2.6.1 General overview
Reliable bed characterisation measurement techniques are unequivocally required for accurate analysis and prediction of mixed sediment settling, deposition and bed re- structuring/consolidation which are essential to improve our understanding of coastal sediment transport and morphodynamics. However, availability of simple and non- destructive measurement techniques to achieve this (for example, characterisation of spatial and temporal variation in sediment bed structure and composition) has been identified as a major challenge [Been, 1981; Ha et al., 2010; etc.].
Most traditional techniques are intrusive and end up altering the structure of the bed under investigation. An example of such is βdirect coringβ, which though still regarded as the standard testing method against which to compare measurements from other characterisation methods that require elaborate calibrations for estimation of bulk density. However, density measurements based on core samples are laborious, time consuming,
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unreliable, and its spatial and temporal resolution is very low for most applications especially in unconsolidated sediments where the sample is unlikely to be representative of the bulk material by the time it is tested (Ha et al., 2010).
Alternatively, from previous experimental studies on sedimentation process, bulk density profiles and porosities of the sediment bed deposits have been successfully obtained by passing high energy X-rays or attenuated gamma rays through the sediment bed (Been, 1981; Pane and Schiffman, 1997; Ellis, 1987; Been and Sills, 1981, Jacobs et al. 2009). These nuclear devices are based on the principle that an increase in sediment bulk density will cause the sediment to absorb more nuclear radiation (Hirst et al., 1975; Been and Sills, 1981) hence, the bulk density of the sediment can be estimated from the attenuation of nuclear radiation passing through its layers (Ha et al., 2010). However, although these X-ray/Ζ³-ray techniques are non-intrusive, they are relatively inflexible, expensive, laborious and have clear health and safety implications. In addition, field loss from these radioactive materials can lead to serious contamination problems (Ha et al., 2010).
Recently, other non-intrusive methods such as acoustic and wave attenuation (AWA) and turning fork (TF) methods have been developed (Libicki and Bedford, 1989; Maa et al., 1997; Fontein and van der Wal, 2006). AWA techniques are based on the principle that acoustic echo strength is proportional to the product of the speed of sound and density (i.e. acoustic resistivity), thus analysis of the acoustic signals returned from the sediment bed can be used as a proxy to calculate corresponding bulk density (Maa and Lee, 2002; Kaya et al., 2008). Though they have the advantages of being relatively simple and safe to use, they are however limited to the top layer of the sediment beds and their vertical resolution is relatively too low. AWA based techniques have been found to be unreliable in the presence of air bubbles and organic materials; and give varying results depending on the composition of the mud (Hydramotion Ltd, 2013). In addition, the calibration of AWA requires direct extraction of sediment sample thereby defeating the non-intrusive goal (e.g. Ha et al., 2010). In TF devices on the other hand, the bulk density of the medium under test is derived from the vibration frequency of the exposed prongs of the tuning fork (Hydramotion Ltd, 2013). They have drawbacks of being applicable only to low-density fluid mud and require complementary methods for higher density sediment layers. Furthermore, errors in the measurements are very common in these devices because granular material can easily become trapped between the prongs of the fork. (Libicki and Bedford, 1989; Dowling, 1990; Hydramotion Ltd, 2013 and Ha, et al., 2010).
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The drawbacks of the non-intrusive methods mentioned above emphasise the need for reliable, less cumbersome and non-intrusive sediment bed characterisation measurement techniques. Studies have shown that, although knowledge of the electrical properties of marine sediments is scant, the increasing use of electrical resistivity techniques to study soils has shown significant promise as a viable geophysical tool (Breitzke, 2006; Blewett et al., 2001: 2003; Lovell, 1985; Jackson, 1975; Dai et al., 2009; Samouelian et al., 2005; Schlaberg et al., 2006; te Slaa, et al., 2013). Hence, one of the main objectives of the current study is to explore and develop a non-invasive characterisation technique based on the principle of electrical resistivity to characterise the spatial and temporal variation in sediment bed structure and composition resulting from differential settling behaviour of the sediment mixtures, without most of the various limitations of other techniques highlighted above.
