TY - JOUR
AU1 - Revil,, A.
AU2 - Le Breton,, M.
AU3 - Niu,, Q.
AU4 - Wallin,, E.
AU5 - Haskins,, E.
AU6 - Thomas,, D.M.
AB - SUMMARY We performed complex conductivity measurements on 28 core samples from the hole drilled for the Humu'ula Groundwater Research Project (Hawai'i Island, HI, USA). The complex conductivity measurements were performed at 4 different pore water conductivities (0.07, 0.5, 1.0 or 2.0, and 10 S m−1 prepared with NaCl) over the frequency range 1 mHz to 45 kHz at 22 ± 1 °C. The in-phase conductivity data are plotted against the pore water conductivity to determine, sample by sample, the intrinsic formation factor and the surface conductivity. The intrinsic formation factor is related to porosity by Archie's law with an average value of the cementation exponent m of 2.45, indicating that only a small fraction of the connected pore space controls the transport properties. Both the surface and quadrature conductivities are found to be linearly related to the cation exchange capacity of the material, which was measured with the cobalt hexamine chloride method. Surface and quadrature conductivities are found to be proportional to each other like for sedimentary siliclastic rocks. A Stern layer polarization model is used to explain these experimental results. Despite the fact that the samples contain some magnetite (up to 5 per cent wt.), we were not able to identify the effect of this mineral on the complex conductivity spectra. These results are very encouraging in showing that galvanometric induced polarization measurements can be used in volcanic areas to separate the bulk from the surface conductivity and therefore to define some alteration attributes. Such a goal cannot be achieved with resistivity alone. Electrical properties, Hydrogeophysics, Hydrothermal systems, Permeability and porosity 1 INTRODUCTION Large scale imaging of resistivity have been successfully conducted in a number of volcanic environments for the purpose of finding water (Lénat et al. 2000), delimiting hydrothermal systems (Revil et al. 2010), and assessing geohazards associated with potential flank collapses (Aizawa et al. 2009). At large scales, deep resistivity surveys offer a unique approach of providing information on how magma and hydrothermal fluids are stored (Ogawa et al. 1999; Matsushima et al. 2001). Two approaches have been used to obtain electrical resistivity in the field. The first approach involved passive and/or active induction-based electromagnetic methods such as the magnetotelluric and audiomagnetotelluric methods (e.g. Ogawa et al. 1999; Matsushima et al. 2001). The second approach is galvanometric and corresponds to the so-called electrical resistivity tomography (ERT) method. In the last decade, very long (up to 2.5 km) resistivity cables have been developed to image small volcanoes (Revil et al. 2010, 2011; Rosas-Carbajal et al. 2016). Direct current (dc) resistivity has also recently benefited from new developments in imaging algorithms (e.g. Günther & Rücker 2006a,b; Johnson et al. 2010; Wallin et al. 2013; Zhou et al. 2014). The interpretationof resistivity imaging inherently remains difficult. This is because of the resistivity of rocks is affected by the water content, alteration (through the clay content and the clay mineralogy), salinity of the pore water, and temperature (e.g. Vinegar & Waxman 1984; Revil 2012). At the very least, electrical conductivity comprises two contributions. The first contribution is associated with conduction in the bulk pore water. The second contribution, called surface conductivity, is associated with conduction in the electrical double layer coating the surface of the grains (Vinegar & Waxman 1984). This electrical double layer comprises a diffuse layer of counterions (called the Gouy-Chapman layer, e.g. Chapman 1913) and a compact layer of sorbed counterions called the Stern layer (Davis et al. 1978; Revil 2012). Magnetite can also play a role on the electrical conductivity of volcanic rocks as discussed for instance by Drury & Hyndman (1979) and Ildefonse & Pezard (2001). With only resistivity data, there is no way to separate surface from bulk conductivities at a given pore water conductivity and therefore to analyze quantitatively resistivity tomograms. Induced polarization is a geophysical method used to characterize the reversible storage of electrical charges in rocks (e.g. Chelidze & Guégen 1999), and is therefore a natural extension of the galvanometric DC resistivity method (e.g. Börner et al. 1996). Each volume of a rock behaves approximately as a resistance in parallel with a small capacitance (Vinegar & Waxman 1984). In induced polarization, the conductivity can be therefore represented as a complex number with a in-phase (real) component representing true conduction (electromigration of the charge carriers) and a quadrature (imaginary) component representing true polarization through the coupling of the current density and the diffusion density. The POLARIS model has been developed by Revil et al. to capture the underlying physics behind the induced polarization of sedimentary rocks. This model is based on the polarization of the inner part of the electrical double layer coating the mineral grains and called the Stern layer. This polarization may be enhanced by the presence of the diffuse layer as discussed recently by Niu & Revil (2016). The POLARIS model has been able to successfully explain the dependence of induced polarization parameters on salinity (Revil & Skold 2011; Niu et al. 2016), on pore water chemistry (Vaudelet et al. 2011), and on cation exchange capacity (CEC) and specific surface area of the material (Revil 2012; Revil et al. 2015a,b). Induced polarization spectra can be also related to permeability (Slater & Lesmes 2002; Binley et al. 2005; Titov et al. 2010; Revil et al. 2015a,b) and pore size distribution (see Tong et al. 2006; Revil et al. 2014; Niu & Revil 2016). To date, the POLARIS model has only been tested on sedimentary rocks. Our goal in this paper is to test this model on volcanic rocks to further extend results on electrical conductivities already published on volcaniclastic rocks (Revil et al. 2002), basalts (Pezard 1990), gabbros (Pezard et al. 1991; Ildefonse et al. 1999) and peridotites (Ildefonse et al. 1999; Ildefonse et al. 2000). To the best of our knowledge, there is not a single published work dealing with induced polarization of volcanic rocks. The samples used in this study are basalts from the Humu'ula Groundwater Research Project (HGRP, Fig. 1). Magnetotelluric and audiomagnetotelluric (MT and AMT) surveys conducted in 2004 and 2008 in the Humu'ula saddle region between Mauna Kea and Mauna Loa volcanoes on the island of Hawai'i (Pierce & Thomas 2009) revealed evidence of high-elevation groundwater as well as deeper, more conductive materials. The elevation of the inferred groundwater levels was much higher than predicted by the prevailing model for this region (Bauer 2003, and references therein). In order to more fully investigate the implications of these geophysical surveys, the HGRP drilling program was initiated to gain a better understanding of the groundwater resources in the Humu'ula saddle region of Hawaii Island. Figure 1. Open in new tabDownload slide Position of HGRP1 (Hole 1) in the Humu'ula saddle region between Mauna Kea and Mauna Loa volcanoes on the island of Hawai'i (Hawaii, HI, USA). This hole was drilled by the University of Hawaii and the 28 samples used in this study are from this well. Figure 1. Open in new tabDownload slide Position of HGRP1 (Hole 1) in the Humu'ula saddle region between Mauna Kea and Mauna Loa volcanoes on the island of Hawai'i (Hawaii, HI, USA). This hole was drilled by the University of Hawaii and the 28 samples used in this study are from this well. A total of 28 sampleswere used for the present investigation. They were extracted from Hole 1 drilled for the HGRP project (Fig. 1). These samples were characterized in terms of a broad range of petrophysical properties including complex conductivity measurements at 4 distinct pore water conductivities, porosity, grain density, X-ray (XRD)-based mineralogy and CEC measurements. Their mineralogy (including the presence of magnetite and varying concentration of clays) and texture are highly variables making this type of rock extremely challenging in terms of deciphering their complex conductivity spectra. Their permeability and pore size distribution are analyzed in the second paper of this series as well as their potential control regarding the complex conductivity spectra. This results in a unique dataset that can be used to assess the reliability of the POLARIS model for volcanic rocks. In this paper, we are especially interested in looking at the relationships between (1) surface conductivity and (2) CEC, quadrature conductivity and CEC and (3) to know if we can predict surface conductivity from quadrature conductivity. 2 THEORY We first describe the Stern layer polarization model used to interpret the experimental data obtained in Section 4. As discussed by Vinegar & Waxman (1984) and Revil (2013a), this model is valid up to 300 kHz. This model has been developed and refined in a number of papers including those of Leroy et al. (2008), Revil (2013b), Revil et al. (2014, 2015a,b) and references therein. Alternative models such as membrane polarization-based models exist (see Titov et al. 2002; Bücker & Hördt 2013a,b, and references therein) that will not be described in this paper. In both types of models, the coupling mechanism is related to the cross-coupling effect between the ionic fluxes and both the electrical field and the gradient of the chemical potential of the charge carriers. A unified model is, however, still missing. We favor here the Stern layer polarization model because in the last 10 yr, its heuristic power has been demonstrated over and over. 2.1 Complex surface conductivity model The complex conductivityof porous material is written as σ* = σ' + iσ'', where σ' denotes its in-phase conductivity and σ'' (<0) denotes the quadrature conductivity (both expressed in S m−1) and i denotes the pure imaginary number (with i2 = –1). The in-phase conductivity describes the ability of the material to carry an electrical current under the action of an electrical field. Conduction occurs both in the bulk pore water and in the electrical double layer coating the surface of the minerals. The quadrature conductivity describes the ability of the material to reversibly store electrical charges. The associated polarization mechanism found its root in the ionic concentration disturbances associated with the polarization of the electrical double layer and disturbances in the formation of neutral clouds of ions around the grains or the pores (see Revil & Florsch 2010, for a complete description of these different mechanisms). According to the POLARIS model, the main polarization mechanism is the polarization of the Stern layer coating the surface of the mineral grains. Surface conduction and polarization can be understood in terms of a complex surface conductivity σSS* (in S m−1, e.g. Niu et al. 2016) while the bulk conductivity of the pore water, σw (in S m−1), is frequency independent. According to the POLARIS model, the high salinity asymptotic behavior of the complex conductivity is given by, \begin{equation}\sigma^* = \frac{1}{F}{\sigma _{\rm{w}}} + {\sigma _{SS}}^* - {\rm{i}}\omega {\varepsilon _\infty },\end{equation} (1) where ω is the pulsation frequency, ε∞ denotes the high frequency permittivity (actually the low-frequency component of the Maxwell–Wagner polarization and not the high frequency dielectric permittivity, see Revil 2013a,b) the intrinsic formation factor F (dimensionless) is related to the connected porosity ϕ (dimensionless) by a power law relationship called Archie's law F = ϕ− m where m (dimensionless) denotes the so-called cementation exponent (Archie 1942). Eq. (1) is only a high salinity linear approximation of a more complex non-linear equation based on the differential effective medium theory as discussed for instance by Niu et al. (2016). By high salinity, we mean that Du >> 1 where Du denotes the Dukhin number defined as the ratio of the surface conductivity (the real part of σSS*) to the pore water conductivity. The complex conductivity spectra should incorporate the heterogeneity of the porous materials in terms of pore size or grain size distributions (e.g. Revil & Florsch 2010). The specific surface conductivity is written through the following convolution product (Revil et al. 2014) \begin{equation}{\sigma _{SS}}^* = \sigma _\infty ^{SS} - \left( {\sigma _\infty ^{SS} - \sigma _0^{SS}} \right)\int\limits_0^\infty {\frac{{h(\tau )}}{{1 + {{\left( {{\rm{i}}\omega \tau } \right)}^{1/2}}}}} {\rm{d}}\tau ,\end{equation} (2) where h(τ) denotes the probability density distribution of the