function of the time that the pore solution had to equilibrate with the sample. In addition, the porosity (not measured) of the sand/clay samples may have a complex relation to clay content (Knoll, et al., 1994). These factors control the resistance of the electrolytic conduction pathway, which has a strong effect on M. The important result of this simple experiment is the strong correlation observed between MN and clay content. Changes in structural properties (clay content) are clear in the response of MN but unclear in the response of M. This is particularly relevant to the interpretation of field studies, where changes in the fluid conductivity, saturation, and porosity are generally unknown. FIELD MEASUREMENTS Example 1: Crescent Beach State Park, Maine A time-domain IP survey was used to investigate the structure of the freshwater-saltwater interface at Crescent Beach State Park, near Portland, Maine (Slater and Sandberg, 2000). Figure 6a shows apparent resistivity (ρ a ), apparent chargeability (M a ), and apparent normalized chargeability (MN a ) pseudosections for a dipole-dipole survey line extending from 20 m above the high tide mark to 60 m below the high tide mark. Measurements were made using a Phoenix V5 receiver; electrode separation was 1 m with measurements made to n = 6 levels. The data were inverted using a 2-D regularized algorithm based on LaBrecque (1991). The forward problem is solved using the finite element method, and a resistivity/ip solution is FIG. 5. (a) Dependence of resistivity ( ρ ), chargeability (M), and normalized chargeability (MN) on percentage clay content for sand-bentonite mixtures. (b) Physical properties of the clean sand.

7 IP Interpretation in Environmental Investigations 83 found using an iterative least-squares approach. The algorithm minimizes a combination of data fit and model roughness to arrive at a geologically reasonable (smooth) model (LaBrecque et al., 1996). The chargeability M is calculated from the predicted subsurface resistivity model, in a linear step, after the definition of Seigel (1959). Oldenburg and Li (1994) compared this approach with two other (theoretically more robust) approaches to IP inversion and found that, in the absence of very high chargeabilities, the method performed well. Inverted values of MN were obtained from normalizing the inverted M parameters by the inverted ρ parameters (multiplication by σ ), using equation (15). The inverted models are shown in Figure 6b. The freshwater-saltwater interface is revealed as the sharp resistivity transition immediately up beach from the high tide mark (Figure 6). Two auger holes were drilled (through uniform fine sand) to a depth of 0.5 m to confirm the fluid conductivity change across this interface. Water samples indicate a factor of 100 change in fluid conductivity across this interface. The M a pseudosection and the M inversion display a change in chargeability across this interface. Immediately downbeach of 142 m, the chargeability is effectively zero because of the high salinity and consequent dominance of the electrolytic conduction pathway. Upbeach of 142 m, the chargeability increases up to a maximum of 9.6 mv/v in the inversion (Figure 6b). However, both the MN a pseudosection and the inverted MN model are uniform across this interface. A second chargeable zone, strongest at higher n levels, is observed between 160 and 190 m, downbeach of the freshwatersaltwater interface. This feature is not removed in the MN a pseudosection or the MN inversion. Although the ρ inversion reveals an increase in resistivity in this region, the MN inversion illustrates that this resistivity increase is not the cause of the high chargeability (Figure 6b). This indicates a change in surface conduction (i.e., polarization properties) due to a lithologic control. The polarizability of this zone has a strong effect on the chargeability, but a small effect on the resistivity, which is primarily determined by the properties of the electrolyte. To investigate the cause of this chargeable zone, a trial pit was dug at the location shown in Figure 6. Glacial till was encountered at a depth of 1 m. Laboratory measurements on sand and till samples from around Maine have shown the chargeability of till to be typically an order of magnitude greater than clean sand. The slight resistivity increase may be due to the lower porosity of the till. Figure 6b hence illustrates how the MN parameter assists in identification of IP signatures related to structural changes from IP signatures related only to changes in the electrolyte conductivity. Example 2: Beder aquifer, Aarhus County, Denmark Over the last 10 years, Danish scientists have concentrated on the geophysical characterization of the unconsolidated sands and clays that cover much of Denmark and contain/ protect important water supply aquifers. The data presented in Figure 7 were collected as part of an effort to investigate the value of IP in characterizing these sand/clay units in Aarhus County. The field data and 1-D inversion results for a Schlumberger resistivity/ip sounding conducted at Beder, Aarhus County, Denmark, are shown. Measurements were made using the Phoenix V5 resistivity/ip system. This sounding was performed at a site where good borehole control exists. Results of pulled array resistivity and transient EM soundings indicate that a 1-D layered earth model is a fair approximation at the location of this sounding. The resistivity and IP data were simultaneously inverted using the interpretation program EIN- VRT6 (Sandberg, 1996). The resulting 1-D resistivity/ip model is compared to resistivity and gamma (γ ) logs obtained at the center of the sounding in Figure 7. From the