2.6.2 Electrical resistivity and sediments
The electrical resistivity measurement technique (ERMT) is based on the principle that, when an electric current passes through water-saturated marine sediments, the electrical resistivity of the sediments will depend on the resistivity of both the solid (sand-mud fractions) and fluid components. Hence different combinations of these components should, in theory, have different resistivities associated with them (Breitzke, 2006). As the sediment grains are insulators (or at least poor conductors), it has been concluded that the propagation of electric current takes place via the interstitial pore fluid (Jackson, 1975; Dowling, 1990; Lovell, 1985; Breitzke, 2006). Many researchers have recorded that the dominant transport mechanism for electrical current propagation in a pore fluid is by ionic (electrolytic) conduction. Therefore, current propagation within the water-saturated sediments actually occurs through the pore spaces and hence, the resistivity of the sediment bed has been noted to depend both on the conductivity of the pore water and the microstructure of the sediment (e.g. porosity, pore geometry, grain surface morphology and dielectric properties of the mineral grains) [Kanagy and Mann, 1994; Salem, 2001; Wildenschild et al., 2000; Roberts and Wildenschild, 2004; Breitzke, 2006; Metayer et al., 2010]. Therefore, this dependency or relationship can then be used as a proxy to estimating bulk density.
Electrical conductivity of the pore fluid is, therefore, a function of salinity, pH, dissolved ions/molecules mobility, and concentration (i.e. fluid saturation); while that of sediment microstructure is controlled by the amount and distribution of the pore space, and its
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capillarity and tortuosity; common to both is sensitivity to temperature (Breitzke, 2006; te Slaa et al., 2013). As a consequence, the electrical resistivity of marine sediments cannot be considered as a bulk parameter solely dependent on relative amount of solid and pore fluid in the bed deposit. However, it has been successfully shown (Archie, 1942; Winsauer et al., 1952; Boyce, 1968; Taylor Smith, 1971; Erchul and Nacci, 1972; Jackson, 1975; Sen et al., 1981; Lovell, 1985; Wildenschild et al., 2000; Samouelian et al., 2005; Breitzke, 2006; Blewett et al.,2001; 2003; Ibikunle et al., 2014) that it can be correlated to bed porosity, wet bulk density and permeability, provided an adequate calibration to a typical sediment composition can be carried out. Recently, electrical conductivity measurements have been successfully correlated to sediment mass concentration [te Slaa et al. (2013)] and suspended sediment concentration (Dai et al., 2009).
2.6.3 Theory of Electrical resistivity technique: Galvanic method
There have been several models developed to describe electric flow through solid structure like rocks and water-saturated sediments theoretically (Sen et al., 1981; Waxman and Smits, 1968, Ruffet et al., 1991), but, in practice, because often only few of the required model parameters are known, these models are not very useful, and the most widely preferred model was an empirical equation proposed by Archie in 1942 (Breitzke, 2006). Conventional treatment of rock resistivity data (Archie, 1942 and Winsauer et al., 1952) has been to use a term known as the formation factor F to define a normalised resistivity which is the ratio of bulk deposit resistivity of the saturated rock πππ’ππ to the
resistivity of the saturating liquid ππ, and relating F to the porosity οͺ through the following relationship (Archie, 1942),
πΉ = Οππ’ππ
Οπ = πΟ
βπ (2-33)
where exponent m is known as the cementation factor and is related to the tortuosity and connectivity of the pore network within the rock; a is an empirically-derived coefficient of saturation (Winsauer et al.,1952 and Breitzke, 2006), which is valid over a particular range of porosities οͺ. Similar to those of a, values of m are also determined empirically and are characteristic for a given porous rock system. A wide range of values have been reported for m and a for different rock and sediment formations, with a, typically in the range 0.4β2.5 and m (m > 1), typically ranging between 1.2 β 3.5 (e.g. Worthington 1993;
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Bassiouni, 1994; Devarajan et al., 2006; Khalil and Santos 2011). It should be noted that, Archie (1942) postulated that the formation factor F is a constant independent of resistivity of the liquid and solely a function of pore geometry.
This model [i.e. Equation (2-33)] has been extended successfully to study sedimentation of clay slurries (Blewett et al., 2003; te Slaa et al., 2013), where F is defined as the ratio of the bulk resistivity of the clay-water mixture πππ’ππ to that of the water phase ππ, with
a and m, again, empirical coefficients. Equation (2-33) can thus be used, provided appropriate calibration is carried out, to determine more physically-relevant properties of a porous material, example of such is solids volume concentration ο¦s (i.e. ratio of the
volume of solids to the total wet volume) expression derived by te Slaa, et al. (2013), i.e.
ο¦π = [1 βΟΟπ
π€] Γ₯ (2-34)
where Γ₯ is an empirically-derived coefficient, Οπ€ and Οπ are the conductivity of water
and the sediment-water mixture respectively (note: conductivity is the reciprocal of resistivity). From Equation (2-34), the corresponding mass concentration cs can be
computed via cs = ο¦s.ο§s, where ο§s is the density of the sediment particles.