relaxation time τ, which can be in turn related to the distribution of the pore sizes (see Revil et al. 2014). This relationship will be extensively investigated in the second paper of this series. In the POLARIS model, the low- and high-frequency limits of the surface conductivity entering eq. (2) are given by (Revil 2013a,b), \begin{equation}\sigma _0^{SS} = \left( {\frac{1}{{F\phi }}} \right){\rho _g}{\beta _{( + )}}(1 - f){\rm{CEC,}}\end{equation} (3) \begin{equation}\sigma _\infty ^{SS} = \left( {\frac{1}{{F\phi }}} \right){\rho _g}\left[ {{\beta _{( + )}}(1 - f) + \beta _{( + )}^Sf} \right]{\rm{CEC,}}\end{equation} (4) respectively, and where f (dimensionless) denotes the partition coefficient describing the fraction of counterions in the Stern layer, ρg is the grain density (in kg m−3), and CEC is the CEC (in C kg−1). The values of the mobility of ions in diffuse layer of sedimentary rocks β( + )(Na+, 25 °C) is 5.2 × 10−8 m2 s−1 V−1, respectively (see Revil 2012), that is, the value of the mobility of the counterions in the diffuse layer is the same than in the free water. The partition coefficient f is defined as the ratio between the surface concentration of counterions in the Sternlayer over the total surface concentration of counterions in both the diffuse and Stern layers. In other words, 0 ≤ f ≤ 1 denotes the fraction of counterions in the Stern layer. For clay minerals, the value of f depends on the clay mineralogy with typical value of f ≈ 0.80–0.95 for illite and smectite and f ≈ 0.98–0.99 for kaolinite. These values can be independently determined either from a surface complexation model combined with an electrical double layer model (e.g. Leroy et al. 2008, for silica; Leroy & Revil 2009, and Niu et al. 2016 for clay minerals) or from various measurements involving the electrical double layer such as for instance osmotic pressure measurements (Gonçalvès et al. 2007). We can also define the excess of exchangeable cations (in equivalent charge) per unit pore space (C m−3) as \begin{equation}{Q_V} = {\rho _g}\left( {\frac{{1 - \phi }}{\phi }} \right){\rm{CEC}}{\rm{.}}\end{equation} (5) Then the in-phase and quadrature conductivities can be related to this excess charge density (see Revil 2013a) \begin{equation}\sigma _0^{SS} = \frac{1}{F}{\beta _{( + )}}(1 - f){Q_V},\end{equation} (6) \begin{equation}\sigma _\infty ^{SS} = \frac{1}{F}\left[ {{\beta _{( + )}}(1 - f) + \beta _{( + )}^Sf} \right]{Q_V}.\end{equation} (7) Eqs (6) and (7) are not exactly consistent with eqs (3) and (4) and are generally obtained with different assumptions (see Revil 2013a, for details). When using an apparent mobility|$B = {\beta _{( + )}}(1 - f) + \beta _{( + )}^Sf$|⁠, eq. (7) becomes similar to the conductivity equation σS = BQV/F proposed empirically by Waxman & Smits (1968) for high porosity sedimentary rocks. Eqs (3) and (4) are developed using a volume-averaging approach (used as upscaling technique) and are valid whatever the porosity. 2.2 Relaxation times We consider the Cole–Cole model (Cole & Cole 1941; Tarasov & Titov 2013) as an example of the spectral response, since often a response similar to a Cole–Cole model response is observed. In this case, the complex conductivity in the frequency-domain is written as: \begin{equation}\sigma^*(\omega ) = {\sigma _\infty }\left[ {1 - \frac{M}{{1 + {{\left( {{\rm{i}}\omega {\tau _0}} \right)}^c}}}} \right] - {\rm{i}}\omega {\varepsilon _\infty },\end{equation} (8) where τ0 is the Cole–Cole time constant, c is the Cole–Cole exponent (typically between 0 and 0.5, the Cole–Cole exponent c can be understood as describing the broadness of the pore size distribution, see Revil et al. 2014), M = (σ∞ − σ0)/σ∞ is the dimensionless chargeability (the normalized chargeability is Mn = Mσ∞ = σ∞ − σ0 expressed in S m−1), σ0 and σ∞ denoting the DC (ω = 0) and high-frequency electrical conductivities, respectively, which can be easily connected to eqs (3) and (4) or (6) and (7). Our data will not be fitted by a Cole–Cole model, which is only used here as an example of spectral function. A more advanced approach (based on eq. 2) will be used in Part 2 to fit the spectral response of our data. Initially, we developed a grain-size based polarization model to connect the relaxation time τ0 to the mean grain size of the material (Revil & Florsch 2010). Later, we realized however that the relaxation time was better directly related to the mean pore size (Revil et al. 2012). This observation was modeled recently by Niu & Revil (2016). This new model predicts that the characteristic relaxation time, τ0, is associated with a characteristic pore size Λ according to \begin{equation}{\tau _0} = \frac{{{\Lambda ^2}}}{{2MD_{{\rm{( + )}}}^S}},\end{equation} (9) where |$D_{{\rm{( + )}}}^S$| denotes the diffusion coefficient of the counterions in the Stern layer (expressed in m2 s−1). The value of this diffusion coefficient |$D_{{\rm{( + )}}}^S$| should related to the mobility of the counterions in the Stern layer, |$\beta _{( + )}^S$|⁠, by the Nernst-Einstein relationship |$D_{{\rm{( + )}}}^S = {k_b}T\beta _{{\rm{( + )}}}^S/| {{q_{{\rm{( + )}}}}} |$|⁠, where T denotes the absolute temperature (in K), kb denotes the Boltzmann constant (1.3806 × 10−23 m2 kg s−2 K−1), |q( + )| is the charge of the counterions in the Stern layer coating the surface of the grains (|q( + )| = e where e is the elementary charge for Na+). Finally, the parameter M accounts for the influence of the exchange of counterions between the diffuse and Stern layer (see Lyklema et al. 1983, Niu & Revil 2016). 2.3 Relationship between surface and quadrature conductivity If we consider the real part of the terms entering eq. (1), we get (e.g. Waxman & Smits 1968; Revil et al. 1996; Revil 2013a,b), \begin{equation}\sigma ' = \frac{1}{F}{\sigma _{\textit w}} + {\sigma _S}.\end{equation} (10) where σS (in S m−1) denotes the surface conductivity of the material. As discussed by Niu et al. (2016), this macroscopic surface conductivity is not necessarily the same than the equivalent conductivity of the grains (σSS). Eq. (10) will be obtained below to determine form the experimental data the intrinsic formation factor and the surface conductivity. Using the Cole–Cole model described previously, the quadrature conductivity and the normalized chargeability are approximately related to each other by (see Revil et al. 2015a,b,c, for details of the derivation) \begin{equation}\sigma '' \approx - \frac{{{M_n}}}{\alpha },\end{equation} (11) \begin{equation}{M_n} = \left( {\frac{1}{{F\phi }}} \right){\rho _S}\beta _{( + )}^Sf\,{\rm{CEC,}}\end{equation} (12) and with α typically in the range 5–10. Finally, in the recent literature, several authors have discussed the possibility of predicting surface conductivity from the measurement of the quadrature conductivity. This idea was first proposed by Revil (2013b) and Weller et al. (2013) and extended by Woodruff et al. (2014) to anisotropic oil and gas shales. Following Revil et al. (Revil et al. 2015a,b,c), we use the following definition for the dimensionless number R: \begin{equation}{\rm{R}} \equiv \frac{{{M_n}}}{{{\sigma _S}}} \approx - \alpha \left( {\frac{{\sigma ''}}{{{\sigma _S}}}} \right),\end{equation} (13) and therefore we have − σ''/σS = R/α. This ratio R can be related to the partition coefficient f (e.g. Revil et al. 2015a,b,c) by \begin{equation}{\rm{R}} = \frac{{\beta _{( + )}^Sf}}{{\left[ {{\beta _{( + )}}(1 - f) + \beta _{( + )}^Sf} \right]}}.\end{equation} (14) We will test the relationship between the quadrature and surface conductivity for our volcanic core samples. We will develop a new procedure to get the normalized chargeability in Paper 2. This procedure will involve using the pore size distribution from mercury porosimetry. 3 ORIGIN OF THE CORE SAMPLES The samples used in this study are from Hole 1 of the Humu'ula Groundwater Research Project (HGRP) drilling (Hawai'i). Hole 1 was drilled by the University of Hawaii, using a truck-mounted Boart Longyear LF230 diamond core drill rig. It is located on the U.S. Army's Pōhakuloa Training Area (PTA) parade ground (lat 19; 41; 44.22, long 155; 32; 57.48) and ∼1950 m amsl. Drilling of Hole 1 began on 2013 March 3 and finished on June 21, with a drilling hiatus from 2013 April 21 to May 18. The hole recovered a continuous stratigraphic sequence of subaerial shield-stage and postshield-stage rock samples sourced from Mauna Kea volcano. Cores were usually transported twice daily from the drill site to UH Hilo for processing. Logging of the core also took place at UH Hilo, and lasted for a few months after core processing was completed. Once logged, boxes of rock core were brought back to the arid environment of PTA for storage and later sampling. The final depth of Hole 1 is 1764.5 m, with a recovery rate of 93.3 per cent. In general, recovery increased with depth and was very high (98.5 per cent) below 600 m. Hole 1 is saturated with groundwater below ∼550 m depth, with a series of at least six shallower perched aquifers of variable thicknesses (up to ∼100 m) from ∼170 to 500 m. Drilling could not proceed beyond 1764.5 m to the goal of sea level because of elevated heat in the lower half of the borehole. Below ∼1000 m, downhole temperature was monitored daily during drilling, and once the drilling permit temperature limit of 100 °C was reached, drilling operations at Hole 1 ceased (Thomas & Haskins 2013). A total of 28 samples from Hole 1 were collected for this study, from a variety of depths (Table 1). Mostly shield-stage (>250 ka) Mauna Kea lavas were sampled, because that material makes up most of the borehole (Lautze et al. 2013). A few postshield-stage Laupahoehoe (4–65 ka) and Hamakua (65–250 ka, ages from Wolfe et al. 1997) a΄a and pahoehoe lavas and intrusive rock samples were also taken, many from within the same units chosen for the preliminary geochemical reference suite. Table 1. Characteristics of the 28 samples used in this study (the name is based on the core box). Quantitative meaning of the terms used in the description of the core samples: aphyric <1 per cent phenocryst, sparsely 1–2 per cent phenocrysts, moderately 3–10 per cent phenocrysts, highly >10 per cent phenocrysts. All the samples were collected from the archived core on 04/10/2014. Sample Sample ID Unit Top depth (m) Comment HG7 SR010-2.2 U0008 20.94 Sparsely plagioclase-phyric hawaiite HG44 SR042-2.7 U0014 100.80 Aphyric hawaiite intrusion (laccolith or lopolith) HG78 SR057-4.1 U0017 138.71 Moderately olivine plagioclase-phyric a΄a HG137 SR095-3.0 U0031 209.09 Aphyric a΄a HG157 SR105-5.8 U0036 234.33 Aphyric a΄a HG179 SR0122-0.6 U0039 264.75 Intrusion (dike) HG279 SR0172-2.1 U0050 381.03 Aphyric a΄a HG341 SR0194-3.4 U0054 445.43 Intrusion (dike) HG365 SR0203-4.0 U0061 470.00 Aphyric a΄a HG444 SR0233-4.6 U0081 558.58 Aphyric a΄a HG463 SR0242-7.0 U0085 582.47 Aphyric a΄a HG482 SR0249-2.3 U0091 601.77 Picrite HG524 SR0263-1.1 U0104 644.83 Aphyric pahoehoe HG552 SR0273-0.2 U0108 673.06 Aphyric a΄a HG554 SR0273-9.4 U0109 675.74 Aphyric a΄a HG583 SR0284-0.3 U0120 706.62 Intrusion (dike) HG623 SR0298-2.8 U0134 750.05 Aphyric pahoehoe HG723 SR0333-0.3 U0156 855.97 Aphyric pahoehoe HG761 SR0351-2.1 U0170 910.77 Aphyric a΄a HG785 SR0373-5.2 U0183 977.25 Nearly aphyric pahoehoe HG804 SR0392-3.1 U0200 1030.9 Highly olivine-phyric pahoehoe HG820 SR0407-3.8 U0210d 1074.8 Intrusion (dike) HG851 SR0440-1.8 U0244 1162.8 Aphyric a΄a HG904 SR0493-3.1 U0276 1316.8 Sparsely plagioclase-phyric a΄a HG919 SR0507-2.1 U0289a 1358.8 Aphyric pahoehoe HG921 SR0511-3.4 U0289c 1362.3 Aphyric pahoehoe HG924 SR0507-5.0 U0295 1371.8 Moderately olivine-phyric pahoehoe HG1058 SR0639-10.1 U0399 1763.6 Moderately plagioclase olivine-phyric pahoehoe Sample Sample ID Unit Top depth (m) Comment HG7 SR010-2.2 U0008 20.94 Sparsely plagioclase-phyric hawaiite HG44 SR042-2.7 U0014 100.80 Aphyric hawaiite intrusion (laccolith or lopolith) HG78 SR057-4.1 U0017 138.71 Moderately olivine plagioclase-phyric a΄a HG137 SR095-3.0 U0031 209.09 Aphyric a΄a HG157 SR105-5.8 U0036 234.33 Aphyric a΄a HG179 SR0122-0.6 U0039 264.75 Intrusion (dike) HG279 SR0172-2.1 U0050 381.03 Aphyric a΄a HG341 SR0194-3.4 U0054 445.43 Intrusion (dike) HG365 SR0203-4.0 U0061 470.00 Aphyric a΄a HG444 SR0233-4.6 U0081 558.58 Aphyric a΄a HG463 SR0242-7.0 U0085 582.47 Aphyric a΄a HG482 SR0249-2.3 U0091 601.77 Picrite HG524 SR0263-1.1 U0104 644.83 Aphyric pahoehoe HG552 SR0273-0.2 U0108 673.06 Aphyric a΄a HG554 SR0273-9.4 U0109 675.74 Aphyric a΄a HG583 SR0284-0.3 U0120 706.62 Intrusion (dike) HG623 SR0298-2.8 U0134 750.05 Aphyric pahoehoe HG723 SR0333-0.3 U0156 855.97 Aphyric pahoehoe HG761 SR0351-2.1 U0170 910.77 Aphyric a΄a HG785 SR0373-5.2 U0183 977.25 Nearly aphyric pahoehoe HG804 SR0392-3.1 U0200 1030.9 Highly olivine-phyric pahoehoe HG820 SR0407-3.8 U0210d 1074.8 Intrusion (dike) HG851 SR0440-1.8 U0244 1162.8 Aphyric a΄a HG904 SR0493-3.1 U0276 