resistivity and γ logs, an important boundary between a clay-containing (low ρ, high γ count) layer and a deeper clay-reduced (high ρ, low γ count) zone occurs at 9m. This boundary is defined in the electrical model at 9.4 m. The γ log indicates additional smaller clay lenses at 10 m and m, which are not resolved in the modeling. The resistivity increases by a factor of 4 across this boundary (from layer 3 to layer 4), reflecting the ohmic conduction contribution due to the clay in layer 3. The chargeability is relatively insensitive to this boundary, increasing by a factor of 2 from layer 4 to layer 3. In contrast, the normalized chargeability is 7 times greater in layer 3 relative to layer 4, reflecting the enhanced surface polarization caused by the presence of the clay in layer 3. This field example substantiates the laboratory data shown in Figure 5 and further illustrates how the MN parameter helps resolve lithological changes in field IP surveys. The insensitivity of the basic chargeability M to this boundary again reflects the strong control of the ohmic conduction pathway on this field IP parameter. DISCUSSION Our experiments illustrate the value of the normalized chargeability parameter MN in improving interpretation of IP anomalies. Laboratory and field results demonstrate how MN assists in distinguishing IP effects due to predominantly electrolytic controls from effects due to structural (primarily clay content) variation. Laboratory results reveal the expected increase in MN associated with increasing polarizability due to increasing clay content in sand/clay mixtures saturated with tap water. The strong near-linear relationship between MN and percent clay content is similar to the relationships observed between σ and specific surface area or cation exchange capacity presented by other workers (Vinegar and Waxman, 1984; Knight and Nur, 1987; Borner and Schon, 1991; Sturrock et al., 1999). The field IP parameters (φ, PFE, M) are also related to the clay content, although in a nontrivial manner. Our laboratory experiments show that the chargeability is strongly correlated with the sample resistivity, but poorly correlated with the clay content (Figure 5). Previous investigators have observed similar nonlinear relationships between chargeability and clay content, with maximum IP effects observed at clay contents between 3% and 8% (Vacquier et al., 1957; Marshall and Madden, 1959; Ogilvy and Kuzmina, 1972; Klien and Sill, 1982). The optimal clay content, corresponding to the peak chargeability, depends upon clay type (cation exchange capacity). It is relatively low for montmorillonite, but higher for illite and kaolinite (Telford et al., 1990). The nonlinear relationship between chargeability and clay content generally results from a tradeoff between increasing polarization and decreasing resistivity

8 84 Slater and Lesmes with increasing clay content. Since the resistivity is a function of the salinity, porosity, and surface conductivity, which all vary with clay content, the relationship between chargeability and clay content can be highly variable (as observed in Figure 5). The laboratory data from the salinity and the clay experiments (Figures 4 and 5) are replotted in Figure 8 as the normalized chargeability versus the sample conductivity. In this figure, the clay-containing sands from the clay experiment are distinct from the clean sands of the salinity experiment. The only overlap occurs for the 0% clay content sample from the clay experiment, as expected. A conceptual description of the relationships between the field IP parameters and the normalized IP parameters is presented in Figure 9. This plot again illustrates how the normalized IP parameters help distinguish between salinity and clay content effects. Generally, clean sands will have low MN values, and clayey sands will have high MN values. There is some salinity dependence on the MN values, but the clay effects dominate the salinity effects. Partial saturation, which was not investigated in this study, is expected to lower the conductivity and therefore enhance the charge- ability (Olorunfemi and Griffiths, 1985). However, if sufficient water is available to establish an electrochemical surface phase, then the surface polarization and the normalized IP parameters should be relatively insensitive to the effects of partial saturation (Knight and Nur, 1987; Vanhala and Soininen, 1995). Therefore, partially saturated samples containing clay should be characterized by high MN values and low conductivity, as shown in Figure 9. We assume that, at low frequencies, the imaginary conductivity is only a function of the surface polarization, which is controlled by the microgeometry and the surface chemical properties of the sample. The surface chemistry is a function of the mineralogy and the pore fluid chemistry. In this experiment, we measured the complex conductivity of relatively clean sand as a function of the pore fluid salinity. We also measured the complex conductivity response of the sand with varying clay content when saturated with tap water. These results in00 were dicated that the affects of varying clay content on σsurf more significant than the affects of varying salinity. However, pore fluid concentration can significantly affect the surface FIG. 6. (a) Pseudosections of apparent resistivity (ρa ), apparent chargeability (Ma ), and apparent normalized chargeability (MNa ), Crescent Beach State Park, Maine. (b) 2-D inversions of resistivity (ρ), chargeability (M), and normalized chargeability (MN), Crescent Beach State Park, Maine. TP1 marks the location of a trial pit, and A marks the location of auger holes where the water salinity was measured. (Continued).