1316.8 Sparsely plagioclase-phyric a΄a HG919 SR0507-2.1 U0289a 1358.8 Aphyric pahoehoe HG921 SR0511-3.4 U0289c 1362.3 Aphyric pahoehoe HG924 SR0507-5.0 U0295 1371.8 Moderately olivine-phyric pahoehoe HG1058 SR0639-10.1 U0399 1763.6 Moderately plagioclase olivine-phyric pahoehoe Open in new tab Table 1. Characteristics of the 28 samples used in this study (the name is based on the core box). Quantitative meaning of the terms used in the description of the core samples: aphyric <1 per cent phenocryst, sparsely 1–2 per cent phenocrysts, moderately 3–10 per cent phenocrysts, highly >10 per cent phenocrysts. All the samples were collected from the archived core on 04/10/2014. Sample Sample ID Unit Top depth (m) Comment HG7 SR010-2.2 U0008 20.94 Sparsely plagioclase-phyric hawaiite HG44 SR042-2.7 U0014 100.80 Aphyric hawaiite intrusion (laccolith or lopolith) HG78 SR057-4.1 U0017 138.71 Moderately olivine plagioclase-phyric a΄a HG137 SR095-3.0 U0031 209.09 Aphyric a΄a HG157 SR105-5.8 U0036 234.33 Aphyric a΄a HG179 SR0122-0.6 U0039 264.75 Intrusion (dike) HG279 SR0172-2.1 U0050 381.03 Aphyric a΄a HG341 SR0194-3.4 U0054 445.43 Intrusion (dike) HG365 SR0203-4.0 U0061 470.00 Aphyric a΄a HG444 SR0233-4.6 U0081 558.58 Aphyric a΄a HG463 SR0242-7.0 U0085 582.47 Aphyric a΄a HG482 SR0249-2.3 U0091 601.77 Picrite HG524 SR0263-1.1 U0104 644.83 Aphyric pahoehoe HG552 SR0273-0.2 U0108 673.06 Aphyric a΄a HG554 SR0273-9.4 U0109 675.74 Aphyric a΄a HG583 SR0284-0.3 U0120 706.62 Intrusion (dike) HG623 SR0298-2.8 U0134 750.05 Aphyric pahoehoe HG723 SR0333-0.3 U0156 855.97 Aphyric pahoehoe HG761 SR0351-2.1 U0170 910.77 Aphyric a΄a HG785 SR0373-5.2 U0183 977.25 Nearly aphyric pahoehoe HG804 SR0392-3.1 U0200 1030.9 Highly olivine-phyric pahoehoe HG820 SR0407-3.8 U0210d 1074.8 Intrusion (dike) HG851 SR0440-1.8 U0244 1162.8 Aphyric a΄a HG904 SR0493-3.1 U0276 1316.8 Sparsely plagioclase-phyric a΄a HG919 SR0507-2.1 U0289a 1358.8 Aphyric pahoehoe HG921 SR0511-3.4 U0289c 1362.3 Aphyric pahoehoe HG924 SR0507-5.0 U0295 1371.8 Moderately olivine-phyric pahoehoe HG1058 SR0639-10.1 U0399 1763.6 Moderately plagioclase olivine-phyric pahoehoe Sample Sample ID Unit Top depth (m) Comment HG7 SR010-2.2 U0008 20.94 Sparsely plagioclase-phyric hawaiite HG44 SR042-2.7 U0014 100.80 Aphyric hawaiite intrusion (laccolith or lopolith) HG78 SR057-4.1 U0017 138.71 Moderately olivine plagioclase-phyric a΄a HG137 SR095-3.0 U0031 209.09 Aphyric a΄a HG157 SR105-5.8 U0036 234.33 Aphyric a΄a HG179 SR0122-0.6 U0039 264.75 Intrusion (dike) HG279 SR0172-2.1 U0050 381.03 Aphyric a΄a HG341 SR0194-3.4 U0054 445.43 Intrusion (dike) HG365 SR0203-4.0 U0061 470.00 Aphyric a΄a HG444 SR0233-4.6 U0081 558.58 Aphyric a΄a HG463 SR0242-7.0 U0085 582.47 Aphyric a΄a HG482 SR0249-2.3 U0091 601.77 Picrite HG524 SR0263-1.1 U0104 644.83 Aphyric pahoehoe HG552 SR0273-0.2 U0108 673.06 Aphyric a΄a HG554 SR0273-9.4 U0109 675.74 Aphyric a΄a HG583 SR0284-0.3 U0120 706.62 Intrusion (dike) HG623 SR0298-2.8 U0134 750.05 Aphyric pahoehoe HG723 SR0333-0.3 U0156 855.97 Aphyric pahoehoe HG761 SR0351-2.1 U0170 910.77 Aphyric a΄a HG785 SR0373-5.2 U0183 977.25 Nearly aphyric pahoehoe HG804 SR0392-3.1 U0200 1030.9 Highly olivine-phyric pahoehoe HG820 SR0407-3.8 U0210d 1074.8 Intrusion (dike) HG851 SR0440-1.8 U0244 1162.8 Aphyric a΄a HG904 SR0493-3.1 U0276 1316.8 Sparsely plagioclase-phyric a΄a HG919 SR0507-2.1 U0289a 1358.8 Aphyric pahoehoe HG921 SR0511-3.4 U0289c 1362.3 Aphyric pahoehoe HG924 SR0507-5.0 U0295 1371.8 Moderately olivine-phyric pahoehoe HG1058 SR0639-10.1 U0399 1763.6 Moderately plagioclase olivine-phyric pahoehoe Open in new tab The quantitative meaning of the terms used for the rock names are as follows: aphyric <1 per cent phenocrysts, sparsely 1–2 per cent phenocrysts, moderately 3–10 per cent phenocrysts and highly >10 per cent phenocrysts. If two minerals are mentioned in the rock name (e.g. moderately plagioclase olivine-phyric basalt), the abundance term refers to the second mineral (3–10 per cent olivine), and the first mineral (plagioclase) is present but less abundant than the second. If two terms are used (e.g. sparsely to moderately), this is because the phenocryst content varied across those ranges within the flow unit due to processes like olivine settling or internal flow mixing, which is common in pahoehoe flows. Due to the high degree of heat and hydrothermal circulation in the lower reaches of the hole, samples range from unaltered to quite highly altered. This broad range of alteration is very suitable for the complex conductivity measurements discussed below since it is expected to be responsible for a broad range of values for both surface and quadrature conductivities. This will be confirmed also by their CEC measurements and XRD analysis. 4 EXPERIMENTS 4.1 Porosity, density and CEC The physical-chemical properties of the 28 core samples are listed in Tables 2 and 3. The sample number corresponds to the depth of the core in the well. The CEC measurements were independently performed by Weatherford Laboratories in Golden, CO, USA. The (connected) porosity and density of the grains were determined using the volume and mass of the core samples (dry and saturated). Table 2. Properties of the 28 Samples (labels HG denotes the HGRP project) used in this study. The parameter ϕ denotes the connected porosity (dimensionless, precision on the order of 1 per cent in relative value), ρg the grain density (in kg m−3, precision on the order of 1 per cent in relative value), F the electrical (intrinsic) formation factor (dimensionless), σS the surface conductivity (in S m−1), and CEC denotes the cation exchange capacity (here given in meq/100 g with 1 meq/100 g = 1 cMol kg−1 = 963.2 C kg−1, standard deviation unknown). Sample HG583 does not have measurements at 0.5 and 1 Sm−1 because it had a crack. The formation factors and surface conductivities are determined form the conductivity of the rock plotted versus the pore water conductivity (see Figs 9 and 10). The relatively high grain density would mean that the non-connected porosity is relatively small. Sample Rock name ϕ (–) ρg (kg m−3) F (–) σS (10−4 S m−1) CEC (meq/100 g) HG7 Sparsely plagioclase-phyric hawaiite 0.114 2880 172 ± 6 4.5 ± 0.4 0.483 HG44 Aphyric hawaiite 0.065 2850 158 ± 16 3.1 ± 0.1 0.199 HG78 Moderately plagioclase olivine-phyric basalt 0.204 3010 44 ± 1 13.7 ± 1.1 0.256 HG137 Aphyric basalt 0.064 2990 305 ± 34 5.9 ± 1.1 0.199 HG157 Aphyric basalt 0.146 3050 176 ± 7 4.0 ± 0.5 0.482 HG179 Aphyric hawaiite 0.189 2080 56 ± 2 4.5 ± 0.9 0.312 HG279 Aphyric basalt 0.103 3040 240 ± 41 5.7 ± 1.9 0.398 HG341 Sparsely plagioclase-phyric hawaiite 0.198 2720 114 ± 7 4.4 ± 0.9 0.625 HG365 Aphyric to sparsely plagioclase-phyric basalt 0.080 2450 132 ± 67 3.3 ± 6.3 0.397 HG444 Aphyric to sparsely olivine-phyric basalt 0.131 2970 167 ± 1 5.9 ± 0.1 0.663 HG463 Aphyric to sparsely plagioclase-phyric basalt 0.129 3070 143 ± 6 14.4 ± 0.9 0.738 HG482 Moderately to highly olivine-phyric basalt 0.323 3070 33 ± 2 5.9 ± 2.4 0.454 HG524 Aphyric basalt 0.328 3070 29 ± 2 9.4 ± 2.4 0.653 HG552 Aphyric basalt 0.185 2940 112 ± 3 14.2 ± 3.2 0.426 HG554 Aphyric basalt 0.117 2840 208 ± 4 10.7 ± 2.0 0.750 HG583 Aphyric hawaiite 0.178 2820 108 ± 14 7.7 ± 1.7 5.37 HG623 Aphyric basalt 0.323 3350 31 ± 4 47.4 ± 2.1 1.31 HG723 Aphyric basalt 0.292 3220 42 ± 2 7.7 ± 1.2 0.45 HG761 Aphyric basalt 0.297 3000 29 ± 1 43.2 ± 1.8 2.07 HG785 Sparsely plagioclase-phyric basalt 0.134 3020 80 ± 1 4.1 ± 0.1 0.43 HG804 Highly olivine-phyric basalt 0.311 3060 37 ± 4 13.7 ± 1.4 1.68 HG820 Aphyric basalt 0.184 3010 58 ± 6 34.1 ± 1.1 5.05 HG851 Aphyric basalt 0.153 3000 148 ± 12 36.9 ± 2.3 4.99 HG904 Sparsely plagioclase-phyric basalt 0.077 3010 139 ± 17 115 ± 6.5 11.36 HG919 Aphyric basalt 0.255 3030 50 ± 9 172 ± 17 11.27 HG921 Aphyric basalt 0.308 2840 32 ± 10 230 ± 19 7.52 HG924 Sparsely to moderately olivine-phyric basalt 0.261 3100 82 ± 4 221 ± 4 22.68 HG1058 Moderately plagioclase olivine-phyric basalt 0.149 3360 40 ± 2 795 ± 29 23.93 Sample Rock name ϕ (–) ρg (kg m−3) F (–) σS (10−4 S m−1) CEC (meq/100 g) HG7 Sparsely plagioclase-phyric hawaiite 0.114 2880 172 ± 6 4.5 ± 0.4 0.483 HG44 Aphyric hawaiite 0.065 2850 158 ± 16 3.1 ± 0.1 0.199 HG78 Moderately plagioclase olivine-phyric basalt 0.204 3010 44 ± 1 13.7 ± 1.1 0.256 HG137 Aphyric basalt 0.064 2990 305 ± 34 5.9 ± 1.1 0.199 HG157 Aphyric basalt 0.146 3050 176 ± 7 4.0 ± 0.5 0.482 HG179 Aphyric hawaiite 0.189 2080 56 ± 2 4.5 ± 0.9 0.312 HG279 Aphyric basalt 0.103 3040 240 ± 41 5.7 ± 1.9 0.398 HG341 Sparsely plagioclase-phyric hawaiite 0.198 2720 114 ± 7 4.4 ± 0.9 0.625 HG365 Aphyric to sparsely plagioclase-phyric basalt 0.080 2450 132 ± 67 3.3 ± 6.3 0.397 HG444 Aphyric to sparsely olivine-phyric basalt 0.131 2970 167 ± 1 5.9 ± 0.1 0.663 HG463 Aphyric to sparsely plagioclase-phyric basalt 0.129 3070 143 ± 6 14.4 ± 0.9 0.738 HG482 Moderately to highly olivine-phyric basalt 0.323 3070 33 ± 2 5.9 ± 2.4 0.454 HG524 Aphyric basalt 0.328 3070 29 ± 2 9.4 ± 2.4 0.653 HG552 Aphyric basalt 0.185 2940 112 ± 3 14.2 ± 3.2 0.426 HG554 Aphyric basalt 0.117 2840 208 ± 4 10.7 ± 2.0 0.750 HG583 Aphyric hawaiite 0.178 2820 108 ± 14 7.7 ± 1.7 5.37 HG623 Aphyric basalt 0.323 3350 31 ± 4 47.4 ± 2.1 1.31 HG723 Aphyric basalt 0.292 3220 42 ± 2 7.7 ± 1.2 0.45 HG761 Aphyric basalt 0.297 3000 29 ± 1 43.2 ± 1.8 2.07 HG785 Sparsely plagioclase-phyric basalt 0.134 3020 80 ± 1 4.1 ± 0.1 0.43 HG804 Highly olivine-phyric basalt 0.311 3060 37 ± 4 13.7 ± 1.4 1.68 HG820 Aphyric basalt 0.184 3010 58 ± 6 34.1 ± 1.1 5.05 HG851 Aphyric basalt 0.153 3000 148 ± 12 36.9 ± 2.3 4.99 HG904 Sparsely plagioclase-phyric basalt 0.077 3010 139 ± 17 115 ± 6.5 11.36 HG919 Aphyric basalt 0.255 3030 50 ± 9 172 ± 17 11.27 HG921 Aphyric basalt 0.308 2840 32 ± 10 230 ± 19 7.52 HG924 Sparsely to moderately olivine-phyric basalt 0.261 3100 82 ± 4 221 ± 4 22.68 HG1058 Moderately plagioclase olivine-phyric basalt 0.149 3360 40 ± 2 795 ± 29 23.93 Open in new tab Table 2. Properties of the 28 Samples (labels HG denotes the HGRP project) used in this study. The parameter ϕ denotes the connected porosity (dimensionless, precision on the order of 1 per cent in relative value), ρg the grain density (in kg m−3, precision on the order of 1 per cent in relative value), F the electrical (intrinsic) formation factor (dimensionless), σS the surface conductivity (in S m−1), and CEC denotes the cation exchange capacity (here given in meq/100 g with 1 meq/100 g = 1 cMol kg−1 = 963.2 C kg−1, standard deviation unknown). Sample HG583 does not have measurements at 0.5 and 1 Sm−1 because it had a crack. The formation factors and surface conductivities are determined form the conductivity of the rock plotted versus the pore water conductivity (see Figs 9 and 10). The relatively high grain density would mean that the non-connected porosity is relatively small. Sample Rock name ϕ (–) ρg (kg m−3) F (–) σS (10−4 S m−1) CEC (meq/100 g) HG7 Sparsely plagioclase-phyric hawaiite 0.114 2880 172 ± 6 4.5 ± 0.4 0.483 HG44 Aphyric hawaiite 0.065 2850 158 ± 16 3.1 ± 0.1 0.199 HG78 Moderately plagioclase olivine-phyric basalt 0.204 3010 44 ± 1 13.7 ± 1.1 0.256 HG137 Aphyric basalt 0.064 2990 305 ± 34 5.9 ± 1.1 0.199 HG157 Aphyric basalt 0.146 3050 176 ± 7 4.0 ± 0.5 0.482 HG179 Aphyric hawaiite 0.189 2080 56 ± 2 4.5 ± 0.9 0.312 HG279 Aphyric basalt 0.103 3040 240 ± 41 5.7 ± 1.9 0.398 HG341 Sparsely plagioclase-phyric hawaiite 0.198 2720 114 ± 7 4.4 ± 0.9 0.625 HG365 Aphyric to sparsely plagioclase-phyric basalt 0.080 2450 132 ± 67 3.3 ± 6.3 0.397 HG444 Aphyric to sparsely olivine-phyric basalt 0.131 2970 167 ± 1 5.9 ± 0.1 0.663 HG463 Aphyric to sparsely plagioclase-phyric basalt 0.129 3070 143 ± 6 14.4 ± 0.9 0.738 HG482 Moderately to highly olivine-phyric basalt 0.323 3070 33 ± 2 5.9 ± 2.4 0.454 HG524 Aphyric basalt 0.328 3070 29 ± 2 9.4 ± 2.4 0.653 HG552 Aphyric basalt 0.185 2940 112 ± 3 14.2 ± 3.2 0.426 HG554 Aphyric basalt 0.117 2840 208 ± 4 10.7 ± 2.0 0.750 HG583 Aphyric hawaiite 0.178 2820 108 ± 14 7.7 ± 1.7 5.37 HG623 Aphyric basalt 0.323 3350 31 ± 4 47.4 ± 2.1 1.31 HG723 Aphyric basalt 0.292 3220 42 ± 2 7.7 ± 1.2 0.45 HG761 Aphyric basalt 0.297 3000 29 ± 1 43.2 ± 1.8 2.07 HG785 Sparsely plagioclase-phyric basalt 0.134 3020 80 ± 1 4.1 ± 0.1 0.43 HG804 Highly olivine-phyric basalt 0.311 3060 37 ± 4 13.7 ± 1.4 1.68 HG820 Aphyric basalt 0.184 3010 58 ± 6 34.1 ± 1.1 5.05 HG851 Aphyric basalt 0.153 3000 148 ± 12 36.9 ± 2.3 4.99 HG904 Sparsely plagioclase-phyric basalt 0.077 3010 139 ± 17 115 ± 6.5 11.36 HG919 Aphyric basalt 0.255 3030 50 ± 9 172 ± 17 11.27 HG921 Aphyric basalt 0.308 2840 32 ± 10 230 ± 19 7.52 HG924 Sparsely to moderately olivine-phyric basalt 0.261 3100 82 ± 4 221 ± 4 22.68 HG1058 Moderately plagioclase olivine-phyric basalt 0.149 3360 40 ± 2 795 ± 29 23.93 Sample Rock name ϕ (–) ρg (kg m−3) F (–) σS (10−4 S m−1) CEC (meq/100 g) HG7 Sparsely plagioclase-phyric hawaiite 0.114 2880 172 ± 6 4.5 ± 0.4 0.483 HG44 Aphyric hawaiite 0.065 2850 158 ± 16 3.1 ± 0.1 0.199 HG78 Moderately plagioclase olivine-phyric basalt 0.204 3010 44 ± 1 13.7 ± 1.1 0.256 HG137 Aphyric basalt 0.064 2990 305 ± 34 5.9 ± 1.1 0.199 HG157 Aphyric basalt 0.146 3050 176 ± 7 4.0 ± 0.5 0.482 HG179 Aphyric hawaiite 0.189 2080 56 ± 2 4.5 ± 0.9 0.312 HG279 Aphyric basalt 0.103 3040 240 ± 41 5.7 ± 1.9 0.398 HG341 Sparsely plagioclase-phyric hawaiite 0.198 2720 114 ± 7 4.4 ± 0.9 0.625 HG365 Aphyric to sparsely plagioclase-phyric basalt 0.080 2450 132 ± 67 3.3 ± 6.3 0.397 HG444 Aphyric to sparsely olivine-phyric basalt 0.131 2970 167 ± 1 5.9 ± 0.1 0.663 HG463 Aphyric to sparsely plagioclase-phyric basalt 0.129 3070 143 ± 6 14.4 ± 0.9 0.738 HG482 Moderately to highly olivine-phyric basalt 0.323 3070 33 ± 2 5.9 ± 2.4 0.454 HG524 Aphyric basalt 0.328 3070 29 ± 2 9.4 ± 2.4 0.653 HG552 Aphyric basalt 0.185 2940 112 ± 3 14.2 ± 3.2 0.426 HG554 Aphyric basalt 0.117 2840 208 ± 4 10.7 ± 2.0 0.750 HG583 Aphyric hawaiite 0.178 2820 108 ± 14 7.7 ± 1.7 5.37 HG623 Aphyric basalt 0.323 3350 31 ± 4 47.4 ± 2.1 1.31 HG723 Aphyric basalt 0.292 3220 42 ± 2 7.7 ± 1.2 0.45 HG761 Aphyric basalt 0.297 3000 29 ± 1 43.2 ± 1.8 2.07 HG785 Sparsely plagioclase-phyric basalt 0.134 3020 80 ± 1 4.1 ± 0.1 0.43 HG804 Highly olivine-phyric basalt 0.311 3060 37 ± 4 13.7 ± 1.4 1.68 HG820 Aphyric basalt 0.184 3010 58 ± 6 34.1 ± 1.1 5.05 HG851 Aphyric basalt 0.153 3000 148 ± 12 36.9 ± 2.3 4.99 HG904 Sparsely plagioclase-phyric basalt 0.077 3010 139 ± 17 115 ± 6.5 11.36 HG919 Aphyric basalt 0.255 3030 50 ± 9 172 ± 17 11.27 HG921 Aphyric basalt 0.308 2840 32 ± 10 230 ± 19 7.52 HG924 Sparsely to moderately olivine-phyric basalt 0.261 3100 82 ± 4 221 ± 4 22.68 HG1058 Moderately plagioclase olivine-phyric basalt 0.149 3360 40 ± 2 795 ± 29 23.93 Open in new tab Table 3. Quadrature conductivity at different frequencies and different salinities. Sample HG583 is not used in this table (measured only at 0.07, 2 and 10 S m−1). The standard deviation of the data reported in this table is smaller than 10 per cent. Sample σ″ (1) σ″ (2) σ″ (3) σ″ (4) σ″ (5) σ″ (6) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) 10 Hz 100 Hz 1 kHz 10 Hz 100 Hz 1 kHz HG7 1.22 2.15 4.60 2.45 4.86 11.6 HG44 2.20 3.21 3.91 3.60 7.84 11.4 HG78 5.60 10.4 16.5 10.1 36.7 85.2 HG137 1.36 2.52 6.11 2.66 4.69 13.2 HG157 2.26 4.98 9.07 6.64 12.4 31.8 HG179 2.61 4.52 9.27 4.14 11.4 35.0 HG279 1.50 3.51 8.19 5.16 8.63 22.8 HG341 2.71 4.77 10.2 4.84 13.3 35.6 HG365 1.57 3.34 6.54 2.94 4.92 12.4 HG444 1.37 2.93 5.78 3.62 6.97 14.4 HG463 2.41 4.12 11.1 3.22 4.92 12.6 HG482 7.24 12.3 18.1 16.6 32.9 61.6 HG524 4.76 12.8 27.4 14.0 32.6 82.8 HG552 1.04 2.30 5.71 4.26 7.92 23.5 HG554 1.67 4.01 8.45 4.12 7.22 15.2 HG623 4.85 9.75 17.1 8.90 33.4 57.4 HG723 7.53 15.2 23.1 18.2 45.1 76.6 HG761 6.21 7.93 12.8 16.9 16.1 25.5 HG785 3.22 5.30 7.83 7.57 13.9 22.9 HG804 6.10 9.38 15.5 8.74 12.6 20.3 HG820 9.41 17.2 32.7 13.2 33.4 60.6 HG851 4.88 8.73 18.1 6.66 12.4 26.3 HG904 24.2 32.3 54.3 35.5 42.7 62.9 HG919 24.2 31.4 64.8 30.3 53.2 90.0 HG921 6.20 11.2 22.5 40.4 53.2 90.9 HG924 39.7 52.3 95.0 35.3 60.6 105 HG1058 62.9 83.8 184 127 112 171 Sample σ″ (1) σ″ (2) σ″ (3) σ″ (4) σ″ (5) σ″ (6) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) 10 Hz 100 Hz 1 kHz 10 Hz 100 Hz 1 kHz HG7 1.22 2.15 4.60 2.45 4.86 11.6 HG44 2.20 3.21 3.91 3.60 7.84 11.4 HG78 5.60 10.4 16.5 10.1 36.7 85.2 HG137 1.36 2.52 6.11 2.66 4.69 13.2 HG157 2.26 4.98 9.07 6.64 12.4 31.8 HG179 2.61 4.52 9.27 4.14 11.4 35.0 HG279 1.50 3.51 8.19 5.16 8.63 22.8 HG341 2.71 4.77 10.2 4.84 13.3 35.6 HG365 1.57 3.34 6.54 2.94 4.92 12.4 HG444 1.37 2.93 5.78 3.62 6.97 14.4 HG463 2.41 4.12 11.1 3.22 4.92 12.6 HG482 7.24 12.3 18.1 16.6 32.9 61.6 HG524 4.76 12.8 27.4 14.0 32.6 82.8 HG552 1.04 2.30 5.71 4.26 7.92 23.5 HG554 1.67 4.01 8.45 4.12 7.22 15.2 HG623 4.85 9.75 17.1 8.90 33.4 57.4 HG723 7.53 15.2 23.1 18.2 45.1 76.6 HG761 6.21 7.93 12.8 16.9 16.1 25.5 HG785 3.22 5.30 7.83 7.57 13.9 22.9 HG804 6.10 9.38 15.5 8.74 12.6 20.3 HG820 9.41 17.2 32.7 13.2 33.4 60.6 HG851 4.88 8.73 18.1 6.66 12.4 26.3 HG904 24.2 32.3 54.3 35.5 42.7 62.9 HG919 24.2 31.4 64.8 30.3 53.2 90.0 HG921 6.20 11.2 22.5 40.4 53.2 90.9 HG924 39.7 52.3 95.0 35.3 60.6 105 HG1058 62.9 83.8 184 127 112 171 (1) Quadrature conductivity at σw = 0.07 S m−1. (2) Quadrature conductivity at σw = 0.07 S m−1. (3) Quadrature conductivity at σw = 0.07 S m−1. (4) Quadrature conductivity at σw = 0.5 or 1 S m−1. (5) Quadrature conductivity at σw = 0.5 or 1 S m−1. (6) Quadrature conductivity at σw = 0.5 or 1 S m−1. Open in new tab Table 3. Quadrature conductivity at different frequencies and different salinities. Sample HG583 is not used in this table (measured only at 0.07, 2 and 10 S m−1). The standard deviation of the data reported in this table is smaller than 10 per cent. Sample σ″ (1) σ″ (2) σ″ (3) σ″ (4) σ″ (5) σ″ (6) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) 10 Hz 100 Hz 1 kHz 10 Hz 100 Hz 1 kHz HG7 1.22 2.15 4.60 2.45 4.86 11.6 HG44 2.20 3.21 3.91 3.60 7.84 11.4 HG78 5.60 10.4 16.5 10.1 36.7 85.2 HG137 1.36 2.52 6.11 2.66 4.69 13.2 HG157 2.26 4.98 9.07 6.64 12.4 31.8 HG179 2.61 4.52 9.27 4.14 11.4 35.0 HG279 1.50 3.51 8.19 5.16 8.63 22.8 HG341 2.71 4.77 10.2 4.84 13.3 35.6 HG365 1.57 3.34 6.54 2.94 4.92 12.4 HG444 1.37 2.93 5.78 3.62 6.97 14.4 HG463 2.41 4.12 11.1 3.22 4.92 12.6 HG482 7.24 12.3 18.1 16.6 32.9 61.6 HG524 4.76 12.8 27.4 14.0 32.6 82.8 HG552 1.04 2.30 5.71 4.26 7.92 23.5 HG554 1.67 4.01 8.45 4.12 7.22 15.2 HG623 4.85 9.75 17.1 8.90 33.4 57.4 HG723 7.53 15.2 23.1 18.2 45.1 76.6 HG761 6.21 7.93 12.8 16.9 16.1 25.5 HG785 3.22 5.30 7.83 7.57 13.9 22.9 HG804 6.10 9.38 15.5 8.74 12.6 20.3 HG820 9.41 17.2 32.7 13.2 33.4 60.6 HG851 4.88 8.73 18.1 6.66 12.4 26.3 HG904 24.2 32.3 54.3 35.5 42.7 62.9 HG919 24.2 31.4 64.8 30.3 53.2 90.0 HG921 6.20 11.2 22.5 40.4 53.2 90.9 HG924 39.7 52.3 95.0 35.3 60.6 105 HG1058 62.9 83.8 184 127 112 171 Sample σ″ (1) σ″ (2) σ″ (3) σ″ (4) σ″ (5) σ″ (6) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) (10−5 S m−1) 10 Hz 100 Hz 1 kHz 10 Hz 100 Hz 1 kHz HG7 1.22 2.15 4.60 2.45 4.86 11.6 HG44 2.20 3.21 3.91 3.60 7.84 11.4 HG78 5.60 10.4 16.5 10.1 36.7 85.2 HG137 1.36 2.52 6.11 2.66 4.69 13.2 HG157 2.26 4.98 9.07 6.64 12.4 31.8 HG179 2.61 4.52 9.27 4.14 11.4 35.0 HG279 1.50 3.51 8.19 5.16 8.63 22.8 HG341 2.71 4.77 10.2 4.84 13.3 35.6 HG365 1.57 3.34 6.54 2.94 4.92 12.4 HG444 1.37 2.93 5.78 3.62 6.97 14.4 HG463 2.41 4.12 11.1 3.22 4.92 12.6 HG482 7.24 12.3 18.1 16.6 32.9 61.6 HG524 4.76 12.8 27.4 14.0 32.6 82.8 HG552 1.04 2.30 5.71 4.26 7.92 23.5 HG554 1.67 4.01 8.45 4.12 7.22 15.2 HG623 4.85 9.75 17.1 8.90 33.4 57.4 HG723 7.53 15.2 23.1 18.2 45.1 76.6 HG761 6.21 7.93 12.8 16.9 16.1 25.5 HG785 3.22 5.30 7.83 7.57 13.9 22.9 HG804 6.10 9.38 15.5 8.74 12.6 20.3 HG820 9.41 17.2 32.7 13.2 33.4 60.6 HG851 4.88 8.73 18.1 6.66 12.4 26.3 HG904 24.2 32.3 54.3 35.5 42.7 62.9 HG919 24.2 31.4 64.8 30.3 53.2 90.0 HG921 6.20 11.2 22.5 40.4 53.2 90.9 HG924 39.7 52.3 95.0 35.3 60.6 105 HG1058 62.9 83.8 184 127 112 171 (1) Quadrature conductivity at σw = 0.07 S m−1. (2) Quadrature conductivity at σw = 0.07 S m−1. (3) Quadrature conductivity at σw = 0.07 S m−1. (4) Quadrature conductivity at σw = 0.5 or 1 S m−1. (5) Quadrature conductivity at σw = 0.5 or 1 S m−1. (6) Quadrature conductivity at σw = 0.5 or 1 S m−1. Open in new tab The CEC measurements were performed with the cobalt hexamine chloride method (e.g. Ciesielski et al. 1997). Cobalt is known to have a very strong affinity with the surface of the clay minerals. This salt is preferred to the ammonium acetate method because it is sensitive only to the clay minerals and not to the zeolite (Revil et al. 2002) which was common in the deeper core samples. In our case, the CEC is determined as the capacity of removing amine from a 0.045 N Hexaaminne cobalt(III) Chloride solution. As discussed by Ciesielski et al. (1997), several procedures are possible to determine the CEC from hexamine chloride method. Hexaaminne cobalt(III) Chloride is characterized by a pronounced orange color. Because of sorption of the cobalt on the surface of the clay minerals, the color of the solution gets weaker. Due to measuring the difference in strength in color before and after contact with the sample, we can determine the CEC, which corresponds to the amount of interchangeable cations per unit mass of minerals. The reduction in color provides the quantity of used Hexaaminne cobalt(III) Chloride from which the quantity of interchangeable cations in the sample is calculated. CEC and concentration of exchangeable clay cations are calculated based in the absorbance measurements with a calibrated spectrophotometer (Evolution 201, UV-Visible Spectrophotometer, Thermo Scientific). The CEC provided in Table 2 are expressed in meq/100 g (which is the traditional unit for this parameter) and can be converted in the international system of units using 1 meq/100 g = 963.20 C kg−1. 4.2 X-ray diffraction (XRD) analysis For the XRD measurements, a representative portion of each sample was dried and then grounded in a Brinkman MM-2 Retsch Mill to a fine powder (approximately 10–15 μm). The resulting material is then loaded into an alloy sampleholder and scanned with a Bruker AXS D4 Endeavor X-ray diffractometer using copper K-alpha radiation. Computer analysis of the diffractograms provides identification of mineral phases and semiquantitative analysis of their relative abundance (in wt%, see Table 4). Note that the rocks contain a non-negligible amount of magnetite, which is a semi-conductor known to be responsible for a strong induced polarization effect (Wong 1979; Gurin et al. 2013; Revil et al. 2014). Table 4. Results of the semi-quantitative XRD analysis for the 28 samples. The data are given in weight fractions. ‘Plag’ denotes plagioclase, ‘Horn’ stands for Hornblende, ‘Aeg’ stands for Aegirine, and ‘Mag’ stands for magnetite. ‘Tr’ stands for trace. Note that natrolite (a tectosilicate species belonging to the group of zeolite) is only found at the bottom of the well (sample HG1058). Sample K-spar Plag. Augite Aeg Horn Olivine Apatite Mag Natrolite HG7 10 61 11 0 2 6 4 4 0 HG44 3 68 10 1 2 8 4 3 0 HG78 2 45 36 0 1 10 2 3 0 HG137 2 47 37 2 2 Tr 4 5 0 HG157 4 44 40 2 1 2 2 4 0 HG179 6 69 6 1 1 9 5 3 0 HG279 2 46 39 2 1 3 2 4 0 HG341 6 67 8 1 1 9 4 4 0 HG365 Tr 49 43 1 1 1 1 3 0 HG444 Tr 49 42 2 1 1 1 3 0 HG463 1 51 38 3 1 1 2 3 0 HG482 2 36 32 3 1 22 2 1 0 HG524 4 41 40 3 1 6 3 2 0 HG552 3 47 37 2 2 1 3 4 0 HG554 2 47 39 2 1 2 2 4 0 HG583 6 70 6 Tr 1 8 5 3 0 HG623 1 46 43 2 1 4 1 2 0 HG723 2 45 40 3 1 5 3 1 0 HG761 9 45 35 0 1 4 1 3 0 HG785 7 49 37 0 Tr 5 Tr 1 0 HG804 1 40 40 0 0 16 Tr 3 0 HG820 1 48 39 2 1 2 1 5 0 HG851 0 51 41 0 2 2 1 3 0 HG904 Tr 51 39 2 1 2 0 3 0 HG919 9 48 29 0 1 1 0 0 0 HG921 4 58 22 0 0 1 Tr 2 0 HG924 13 35 37 3 2 0 2 1 0 HG1058 6 15 37 7 0 0 0 0 18 Sample K-spar Plag. Augite Aeg Horn Olivine Apatite Mag Natrolite HG7 10 61 11 0 2 6 4 4 0 HG44 3 68 10 1 2 8 4 3 0 HG78 2 45 36 0 1 10 2 3 0 HG137 2 47 37 2 2 Tr 4 5 0 HG157 4 44 40 2 1 2 2 4 0 HG179 6 69 6 1 1 9 5 3 0 HG279 2 46 39 2 1 3 2 4 0 HG341 6 67 8 1 1 9 4 4 0 HG365 Tr 49 43 1 1 1 1 3 0 HG444 Tr 49 42 2 1 1 1 3 0 HG463 1 51 38 3 1 1 2 3 0 HG482 2 36 32 3 1 22 2 1 0 HG524 4 41 40 3 1 6 3 2 0 HG552 3 47 37 2 2 1 3 4 0 HG554 2 47 39 2 1 2 2 4 0 HG583 6 70 6 Tr 1 8 5 3 0 HG623 1 46 43 2 1 4 1 2 0 HG723 2 45 40 3 1 5 3 1 0 HG761 9 45 35 0 1 4 1 3 0 HG785 7 49 37 0 Tr 5 Tr 1 0 HG804 1 40 40 0 0 16 Tr 3 0 HG820 1 48 39 2 1 2 1 5 0 HG851 0 51 41 0 2 2 1 3 0 HG904 Tr 51 39 2 1 2 0 3 0 HG919 9 48 29 0 1 1 0 0 0 HG921 4 58 22 0 0 1 Tr 2 0 HG924 13 35 37 3 2 0 2 1 0 HG1058 6 15 37 7 0 0 0 0 18 Open in new tab Table 4. Results of the semi-quantitative XRD analysis for the 28 samples. The data are given in weight fractions. ‘Plag’ denotes plagioclase, ‘Horn’ stands for Hornblende, ‘Aeg’ stands for Aegirine, and ‘Mag’ stands for magnetite. ‘Tr’ stands for trace. Note that natrolite (a tectosilicate species belonging to the group of zeolite) is only found at the bottom of the well (sample HG1058). Sample K-spar Plag. Augite Aeg Horn Olivine Apatite Mag Natrolite HG7 10 61 11 0 2 6 4 4 0 HG44 3 68 10 1 2 8 4 3 0 HG78 2 45 36 0 1 10 2 3 0 HG137 2 47 37 2 2 Tr 4 5 0 HG157 4 44 40 2 1 2 2 4 0 HG179 6 69 6 1 1 9 5 3 0 HG279 2 46 39 2 1 3 2 4 0 HG341 6 67 8 1 1 9 4 4 0 HG365 Tr 49 43 1 1 1 1 3 0 HG444 Tr 49 42 2 1 1 1 3 0 HG463 1 51 38 3 1 1 2 3 0 HG482 2 36 32 3 1 22 2 1 0 HG524 4 41 40 3 1 6 3 2 0 HG552 3 47 37 2 2 1 3 4 0 HG554 2 47 39 2 1 2 2 4 0 HG583 6 70 6 Tr 1 8 5 3 0 HG623 1 46 43 2 1 4 1 2 0 HG723 2 45 40 3 1 5 3 1 0 HG761 9 45 35 0 1 4 1 3 0 HG785 7 49 37 0 Tr 5 Tr 1 0 HG804 1 40 40 0 0 16 Tr 3 0 HG820 1 48 39 2 1 2 1 5 0 HG851 0 51 41 0 2 2 1 3 0 HG904 Tr 51 39 2 1 2 0 3 0 HG919 9 48 29 0 1 1 0 0 0 HG921 4 58 22 0 0 1 Tr 2 0 HG924 13 35 37 3 2 0 2 1 0 HG1058 6 15 37 7 0 0 0 0 18 Sample K-spar Plag. Augite Aeg Horn Olivine Apatite