9 IP Interpretation in Environmental Investigations properties and microgeometry (through swelling) of clay minerals (Vinegar and Waxman, 1984; Samstag and Morgan, 1991; J. Roberts, 2000, personal communication). The data of Vinegar and Waxman (1984) suggest that the effects of vary00 become more pronounced with increasing ing salinity on σsurf clay content. Improved physiochemical models, which account for both grain and membrane polarization mechanisms, should be developed in order to better understand the coupled effects of varying clay content and pore fluid chemistry on the complex electrical properties of rocks and soils. This understanding is particularly important for the effective interpretation of field IP data where clay content and pore fluid chemistry can be expected to vary simultaneously. One-dimensional and two-dimensional IP modeling is often performed using the conventional IP parameters (M, PFE, and φ). It therefore seems appropriate to divide the inverted chargeability (M) parameters by the resistivity (ρ) parameters to obtain normalized IP (MN) parameters. This approach was applied to the interpretation of the 2-D Crescent Beach data set and the 1-D sounding data from the Beder site. The Crescent beach data set indicates that the normalization is appropriate to both pseudosection data and inverted model parameters. It is encouraging that the MN model is devoid of structure across the freshwater-saltwater interface, in agreement with the MNa pseudosection calculated from the Ma and ρa data. Further- 85 more, the 1-D Beder inversion produced a result that compares favorably with borehole logs. In our field examples, IP structure resulting from electrolytic controls is absent in the MN inversions, whereas effects due to structural changes are either maintained or enhanced. This does not detract from the potential use of IP in the exploration for zones of saline intrusion (see for example, Seara and Granda, 1987). The characteristic IP response due to saline intrusion is low normalized chargeability associated with low resistivity. The value of the IP measurement is that it helps to distinguish saline zones from clay-rich zones, which exhibit low resistivity but relatively high chargeability. The source of IP anomalies is conveniently determined by plotting the normalized chargeability parameter. In Figure 10a, the inverted model parameters from the Crescent beach data are plotted as (left) M against σ, and (right) MN against σ (as in Figures 8 and 9). Comparison of the two plots again illustrates how MN discriminates between polarization and bulk conduction effects. The large chargeabilities caused by high resistivities in the freshwater zone are of comparable magnitude to the chargeabilities associated with the till/clay. However, in the plot of MN versus σ, the polarizable till is clearly identified from the low polarizability sand. Figure 8 suggests that MN is generally more sensitive to clay content than to electrolyte conductivity. The increase in FIG. 6. (Continued).