Mag Natrolite HG7 10 61 11 0 2 6 4 4 0 HG44 3 68 10 1 2 8 4 3 0 HG78 2 45 36 0 1 10 2 3 0 HG137 2 47 37 2 2 Tr 4 5 0 HG157 4 44 40 2 1 2 2 4 0 HG179 6 69 6 1 1 9 5 3 0 HG279 2 46 39 2 1 3 2 4 0 HG341 6 67 8 1 1 9 4 4 0 HG365 Tr 49 43 1 1 1 1 3 0 HG444 Tr 49 42 2 1 1 1 3 0 HG463 1 51 38 3 1 1 2 3 0 HG482 2 36 32 3 1 22 2 1 0 HG524 4 41 40 3 1 6 3 2 0 HG552 3 47 37 2 2 1 3 4 0 HG554 2 47 39 2 1 2 2 4 0 HG583 6 70 6 Tr 1 8 5 3 0 HG623 1 46 43 2 1 4 1 2 0 HG723 2 45 40 3 1 5 3 1 0 HG761 9 45 35 0 1 4 1 3 0 HG785 7 49 37 0 Tr 5 Tr 1 0 HG804 1 40 40 0 0 16 Tr 3 0 HG820 1 48 39 2 1 2 1 5 0 HG851 0 51 41 0 2 2 1 3 0 HG904 Tr 51 39 2 1 2 0 3 0 HG919 9 48 29 0 1 1 0 0 0 HG921 4 58 22 0 0 1 Tr 2 0 HG924 13 35 37 3 2 0 2 1 0 HG1058 6 15 37 7 0 0 0 0 18 Open in new tab An oriented clay fraction mount is also prepared for each sample from a homogenized split of the original sample. The material used for clay speciation is hand ground using mortar and pestle to liberate the clay particles. Ultrasonic treatment is used to suspend the material, then a dispersant is used to prevent flocculation. The samples were further centrifuged to extract the fraction smaller than 4 μm. The solution containing the clay fraction is then passed through a Fisher filter membrane apparatus allowing the solids to be collected on a cellulose membrane filter. These solids are then mounted on a glass slide and scanned with a Bruker AXS diffractometer. Finally, the oriented clay mounts are glycolated and another diffractogram was prepared to determine the expandable, water sensitive minerals, such as smectite. The slide is finally heat-treated and scanned with the same parameters to aid in identifying kaolinite and chlorite. The results are also reported in Table 5. Table 5. Results of the semi-quantitative XRD analysis for the 28 samples for the clay minerals. The data are given in weight fractions. Sample Chlorite Kaolinite Illite/mica Smectite HG7 0 0 1 0 HG44 0 0 0 0 HG78 0 0 0 0 HG137 0 0 0 0 HG157 0 0 0 0 HG179 0 0 0 0 HG279 0 0 0 0 HG341 0 0 0 0 HG365 0 0 0 0 HG444 0 0 0 0 HG463 0 0 0 0 HG482 0 0 0 0 HG524 0 0 0 0 HG552 0 0 0 0 HG554 0 0 0 0 HG583 0 0 1 0 HG623 0 0 0 0 HG723 0 0 0 0 HG761 0 0 0 2 HG785 0 0 1 0 HG804 0 0 0 0 HG820 0 0 0 0 HG851 0 0 0 0 HG904 0 0 0 1 HG919 0 0 0 12 HG921 0 0 0 13 HG924 0 0 0 7 HG1058 0 0 0 17 Sample Chlorite Kaolinite Illite/mica Smectite HG7 0 0 1 0 HG44 0 0 0 0 HG78 0 0 0 0 HG137 0 0 0 0 HG157 0 0 0 0 HG179 0 0 0 0 HG279 0 0 0 0 HG341 0 0 0 0 HG365 0 0 0 0 HG444 0 0 0 0 HG463 0 0 0 0 HG482 0 0 0 0 HG524 0 0 0 0 HG552 0 0 0 0 HG554 0 0 0 0 HG583 0 0 1 0 HG623 0 0 0 0 HG723 0 0 0 0 HG761 0 0 0 2 HG785 0 0 1 0 HG804 0 0 0 0 HG820 0 0 0 0 HG851 0 0 0 0 HG904 0 0 0 1 HG919 0 0 0 12 HG921 0 0 0 13 HG924 0 0 0 7 HG1058 0 0 0 17 Open in new tab Table 5. Results of the semi-quantitative XRD analysis for the 28 samples for the clay minerals. The data are given in weight fractions. Sample Chlorite Kaolinite Illite/mica Smectite HG7 0 0 1 0 HG44 0 0 0 0 HG78 0 0 0 0 HG137 0 0 0 0 HG157 0 0 0 0 HG179 0 0 0 0 HG279 0 0 0 0 HG341 0 0 0 0 HG365 0 0 0 0 HG444 0 0 0 0 HG463 0 0 0 0 HG482 0 0 0 0 HG524 0 0 0 0 HG552 0 0 0 0 HG554 0 0 0 0 HG583 0 0 1 0 HG623 0 0 0 0 HG723 0 0 0 0 HG761 0 0 0 2 HG785 0 0 1 0 HG804 0 0 0 0 HG820 0 0 0 0 HG851 0 0 0 0 HG904 0 0 0 1 HG919 0 0 0 12 HG921 0 0 0 13 HG924 0 0 0 7 HG1058 0 0 0 17 Sample Chlorite Kaolinite Illite/mica Smectite HG7 0 0 1 0 HG44 0 0 0 0 HG78 0 0 0 0 HG137 0 0 0 0 HG157 0 0 0 0 HG179 0 0 0 0 HG279 0 0 0 0 HG341 0 0 0 0 HG365 0 0 0 0 HG444 0 0 0 0 HG463 0 0 0 0 HG482 0 0 0 0 HG524 0 0 0 0 HG552 0 0 0 0 HG554 0 0 0 0 HG583 0 0 1 0 HG623 0 0 0 0 HG723 0 0 0 0 HG761 0 0 0 2 HG785 0 0 1 0 HG804 0 0 0 0 HG820 0 0 0 0 HG851 0 0 0 0 HG904 0 0 0 1 HG919 0 0 0 12 HG921 0 0 0 13 HG924 0 0 0 7 HG1058 0 0 0 17 Open in new tab 4.3 Complex conductivity spectra The measurements were performed in the temperature range 21–23 °C. The potential electrodes M and N are non-polarizable silver–silver chloride (Ag–AgCl) electrodes (2 mm diameter and 2.5 mm long). The injection electrodes A and B are stainless steel plates with the same diameter as the core sample (Fig. 2a). The sample is located inside an elastic and insulating jacket that maintains the voltage electrodes in place and avoids the drying of the core sample during the measurements. The complex conductivity measurement was conducted using the four-terminal method with a high-precision impedance analyzer (Zimmermann et al. 2008, see Fig. 2b). The samples came in different sizes (e.g. Fig. 2c). The injection electrodes are always located at the end-faces of the cylindrical core samples and are therefore never close to each other. For each sample, the measured resistance was converted into resistivity using the geometrical factor that was numerically calculated based on the geometry of the sample and the position and size of the electrodes (see Fig. 2 and details of the method are provided in Jougnot et al. 2010). Note that the same geometrical factor was used for a given electrode configuration and a given sample for all the salinities and frequencies. This was justified here because the current electrodes are at the end-faces of the core sample and the applied electrical field is therefore uniform. The situation could be different for different electrode setups. Figure 2. Open in new tabDownload slide Impedance meter, position of the electrodes, and core samples. (a) Position of the electrodes on the surface of the core samples. (b) ZEL-SIP04-V02 impedance meter used for the laboratory experiments. Current injection electrodes A and B are made of stainless steel plates located at the end-faces of the cylindrical core. Two potential electrodes M and N are non-polarized Ag–AgCl electrodes located on the side of the sample. The geometrical factor is computed from numerical modeling using Comsol Multiphysics. (c) The rock samples are mostly shield-stage lavas from Mauna Kea volcano (HI, USA). Some of the cores were redrilled because of the presence of some cracks. Comparison between cores HG921 (aphyric basalt), HG524 (aphyric basalt), HG463 (aphyric to sparsely plagioclase-phyric basalt) and HG365 (aphyric to sparsely plagioclase-phyric basalt). For each core sample, the geometrical factor was determined by solving numerically the Poisson equation accounting for the position and size of the electrodes and a finite element solver. Figure 2. Open in new tabDownload slide Impedance meter, position of the electrodes, and core samples. (a) Position of the electrodes on the surface of the core samples. (b) ZEL-SIP04-V02 impedance meter used for the laboratory experiments. Current injection electrodes A and B are made of stainless steel plates located at the end-faces of the cylindrical core. Two potential electrodes M and N are non-polarized Ag–AgCl electrodes located on the side of the sample. The geometrical factor is computed from numerical modeling using Comsol Multiphysics. (c) The rock samples are mostly shield-stage lavas from Mauna Kea volcano (HI, USA). Some of the cores were redrilled because of the presence of some cracks. Comparison between cores HG921 (aphyric basalt), HG524 (aphyric basalt), HG463 (aphyric to sparsely plagioclase-phyric basalt) and HG365 (aphyric to sparsely plagioclase-phyric basalt). For each core sample, the geometrical factor was determined by solving numerically the Poisson equation accounting for the position and size of the electrodes and a finite element solver. The core samples were initially saturated with a low salinity NaCl solution in a vacuum chamber for 24 hr. Then samples were left a week in the solution in a closed container, and fluid conductivity was measured, until reaching stability. Afterwards, the samples were taken out for complex conductivity measurement under frequency from 1 mHz to 45 kHz. The standard deviation was determined at each frequency from the data over three cycles. Data with positive phases and/or standard deviations in excess of 10 per cent were not considered in our analysis. The fluid conductivity σw and temperature T of the NaCl solutions were also measured using a conductivity meter. Samples were then dried, by vacuum water extraction and heating in a vacuum oven. The same procedure was repeated to get the complex conductivity of the sample at all the following pore fluid conductivities: 0.07, 0.5, 1.0 or 2 S m−1 and 10 S m−1. For each conductivity, the solution in which the samples were immersed was changed to enforce the value of the desired conductivity. The exchange of ions between the core samples and the surrounding pore water was done by diffusion. Equilibrium was reached in several weeks. For a few selected core samples, the complex conductivity spectra were repeated over time and the batch was considered to have reach equilibrium when the conductivity spectra were not changing over time. Figs 3 –5 show several tests made with the core samples demonstrating the quality of the recorded data including repeatability and stability over time. Figure 3. Open in new tabDownload slide Raw data for sample #851 at a pore water conductivity of 0.070 S m−1. The measurements were repeated over two days at four distinct differences of applied electrical potentials. Note that we usually apply a difference of voltage of 5 V for the measurements. The low-frequency parts of the spectra that are sensitive to the applied voltage may be due to the presence of magnetite. Note regarding the phase: the measured phases are always negative. We take here the magnitude of the phase to be able to plot the phase on a log scale. Figure 3. Open in new tabDownload slide Raw data for sample #851 at a pore water conductivity of 0.070 S m−1. The measurements were repeated over two days at four distinct differences of applied electrical potentials. Note that we usually apply a difference of voltage of 5 V for the measurements. The low-frequency parts of the spectra that are sensitive to the applied voltage may be due to the presence of magnetite. Note regarding the phase: the measured phases are always negative. We take here the magnitude of the phase to be able to plot the phase on a log scale. Figure 4. Open in new tabDownload slide Raw data for sample #785 at the lowest pore water conductivity (0.024 S m−1). The measurements were repeated over seven days after saturation under vacuum using a degassed NaCl electrolyte. We applied a difference of voltage of 5 V. Note regarding the phase: the measured phases are always negative. We took here the magnitude of the phase to be able to plot the phase on a log scale. Figure 4. Open in new tabDownload slide Raw data for sample #785 at the lowest pore water conductivity (0.024 S m−1). The measurements were repeated over seven days after saturation under vacuum using a degassed NaCl electrolyte. We applied a difference of voltage of 5 V. Note regarding the phase: the measured phases are always negative. We took here the magnitude of the phase to be able to plot the phase on a log scale. Figure 5. Open in new tabDownload slide Repeated spectra (raw data) for sample #851 at a pore water conductivity of 0.070 S m−1, and an applied voltage of 5 V. We observe very little variations in the spectra after the saturation of the core sample under vacuum with the degassed electrolyte. Note regarding the phase: the measured phases are always negative. We considered here the magnitude of the phase to be able to plot the phase on a log scale. Figure 5. Open in new tabDownload slide Repeated spectra (raw data) for sample #851 at a pore water conductivity of 0.070 S m−1, and an applied voltage of 5 V. We observe very little variations in the spectra after the saturation of the core sample under vacuum with the degassed electrolyte. Note regarding the phase: the measured phases are always negative. We considered here the magnitude of the phase to be able to plot the phase on a log scale. Examples of complex conductivity spectra (real and imaginary parts versus frequency) are shown in Figs 6 –8. In Figs 6 and 7, we show spectra for samples characterized by the presence of some magnetite (3–5 per cent wt.) while in Fig. 8, we show some spectra for samples with minor amount of magnetite (≤1 per cent wt.). There is no obvious signature of the magnetite in the spectra. The polarization peaks will be analyzed in details in the second paper of this series. Figure 6. Open in new tabDownload slide Complex conductivity spectra of samples with magnetite. Real (in phase σ΄, filled circles) and imaginary (quadrature σ″, filled triangles) parts of the complex conductivity are displayed as a function of the frequency. Samples HG820 (aphyric basalt), HG552 (aphyric basalt), HG554 (aphyric basalt) and HG279 (aphyric basalt). A and B denote the low-frequency and high-frequency polarizations, respectively. The quadrature conductivity can be perceived as the imaginary part of the complex surface conductivity. The low-frequency part of the quadrature conductivity is usually not plotted because of large uncertainties (standard deviation >10 per cent). Figure 6. Open in new tabDownload slide Complex conductivity spectra of samples with magnetite. Real (in phase σ΄, filled circles) and imaginary (quadrature σ″, filled triangles) parts of the complex conductivity are displayed as a function of the frequency. Samples HG820 (aphyric basalt), HG552 (aphyric basalt), HG554 (aphyric basalt) and HG279 (aphyric basalt). A and B denote the low-frequency and high-frequency polarizations, respectively. The quadrature conductivity can be perceived as the imaginary part of the complex surface conductivity. The low-frequency part of the quadrature conductivity is usually not plotted because of large uncertainties (standard deviation >10 per cent). Figure 7. Open in new tabDownload slide Complex conductivity spectra. Real (in phase σ΄, filled circles) and imaginary (quadrature σ″, filled triangles) parts of the complex conductivity are displayed as a function of the frequency. Samples HG44 (aphyric hawaiite), HG78 (moderately plagioclase olivine-phyric basalt), HG137 (aphyric basalt) and HG904 (sparsely plagioclase-phyric basalt). The low-frequency part of the quadrature conductivity is usually not plotted because of large uncertainties in the measurements. Figure 7. Open in new tabDownload slide Complex conductivity spectra. Real (in phase σ΄, filled circles) and imaginary (quadrature σ″, filled triangles) parts of the complex conductivity are displayed as a function of the frequency. Samples HG44 (aphyric hawaiite), HG78 (moderately plagioclase olivine-phyric basalt), HG137 (aphyric basalt) and HG904 (sparsely plagioclase-phyric basalt). The low-frequency part of the quadrature conductivity is usually not plotted because of large uncertainties in the measurements. Figure 8. Open in new tabDownload slide Complex conductivity spectra for some of the core samples with minor amount of magnetite. Real (in phase σ΄, filled circles) and imaginary (quadrature σ″, filled triangles) parts of the complex conductivity are displayed as a function of the frequency. Samples HG919 (aphyric basalt), HG482 (moderately to highly olivine-phyric basalt), HG723 (aphyric basalt) and HG785 (sparsely plagioclase-phyric basalt). The low-frequency part of the quadrature conductivity is usually not plotted because of large uncertainties. Figure 8. Open in new tabDownload slide Complex conductivity spectra for some of the core samples with minor amount of magnetite. Real (in phase σ΄, filled circles) and imaginary (quadrature σ″, filled triangles) parts of the complex conductivity are displayed as a function of the frequency. Samples HG919 (aphyric basalt), HG482 (moderately to highly olivine-phyric basalt), HG723 (aphyric basalt) and HG785 (sparsely plagioclase-phyric basalt). The low-frequency part of the quadrature conductivity is usually not plotted because of large uncertainties. The in-phase conductivity increases with the frequency especially above 10 Hz. This increase is likely due to the polarization of the clay minerals. The in-phase conductivity spectra have not reached a plateau at the highest frequency used in this study (35 kHz) and therefore it is not possible to determine a total chargeability. 5 COMPLEX SURFACE CONDUCTIVITY RESULTS 5.1 Intrinsic formation factor The conductivity of each core sample is plotted as a function of the pore water conductivity. The data are then fitted with eq. (10) in order to determine the intrinsic formation factor and the surface conductivity. We first show in Figs 9 and 10 that the frequency at which the conductivity data are considered has only a minor effect on the determined value of the formation factor and surface conductivity. Fig. 9 concerns a sample with a low surface conductivity and a low CEC (therefore a low surface conductivity) while Fig. 10 concerns a sample with a high surface conductivity and a large CEC (therefore a high surface conductivity). Figure 9. Open in new tabDownload slide Determination of the formation factor and surface conductivity using the equation shown in the graph. We plot the conductivity of the rock sample HG444 as a function of the conductivity of the pore water at four different pore water salinities and for four distinct frequencies (10 Hz, 100 Hz, 1 kHz and 10 kHz). For each frequency, we use a linear conductivity model to determine the surface conductivity and the formation factor. We observe a small increase in the surface conductivity as the frequency increases and a small decrease in the intrinsic formation factor. This sample is characterized by a low surface conductivity. The mention ‘in situ’ corresponds to the in situ electrical conductivity of the aquifer. The plot shows also the influence of the frequency on the determination of the (intrinsic) formation factor and the surface conductivity. Figure 9. Open in new tabDownload slide Determination of the formation factor and surface conductivity using the equation shown in the graph. We plot the conductivity of the rock sample HG444 as a function of the conductivity of the pore water at four different pore water salinities and for four distinct frequencies (10 Hz, 100 Hz, 1 kHz and 10 kHz). For each frequency, we use a linear conductivity model to determine the surface conductivity and the formation factor. We observe a small increase in the surface conductivity as the frequency increases and a small decrease in the intrinsic formation factor. This sample is characterized by a low surface conductivity. The mention ‘in situ’ corresponds to the in situ electrical conductivity of the aquifer. The plot shows also the influence of the frequency on the determination of the (intrinsic) formation factor and the surface conductivity. Figure 10. Open in new tabDownload slide Determination of the formation factor and surface conductivity using the equation shown in the graph. We plot the conductivity of the very altered rock sample HG1058 as a function of the conductivity of the pore water at four different pore water salinities and for four distinct frequencies (10 Hz, 100 Hz, 1 kHz, and 10 kHz). For each frequency, we use a linear conductivity model to determine the surface conductivity and the (intrinsic) formation factor. We observe a small increase in the surface conductivity as the frequency increases and a small decrease in the intrinsic formation factor. This sample is characterized by a high surface conductivity. The mention ‘in situ’ corresponds to the in situ electrical conductivity of the aquifer. The plot shows also the influence of the frequency on the determination of the (intrinsic) formation factor and the surface conductivity. Figure 10. Open in new tabDownload slide Determination of the formation factor and surface conductivity using the equation shown in the graph. We plot the conductivity of the very altered rock sample HG1058 as a function of the conductivity of the pore water at four different pore water salinities and for four distinct frequencies (10 Hz, 100 Hz, 1 kHz, and 10 kHz). For each frequency, we use a linear conductivity model to determine the surface conductivity and the (intrinsic) formation factor. We observe a small increase in the surface conductivity as the frequency increases and a small decrease in the intrinsic formation factor. This sample is characterized by a high surface conductivity. The mention ‘in situ’ corresponds to the in situ electrical conductivity of the aquifer. The plot shows also the influence of the frequency on the determination of the (intrinsic) formation factor and the surface conductivity. Fig. 11 shows the (intrinsic) formation factor data versus the connected porosity of the 28 core samples. The datasets exhibit at first approximation a power law relationship and is fitted with Archie's law. The resulting average cementation exponent m is 2.45 indicating that only a modest portion of the connected porosity controls the electrical conductivity of these porous rocks, which is consistent with the complex nature of the connected pore space of these materials. Note that the power law correlation between the formation factors and the porosity data is only fair. This is probably because we used all the dataset at once with both fresh and altered rocks taken together. In order to invstigate this poit in more details, we have plotted in Fig. 12 the cementation exponent of the individual rock samples as a function of their CEC, which is considered here as a proxy of alteration. When alteration increases, there is first an increase of the value of the cementation exponent with value around 1.5 for fresh samples to 2.8 for samples with a CEC of 500 C kg−1 (0.5 meq/100 g). Above this value, the cementation exponent remains in the range 2.4–3.2. Figure 11. Open in new tabDownload slide Intrinsic formation factor versus connected porosity. The formation factors are determined by fitting the rock conductivity data versus pore water conductivities data (see Figs 9 and 10) using the model provided in these figures. The fit of the data with Archie's law yields an average cementation exponent m of 2.45, which is much higher than the cementation exponent recorded for sedimentary rocks (typically in the range 1.3–2.3, e.g. Waxman & Smits 1968). Figure 11. Open in new tabDownload slide Intrinsic formation factor versus connected porosity. The formation factors are determined by fitting the rock conductivity data versus pore water conductivities data (see Figs 9 and 10) using the model provided in these figures. The fit of the data with Archie's law yields an average cementation exponent m of 2.45, which is much higher than the cementation exponent recorded for sedimentary rocks (typically in the range 1.3–2.3, e.g. Waxman & Smits 1968). Figure 12. Open in new tabDownload slide Cementation exponent versus the CEC for basalts and volcaniclastic materials. The CEC can be taken as a proxy for the alteration of these volcanic rocks. Note that the rocks from the study of Revil et al. (2002) are altered volcaniclastic materials. In the first phase of alteration, the cementation exponent increases linearly with the CEC reflecting an increase in the complexity of the pore network. For CEC >500 C kg−1, the cementation exponent is roughly constant with a value of 2.8 ± 0.4, therefore much large than the classical value of the cementation exponent (m = 2). This relationship can be used to determine the porosity, in saturated conditions from the formation factor. The two outliers at high CEC correspond to samples HG904 and HG1058 corresponding to sparsely plagioclase-phyric a΄a and moderately plagioclase olivine-phyric pahoehoe, respectively. Figure 12. Open in new tabDownload slide Cementation exponent versus the CEC for basalts and volcaniclastic materials. The CEC can be taken as a proxy for the alteration of these volcanic rocks. Note that the rocks from the study of Revil et al. (2002) are altered volcaniclastic materials. In the first phase of alteration, the cementation exponent increases linearly with the CEC reflecting an increase in the complexity of the pore network. For CEC >500 C kg−1, the cementation exponent is roughly constant with a value of 2.8 ± 0.4, therefore much large than the classical value of the cementation exponent (m = 2). This relationship can be used to determine the porosity, in saturated conditions from the formation factor. The two outliers at high CEC correspond to samples HG904 and HG1058 corresponding to sparsely plagioclase-phyric a΄a and moderately plagioclase olivine-phyric pahoehoe, respectively. 