10 86 Slater and Lesmes MN with σ, observed in the laboratory salinity experiments (Figure 4c), is also observed in the field data. However, because the salinity effect is much smaller than the clay content effect, it is not resolvable on the scale of Figure 10. We characterized the spatial distribution of lithology at Crescent Beach based on the graph of MN versus σ in Figure 10a. MN values less than 0.25 ms/m are considered representative of sand, whereas values greater than 0.25 ms/m are considered representative of till. The inverted data are then assigned a binary value to indicate either clay or till. This binary image is plotted in Figure 10b with blue representing sand and red representing till. We believe the image is a good representation of the sand/till thickness between 175 and 190 m, as the imaged till interface agrees with the depth to till (1 m) obtained at the trial pit location. The near-surface till zones between 155 and 167 m are probably artifacts of the inversion and relatively simple image processing applied here. Although only a simple form of image processing was appropriate here, the example illustrates the value of MN in subsurface lithological characterization. In this study, crossplots of normalized chargeability versus conductivity magnitude are useful for discriminating between lithologies in an IP survey. However, variations in salinity are a complication that we did not fully address in this study. An improved understanding of the physiochemical mechanisms controlling the IP response should enhance the usefulness of this interpretation method. FIG. 8. Plot of normalized chargeability (MN) versus conductivity ( σ ), showing how MN assists identification of clayey samples from clean sands. FIG. 7. Result of resistivity-ip sounding at Beder, Aarhus County, Denmark. (a) Model result with borehole log and lithologic interpretation shown for comparison. (b) Resistivity and IP model curves compared to field data.

11 IP Interpretation in Environmental Investigations CONCLUSIONS The IP method is a valuable geophysical tool in environmental investigations as it provides unique information on the strength of low-frequency polarization occurring in the subsurface. Understanding of the significance of the IP response due to surface polarization effects has been hindered by the dependency of the conventional IP parameters on both electrolytic and structural properties. The adoption of the normalized (by resistivity) IP parameter assists in the clarification of FIG. 9. Conceptual relationship between IP parameters, fluid conductivity, clay content partial saturation. 87 the cause of IP anomalies and leads to stronger relationships between structural properties (primarily clay content) and IP measurements. The normalized parameters are closely related to the low-frequency dielectric constant or quadrature conductivity. Surface polarization processes at the grain-fluid interface control the normalized IP parameters. The magnitude of this surface polarization, and hence the normalized chargeability, depends on the specific surface area, surface charge density, and surface ionic mobility. Fluid conductivity exerts an indirect control on normalized chargeability through its effect on surface charge density and surface ionic mobility. In this paper, laboratory and field examples illustrate the contrasting effects of electrolytic and structural factors on the IP method and how they were conveniently resolved using normalized IP parameters, such as the normalized chargeability MN. We used plots of MN against bulk conductivity σ to identify distinct responses from two lithologic zones (sand and till). Segregation of inverted model parameters into these zones allowed a simple form of image processing, resulting in a 2-D section of lithologic variability. In most environmental applications of IP, the intention is likely to be the determination of changes in structural parameters and how they relate to such fundamental properties as hydraulic conductivity. Modification of surface polarization due to organic chemical interaction with clay minerals is another promising application (Olhoeft, 1985). Normalized IP parameters, which are a direct measure of surface polarization processes, are obtainable from currently available time-domain and frequency-domain field instruments. Consequently, the IP method is a powerful and practical tool for imaging subsurface geochemical and hydrological environments, and it will see wider use as interpretation techniques continue to improve. FIG. 10. (a) Plots of chargeability (M) and normalized chargeability (MN) versus conductivity ( σ ) for inverted Crescent Beach survey parameters. Parameters with MN > 0.25 ms/m are defined as till/clay, and those with MN < 0.25 ms/m are defined as sand. (b) Binary lithological image at Crescent Beach based on the zoning in (a).

12 88 Slater and Lesmes ACKNOWLEDGMENTS This work was partially supported by U.S. Department of Energy and The Maine Science and Technology Foundation via Cooperative Agreements and by U.S. Department of the Interior: Geological Survey Stewart Sandberg (University of Southern Maine) assisted with the field studies, provided 1-D resistivity-ip software, interpreted the sounding data, and provided general encouragement for this project. Lee Slater thanks Kurt Sorensen (Geophysical Laboratory, Earth Sciences, Aarhus University, Denmark) for providing the borehole log for the Beder field site. University of Southern Maine intern students Stacy Towne, Dan Glaser, Al French, Bryant Vandervelde, and Tony Robinson assisted with field data collection. David Lesmes thanks Dale Morgan for sharing with him his insights on IP and surface chemistry. 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