5.2 Surface conductivity The surface conductivitydetermined according to the procedure described in Section 5.1 is now analyzed as a function of the CEC. At first approximation, the surface conductivity can be written as: \begin{equation}{\sigma _S} \approx \left( {\frac{1}{{F\phi }}} \right){\rho _g}{\beta _{( + )}}(1 - f)\,{\rm{CEC,}}\end{equation} (15) or alternatively as, \begin{equation}{\sigma _S} \approx \frac{1}{F}{\beta _{( + )}}(1 - f){Q_V},\end{equation} (16) where β( + )(Na+, 25 °C) = 5.2 × 10−8 m2 s−1 V−1 denotes the mobility of sodium in the free pore water. Eqs (15) and (16) are identical at very low porosity but differ by a factor (1–ϕ) at high porosities. The form of eq. (16) was first proposed by Waxman & Smits (1968) who used the following expression σS = BQV/F where B (in m2 s−1 V−1) is an apparent mobility for the counterions. Since the bulk tortuosity Fϕ is different for different samples, we plot in Fig. 13 the normalized surface conductivity σSFϕ (i.e. the surface conductivity σS is simply multiplied by the bulk tortuosity) as function of the CEC. The slope of the fitted trend is given by a = (2.0 ± 0.1) × 10−5 in the units of the international system. Since a = ρgβ( + )(1 − f) and using ρg = 2800 kg m−3 (see Table 2) and β( + )(Na+, 25 °C) = 5.2 × 10−8 m2 s−1 V−1, we obtain f = 0.86 for this collection of rock samples, typical of smectite-rich samples (see Revil 2012) and therefore consistent with the XRD analysis. Figure 13. Open in new tabDownload slide Normalized surface conductivity (σS F ϕ) (i.e. the product of the surface conductivity by the bulk tortuosity F ϕ) versus cation exchange capacity (CEC). The conductivity data have been taken at 1 Hz. All the data are shown except sample HG583. The regression coefficient falls down to r = 0.848 when the surface conductivity is plotted directly as a function of the CEC without a normalization by the tortuosity of the bulk pore space F ϕ. All the data fall on the same trend, which reflects the fact that the mobility of the counterions in the diffuse layer is the same whatever the mineralogy. The grey area is designed to capture the overall variation in the data in a log log plot while the uncertainty in the factor a is determined form a linear regression. Figure 13. Open in new tabDownload slide Normalized surface conductivity (σS F ϕ) (i.e. the product of the surface conductivity by the bulk tortuosity F ϕ) versus cation exchange capacity (CEC). The conductivity data have been taken at 1 Hz. All the data are shown except sample HG583. The regression coefficient falls down to r = 0.848 when the surface conductivity is plotted directly as a function of the CEC without a normalization by the tortuosity of the bulk pore space F ϕ. All the data fall on the same trend, which reflects the fact that the mobility of the counterions in the diffuse layer is the same whatever the mineralogy. The grey area is designed to capture the overall variation in the data in a log log plot while the uncertainty in the factor a is determined form a linear regression. In Fig. 14, we compare the surface conductivity/CEC relationship between volcanic and sedimentary rocks. Once corrected for the effect of the tortuosity of the bulk pore space (F ϕ), the trends are consistent. For volcanic rocks, the CEC needs to be measured with cobalt rather than ammonium because cobalt provides only the CEC of the clay minerals, not the zeolites (Revil et al. 2002). For comparison, the data from Revil et al. (2002) are summarized in Table 6. Figure 14. Open in new tabDownload slide Surface conductivity σS (in S m−1) versus cation exchange capacity (in C kg−1). We compare here the results from sedimentary rocks and volcanic rocks The volcanic rocks include those from this study (with a corrected tortuosity F ϕ of 3.0 for all the samples), the volcaniclastic materials from Revil et al. (2002) (with the exception of samples BU 96–8A and BU 96–8B, which are less porous than the other samples, see Table 4) and the oceanic dike samples studied by Revil et al. (1996) (normalized by the tortuosity). The data from the literature are from Bolève et al. (2007, glass beads, NaCl), Vinegar & Waxman (1984, shaly sands, NaCl), Churcher et al. (1991) (CEC for the Berea sandstone), Lorne et al. (1999, Fontainebleau sand KCl), Kurniawan (2005, clean sand, Sample CS-7U, porosity 0.1234, CEC = 4088 C kg−1), Börner (1992, sample F3 Fontainebleau sandstone, porosity of 0.068, surface conductivity of 6.6 × 10−5 S m−1, estimated CEC from the grain diameter, see Revil 2013, CEC of 5.80 C kg−3), and Comparon (2005, mixtures of MX80 bentonite and kaolinite, porosity of 0.40, estimated CEC 0.5 meq g−1 from the CEC of smectite and the mass fraction of smectite). The red filled circles are from Table 6. Figure 14. Open in new tabDownload slide Surface conductivity σS (in S m−1) versus cation exchange capacity (in C kg−1). We compare here the results from sedimentary rocks and volcanic rocks The volcanic rocks include those from this study (with a corrected tortuosity F ϕ of 3.0 for all the samples), the volcaniclastic materials from Revil et al. (2002) (with the exception of samples BU 96–8A and BU 96–8B, which are less porous than the other samples, see Table 4) and the oceanic dike samples studied by Revil et al. (1996) (normalized by the tortuosity). The data from the literature are from Bolève et al. (2007, glass beads, NaCl), Vinegar & Waxman (1984, shaly sands, NaCl), Churcher et al. (1991) (CEC for the Berea sandstone), Lorne et al. (1999, Fontainebleau sand KCl), Kurniawan (2005, clean sand, Sample CS-7U, porosity 0.1234, CEC = 4088 C kg−1), Börner (1992, sample F3 Fontainebleau sandstone, porosity of 0.068, surface conductivity of 6.6 × 10−5 S m−1, estimated CEC from the grain diameter, see Revil 2013, CEC of 5.80 C kg−3), and Comparon (2005, mixtures of MX80 bentonite and kaolinite, porosity of 0.40, estimated CEC 0.5 meq g−1 from the CEC of smectite and the mass fraction of smectite). The red filled circles are from Table 6. Table 6. Electrical conductivity data for the volcaniclastic materials (from Revil et al. 2002, Sample San Pedro to BU 96-24B). The CEC is measured with the cobalt hexamine method and is therefore relevant on the clay fraction only. ϕ ρg F σS CEC Sample (–) (kg m−3) (–) (10−4 S m−1) (meq/100 g) San Pedro 0.497 2140 21 500 13.7 BU 3A 0.313 2290 40 390 11.3 BU 3B 0.314 2290 42 610 11.3 BU 5A 0.365 2300 23 730 16.2 BU 5B 0.365 2280 27 780 16.2 BU 9A 0.246 2130 33 310 8.6 BU 9B 0.241 2140 31 310 8.6 BU 9A΄ 0.268 2320 27 280 7.9 BU 9B΄ 0.263 2250 38 200 7.9 BU 96–7A 0.302 2420 37 890 15.9 BU 96–7B 0.298 2410 49 910 15.9 BU 96–8A 0.155 2320 113 29 5.5 BU 96–8B 0.152 2300 112 32 5.5 BU 96-10A 0.254 2260 38 430 8.4 BU 96-10B 0.241 2250 45 350 8.4 BU 96-11A 0.293 2170 27 1190 23.4 BU 96-11B 0.307 2260 31 1060 23.4 BU 96-12A 0.295 2450 26 680 14.2 BU 96-12B 0.284 2450 24 900 14.2 BU 96-14B 0.328 2280 28 350 10.4 BU 96-24A 0.389 2280 19 810 15.0 BU 96-24B 0.387 2270 19 760 15.0 ϕ ρg F σS CEC Sample (–) (kg m−3) (–) (10−4 S m−1) (meq/100 g) San Pedro 0.497 2140 21 500 13.7 BU 3A 0.313 2290 40 390 11.3 BU 3B 0.314 2290 42 610 11.3 BU 5A 0.365 2300 23 730 16.2 BU 5B 0.365 2280 27 780 16.2 BU 9A 0.246 2130 33 310 8.6 BU 9B 0.241 2140 31 310 8.6 BU 9A΄ 0.268 2320 27 280 7.9 BU 9B΄ 0.263 2250 38 200 7.9 BU 96–7A 0.302 2420 37 890 15.9 BU 96–7B 0.298 2410 49 910 15.9 BU 96–8A 0.155 2320 113 29 5.5 BU 96–8B 0.152 2300 112 32 5.5 BU 96-10A 0.254 2260 38 430 8.4 BU 96-10B 0.241 2250 45 350 8.4 BU 96-11A 0.293 2170 27 1190 23.4 BU 96-11B 0.307 2260 31 1060 23.4 BU 96-12A 0.295 2450 26 680 14.2 BU 96-12B 0.284 2450 24 900 14.2 BU 96-14B 0.328 2280 28 350 10.4 BU 96-24A 0.389 2280 19 810 15.0 BU 96-24B 0.387 2270 19 760 15.0 Open in new tab Table 6. Electrical conductivity data for the volcaniclastic materials (from Revil et al. 2002, Sample San Pedro to BU 96-24B). The CEC is measured with the cobalt hexamine method and is therefore relevant on the clay fraction only. ϕ ρg F σS CEC Sample (–) (kg m−3) (–) (10−4 S m−1) (meq/100 g) San Pedro 0.497 2140 21 500 13.7 BU 3A 0.313 2290 40 390 11.3 BU 3B 0.314 2290 42 610 11.3 BU 5A 0.365 2300 23 730 16.2 BU 5B 0.365 2280 27 780 16.2 BU 9A 0.246 2130 33 310 8.6 BU 9B 0.241 2140 31 310 8.6 BU 9A΄ 0.268 2320 27 280 7.9 BU 9B΄ 0.263 2250 38 200 7.9 BU 96–7A 0.302 2420 37 890 15.9 BU 96–7B 0.298 2410 49 910 15.9 BU 96–8A 0.155 2320 113 29 5.5 BU 96–8B 0.152 2300 112 32 5.5 BU 96-10A 0.254 2260 38 430 8.4 BU 96-10B 0.241 2250 45 350 8.4 BU 96-11A 0.293 2170 27 1190 23.4 BU 96-11B 0.307 2260 31 1060 23.4 BU 96-12A 0.295 2450 26 680 14.2 BU 96-12B 0.284 2450 24 900 14.2 BU 96-14B 0.328 2280 28 350 10.4 BU 96-24A 0.389 2280 19 810 15.0 BU 96-24B 0.387 2270 19 760 15.0 ϕ ρg F σS CEC Sample (–) (kg m−3) (–) (10−4 S m−1) (meq/100 g) San Pedro 0.497 2140 21 500 13.7 BU 3A 0.313 2290 40 390 11.3 BU 3B 0.314 2290 42 610 11.3 BU 5A 0.365 2300 23 730 16.2 BU 5B 0.365 2280 27 780 16.2 BU 9A 0.246 2130 33 310 8.6 BU 9B 0.241 2140 31 310 8.6 BU 9A΄ 0.268 2320 27 280 7.9 BU 9B΄ 0.263 2250 38 200 7.9 BU 96–7A 0.302 2420 37 890 15.9 BU 96–7B 0.298 2410 49 910 15.9 BU 96–8A 0.155 2320 113 29 5.5 BU 96–8B 0.152 2300 112 32 5.5 BU 96-10A 0.254 2260 38 430 8.4 BU 96-10B 0.241 2250 45 350 8.4 BU 96-11A 0.293 2170 27 1190 23.4 BU 96-11B 0.307 2260 31 1060 23.4 BU 96-12A 0.295 2450 26 680 14.2 BU 96-12B 0.284 2450 24 900 14.2 BU 96-14B 0.328 2280 28 350 10.4 BU 96-24A 0.389 2280 19 810 15.0 BU 96-24B 0.387 2270 19 760 15.0 Open in new tab In Fig. 15, we test the two types of proposed relationships between the surface conductivity and CEC (see discussion in Section 2). It is clear that the direct scaling of the surface conductivity with the bulk tortuosity and the CEC is more robust than the scaling of the surface conductivity with the formation factor and the excess of charge per unit pore volume. This provides further validation of the POLARIS model. Figure 15. Open in new tabDownload slide Comparison between the two scaling laws discussed in the main text (Waxman and Smits model prediction versus POLARIS) with the data from this study (black filled circles) and the data from Table 6 (blue filled circles). On the left-hand panel, the surface conductivity times the formation factor is plotted versus the volumetric charge density. This scaling was first proposed by Waxman & Smits (1968) and Vinegar & Waxman (1984). On the right-hand panel, we plot the normalized surface conductivity (product of the surface conductivity by the bulk tortuosity). This normalization was proposed by Revil (2013a) and is based on volume-averaging arguments. The data shows that this second scaling law is more robust than the first one. In both cases, the line shows the linear fit to the data. For this second trend, the slope of the fitted trend is a = (2.0 ± 0.1) × 10−5 in the units of the international system. The grey areas show the proportionality trends. Figure 15. Open in new tabDownload slide Comparison between the two scaling laws discussed in the main text (Waxman and Smits model prediction versus POLARIS) with the data from this study (black filled circles) and the data from Table 6 (blue filled circles). On the left-hand panel, the surface conductivity times the formation factor is plotted versus the volumetric charge density. This scaling was first proposed by Waxman & Smits (1968) and Vinegar & Waxman (1984). On the right-hand panel, we plot the normalized surface conductivity (product of the surface conductivity by the bulk tortuosity). This normalization was proposed by Revil (2013a) and is based on volume-averaging arguments. The data shows that this second scaling law is more robust than the first one. In both cases, the line shows the linear fit to the data. For this second trend, the slope of the fitted trend is a = (2.0 ± 0.1) × 10−5 in the units of the international system. The grey areas show the proportionality trends. 5.3 Quadrature conductivity The quadrature conductivityis first determined at σw = 0.5–1.0 S m−1 (i.e. at an intermediate pore water conductivity) and at 100 Hz. In Fig. 16, the quadrature conductivity is plotted as a function of the CEC. We see that there are two trends shown by the data. The first trend is essentially due to the increase of the tortuosity at constant CEC. The second trend is due to the increase of the CEC (alteration trend) at more or less constant tortuosity. Figure 16. Open in new tabDownload slide Quadrature conductivity (absolute value) versus cation exchange capacity at 100 Hertz and 0.5–1.0 S m−1 (NaCl). The arrows show the evolution with depth (note that the sample number corresponds to the depth). The samples in the shallow part of the borehole are relatively fresh while there are relatively altered in the bottom part of the hole, especially below 700 m. The same trend can be observed at 1 kHz. The first trend is explained primarily by the effect of the mineralogy while the second trend is due to the effect of alteration (increase of the CEC associated with an increase of the clay content). Figure 16. Open in new tabDownload slide Quadrature conductivity (absolute value) versus cation exchange capacity at 100 Hertz and 0.5–1.0 S m−1 (NaCl). The arrows show the evolution with depth (note that the sample number corresponds to the depth). The samples in the shallow part of the borehole are relatively fresh while there are relatively altered in the bottom part of the hole, especially below 700 m. The same trend can be observed at 1 kHz. The first trend is explained primarily by the effect of the mineralogy while the second trend is due to the effect of alteration (increase of the CEC associated with an increase of the clay content). At a first approximation, the quadrature conductivity is written as: \begin{equation}\sigma '' \approx \left( {\frac{1}{{\alpha F\phi }}} \right){\rho _g}\beta _{( + )}^Sf\,{\rm{CEC,}}\end{equation} (17) or alternatively, \begin{equation}\sigma '' \approx \frac{1}{{\alpha F}}\beta _{( + )}^Sf{Q_V}.\end{equation} (18) Like for surface conductivity, eqs (17) and (18) are identical at very low porosity but differ by a factor (1–ϕ) at high porosities. The form of eq. (18) was first proposed by Vinegar & Waxman (1984) who used the following form σ'' = λQV/F where λ is an apparent mobility for the counterions. Since the tortuosity Fϕ of the bulk pore space is different for different samples, we plot in Fig. 17 the normalized quadrature conductivity σ''Fϕ as function of the CEC. The slope of the fitted trend is given by b = (4.7 ± 0.5) × 10−7 in the units of the international system. Since according to the POLARIS model |$b = {\rho _g}\beta _{( + )}^Sf/\alpha $| (see eq. 13) and using α = 10, ρg = 2800 kg m−3 and f  = 0.86, we obtain a mobility of the counterions in the Stern layer of |$\beta _{( + )}^S$|(Na+, 25 °C) = 2.0 × 10−9 m2 s−1 V−1 for this collection of rock samples. Figure 17. Open in new tabDownload slide Relationship between the normalized quadrature conductivity (absolute value) and the cation exchange capacity at 100 Hz and 0.5–1.0 S m−1 (NaCl). The normalized quadrature conductivity corresponds to the quadrature conductivity times the bulk tortuosity, which is equal to the product between the intrinsic formation factor and the connected porosity. This trend yields a mobility of the counterions in the Stern layer of |$\beta _{( + )}^S$| = 2.0 × 10−10 m2 s−1 V−1. Figure 17. Open in new tabDownload slide Relationship between the normalized quadrature conductivity (absolute value) and the cation exchange capacity at 100 Hz and 0.5–1.0 S m−1 (NaCl). The normalized quadrature conductivity corresponds to the quadrature conductivity times the bulk tortuosity, which is equal to the product between the intrinsic formation factor and the connected porosity. This trend yields a mobility of the counterions in the Stern layer of |$\beta _{( + )}^S$| = 2.0 × 10−10 m2 s−1 V−1. In Fig. 18, we compare the data obtained in this work with data from the literature for sedimentary rocks. Once a correction has been done for the tortuosity of the bulk pore space, the trends are consistent in terms of dependence with the CEC. The quadrature conductivity is also dependent on the conductivity of the pore water (Fig. 19), which implies a salinity correction if we want to interpret the quadrature conductivities made at different pore water salinities. Figure 18. Open in new tabDownload slide Quadrature conductivity (absolute value) versus cation exchange capacity at 100 Hz and 2.0 S m−1 (NaCl). For the volcanic rocks, the quadrature conductivity data are first corrected for the tortuosity sample by sample and then bring to a constant tortuosity of 3. Only the data belonging to the alteration trend are used. For the sedimentary rocks, the quadrature conductivity is obtained at the relaxation peak. These data show that for clayey media and ‘dirty’ sands, the quadrature conductivity close to the relaxation peak is proportional to the cation exchange capacity (the data are those discussed in Revil et al. 2015c). Figure 18. Open in new tabDownload slide Quadrature conductivity (absolute value) versus cation exchange capacity at 100 Hz and 2.0 S m−1 (NaCl). For the volcanic rocks, the quadrature conductivity data are first corrected for the tortuosity sample by sample and then bring to a constant tortuosity of 3. Only the data belonging to the alteration trend are used. For the sedimentary rocks, the quadrature conductivity is obtained at the relaxation peak. These data show that for clayey media and ‘dirty’ sands, the quadrature conductivity close to the relaxation peak is proportional to the cation exchange capacity (the data are those discussed in Revil et al. 2015c). Figure 19. Open in new tabDownload slide Evolution of the absolute value of the quadrature conductivity (at 10 Hz) as a function of the pore water conductivity for six of the core samples characterized by similar quadrature conductivities. The grey area reflects the trend between the two parameters. Note that all the samples exhibit this type of trend. Figure 19. Open in new tabDownload slide Evolution of the absolute value of the quadrature conductivity (at 10 Hz) as a function of the pore water conductivity for six of the core samples characterized by similar quadrature conductivities. The grey area reflects the trend between the two parameters. Note that all the samples exhibit this type of trend. 5.4 Relationship between surface and quadrature conductivity In Fig. 20, we plot the quadrature conductivities as a function of the surface conductivities. These ratios agree with the data obtained in the last decade for sedimentary rocks. Therefore the POLARIS model discussed in Section 1 seems valid for the volcanic rocks investigated in our study. The presence of minor amounts of magnetite does not seem to be an issue in the frequency range investigated possibly because of the size of the crystals of magnetite. Figure 20. Open in new tabDownload slide Quadrature conductivity versus surface conductivity for sedimentary and volcanic rocks. Data from Weller et al. (2013) (sands and sandstones), Woodruff et al. (2014) (oil and gas shales) and Revil et al. (2014) (Fontainebleau sandstones). The black lines corresponds to – σ″/ σS = 0.037 ± 0.02, that is, a value of the dimensionless number R equals to 0.185 (correlation coefficient r2 = 0.79). As discussed by Woodruff et al. (2014), this relationship is independent on the water saturation of the material and anisotropy. The grey area corresponds to the 98 per cent confidence interval. The quadrature conductivity for the volcanic rock samples are reported at 0.5–1.0 S m−1 (25 °C, NaCl) and 10 Hertz. Figure 20. Open in new tabDownload slide Quadrature conductivity versus surface conductivity for sedimentary and volcanic rocks. Data from Weller et al. (2013) (sands and sandstones), Woodruff et al. (2014) (oil and gas shales) and Revil et al. (2014) (Fontainebleau sandstones). The black lines corresponds to – σ″/ σS = 0.037 ± 0.02, that is, a value of the dimensionless number R equals to 0.185 (correlation coefficient r2 = 0.79). As discussed by Woodruff et al. (2014), this relationship is independent on the water saturation of the material and anisotropy. The grey area corresponds to the 98 per cent confidence interval. The quadrature conductivity for the volcanic rock samples are reported at 0.5–1.0 S m−1 (25 °C, NaCl) and 10 Hertz. 6 DISCUSSION What is the importance of surface conductivity for in situ conditions? Electrical conductivity measurement of the production water was performed during a pump test in the well from which the samples were cored. The conductivity of the production water was 523 μS cm−1, that is, σw = 0.052 S m−1 (corrected at 25 °C), its pH = 8.79; and T = 39.9 °C from a depth of 670 m below ground surface (120 m below the top of the regional water table; and at an elevation of ∼1300 m). The in situ conductivity value is therefore close to the lowest conductivity investigated in our laboratory investigations. From Figs 9 and 10, it is clear that the surface conductivity is going to be strong to dominant for these in situ conditions. It follows that alteration should strongly impact the overall electrical conductivity of the material and therefore Archie's law cannot be used alone without taking into account the contribution of surface conductivity. For instance, the study of Pierce & Thomas (2009) revealed evidence of conductive materials at depth in the Humu'ula saddle region of Hawaii Island. From this study, this conductive materials are likely a result of the alteration of the volcanic rocks. In order to assess the importance of surface conductivity for a given material, we can normalized the conductivity of this material by the conductivity of the pore water, that is, \begin{equation}\,\frac{\sigma }{{{\sigma _{\rm{w}}}}} = \frac{1}{F} + {\rm{Du,}}\end{equation} (19) where Du is the Duhin number defined as the ratio of the surface conductivity to the pore water conductivity. Using the expression of the surface conductivity, this number is given by, \begin{equation}{\rm{Du}}\,{\rm{ = }}\left( {\frac{1}{{F\phi }}} \right){\rho _g}{\beta _{( + )}}(1 - f)\frac{{{\rm{CEC}}}}{{{\sigma _{\rm{w}}}}}.\end{equation} (20) In order to check the validity of eq. (19), we plot in Fig. 21 the reduced conductivity versus the Dukhin number for four core samples characterized by the same value of the formation factor. All the data plot on the same trend indicating that the simple linear model (eq. 19) is appropriate for our dataset. The Dukhin number translates therefore the effect of alteration (described through the effect of the CEC) to the pore water conductivity. For in situ conditions, this is this number that we need to critically examine to decide how much alteration affects the resistivity tomograms in field conditions. Note that since the temperature dependence of β(+) is the same that the temperature dependence of σw, this ratio is temperature independent. Figure 21. Open in new tabDownload slide Reduced conductivity versus the Dukhin number (ratio of surface conductivity versus pore water conductivity). We use here four samples characterized by the same formation factor F (here in the range 37–44) but different surface conductivities. All the data fall on the same trend in agreement with the model. Figure 21. Open in new tabDownload slide Reduced conductivity versus the Dukhin number (ratio of surface conductivity versus pore water conductivity). We use here four samples characterized by the same formation factor F (here in the range 37–44) but different surface conductivities. All the data fall on the same trend in agreement with the model. 7 CONCLUSIONS This work offers an unprecedented analysis regarding the complex conductivity of basaltic volcanic rocks with a special focus on the relationship between surface conductivity and quadrature conductivity, and their relationship to the CEC. The following conclusions have been reached. For the rocks investigated in this study, the relationship between the intrinsic formation factor and the connected porosity conforms to Archie's law. The cementation exponent is pretty large (on average around 2.45), much larger than for sedimentary siliciclastic rocks (typically in the range 1.3–2.2). This is explained by the complex nature of the pore space. If the cementation exponent is plotted as a function of the CEC, the cementation exponent increases with the CEC from 1.5 to 2.8 when the CEC increases up to 500 C kg−1 (approximately 0.5 meq/100 g). For higher CEC values, the cementation exponent is roughly constant in the range 2.8 ± 0.4. The surface conductivity is strongly correlated with the CEC of the material measured with the cobalt hexamine chloride method. A correction for the tortuosity of the bulk pore space (defined as the product of the intrinsic formation factor and the connected porosity) improves this correlation. The quadrature conductivity is also controlled by the CEC but the correlation coefficient is less robust than for the surface conductivity. Nevertheless, the ratio between the quadrature conductivity and the surface conductivity is quite consistent with the data obtained for sedimentary siliciclastic rocks. Substantial amounts of magnetite is present in the core samples but it is unclear how this magnetite affect the complex conductivity spectra and this will need to be further investigated in future works. This work can be used to fill the gap between the modeling of the porosity and alteration of volcanoes and the geophysical observables of electrical nature (in-phase conductivity, quadrature conductivity, their dependence on frequency, the chargeability). Conversely, the results can be used to provide a better interpretation of the geophysical data in terms of porosity and alteration distributions. Acknowledgments Funding for the drilling and initial characterization of the recovered rock was provided primarily by the U.S. Army through the Pacific Cooperative Ecosystems Study Unit, with additional support from the National Science Foundation. We thank DOE (Geothermal Technology Advancement for Rapid Development of Resources in the United States, GEODE, Award #DE-EE0005513) for funding. We thank the two referees for their very useful comments and their time. REFERENCES Aizawa K. , Ogawa Y. , Ishido T. , 2009 , Groundwater flow and hydrothermal systems within volcanic edifices: delineation by electric self-potential and magnetotellurics , J. geophys. Res. , 114 , B01208 , doi:10.1029/2008JB005910 Google Scholar Crossref Search ADS WorldCat Archie G.E. , 1942 , The electrical resistivity log as an aid in determining some reservoir characteristics , Petrol. Trans. 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TI - Induced polarization of volcanic rocks – 1. Surface versus quadrature conductivity
JF - Geophysical Journal International
DO - 10.1093/gji/ggw444
DA - 2017-02-01
UR - https://www.deepdyve.com/lp/oxford-university-press/induced-polarization-of-volcanic-rocks-1-surface-versus-quadrature-Cg0msF8INV
SP - 826
VL - 208
IS - 2
DP - DeepDyve
ER -