Very fine silt Clay

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Magdalen Potter
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1 Very fine silt Clay Mineral Identification (x-ray diffraction technique) Two types of x-ray diffraction (XRD) analyses were carried out, these include bulk and clay separate analyses. Due to the high quartz content of most samples, the clay minerals could not be positively identified due to low abundance and high background noise (Figure 3.32). In addition, the common clay minerals have one or two diffraction peaks that have the same d-spacing, which make it difficult to resolve using bulk sample analysis alone. Therefore, a full protocol of tests is necessary to make a positive identification (Table 2) (Eggleton, 1995). For example, in a bulk analysis, distinction between clays can be made from the first reflection: 7.2 Å from kaolinite, 10 Å from illite, 14.2 Å from chlorite, 15 Å from smectite. However, smectite, chlorite and vermiculite may occur in the same sample and the peaks from one mineral may mask the diagnostic peak of another, eg. chlorite has a strong 7.2 Å line which obscure the 7.2 Å peak of kaolinite. Hence, in the clay separate analysis, the samples were subjected to various treatments and each clay type can then be identified using the unique d-spacing characteristics after the treatment (Figure 3.33). This additional step improves the identification of the clay minerals and provides better result quantification. Table 2. Step-wise identification of individual clay minerals Basal Spacing (d 001 ) Å Minerals As collected Mg-saturate Glycerol Heat to 330 o C Kaolinite Halloysite Illite Smectite Vermiculite Chlorite For the bulk analysis, each sample was homogenised before approximately 5 g was removed and crushed in a mortar and pestle to a fine, talc-like consistency (1-10 µm) to optimise diffraction (Moore and Reynolds, 1989). Each finely crushed sample was dried and randomly packed into a U-shaped aluminium holder covered with frosted glass slide (to overcome preferred orientation of crystallites). The glass slide was then removed and the sample was placed in a Siemens D5000 series X-ray diffractometer (Co-Kα) and scanned from 4 o to 70 o 2 theta, at a speed of 2 o per minute, and a step size of To prepare the samples for clay separate analysis, approximately 25 g of raw sample was mixed with distilled water and disaggregated using an ultrasonic probe. The probe was clamped to a retort stand and placed in a custom made sound dampening cupboard. The sample underwent a few cycles (commonly 5 cycles) of disaggregation to completely separates the fine from the coarse (medium to very coarse sand and gravel), with each cycle lasting 3 minutes, and the supernatant suspension was transferred to a centrifuge bottle (1 litre). A number of samples with high electrical conductivity (EC) were desalinised by gravity settling using centrifugation, which was carried out at 2500 revolution per minute (rpm) for 30 minutes. The resulting saline solution was decanted and the settled sample residue was mixed and re-dispersed in distilled water. Anti-flocculant agent was then added to the suspension in the centrifuge bottles and tumbled overnight. The suspensions were collected, poured into 1 litre measuring cylinders and allow to settle for 8 hrs at room temperature. Subsequently, the top 10 cm of solution, which hosts the <2 µm clay fraction according to Stoke s law, was collected using a pipette and kept in small bottles. The clay separate specimens were prepared using the clay film method, in which the solution was passed through 0.45 µm filter paper and washed generously with distilled water to flush the clay of anti-flocculant chemicals. The clay was then saturated with Mg by adding a few drops of MgCl 2, and the moist clay retained on the filter paper was transferred onto glass slide sample holders. The clay specimens were analysed using a Siemens D501 diffractometer (CuKα, operating at 40 kv and 40 ma) and scanned from 0 to 32 o at 1 o per minute. Subsequently, the analysed specimens were subjected to ethylene glycerol treatment, which expands the smectite layers. Slides of the clay sample were incubated for periods of up to two days at 50 o C in a vessel containing ethylene glycol vapour, then analysed immediately by XRD. Following, specimen dehydration was carried out in an oven operating

2 for 24 hours at 330 o C. At this temperature swelling clay minerals lose the interlayer water but retaining the basic structural integrity. To reduce the possibility of smectite re-hydration, samples were analysed with the minimal exposure to atmospheric water. The XRD results were examined using various software packages including Traces and Siemens mineral evaluation program (EVA ). For each scanned result, the background trace was subtracted and the diagnostic peaks of various minerals identified. Semi-quantitative determination of the mineral abundance was achieved using SiroQuant (marketed and distributed by Sietronics Pty. Ltd.), which is a Rietveld multiphase analysis program. This software replicates a measured diffraction pattern by employing a least-squares fitting routine which adjusts the scaling factors until the calculated profile best approximates the measured one. In the process, a differential pattern is produced which indicates the degree to which the calculated pattern replicates the measured one. However, this process does not give reliable estimates for phyllosilicate (ie clay) minerals, although the total percentage allocated to clays is considered accurate. An estimate of the amounts of the various clay minerals present was made by measuring areas beneath the various peaks in the clay separates trace, and apportioning the SiroQuant estimate of clay content to the various clays identified Mineral Identification (near infrared spectral analysis) The aim of using infrared spectroscopy is to rapidly characterise aspects of mineralogy of regolith and lithological units, and help define boundaries between those units. Variations in mineral composition and crystalline order of the kandite group minerals result in varying spectral intensities and subtle shifts in wavelength diagnostic to specific minerals (eg. illite vs. phengite). Spectral responses were measured using PIMA 1 (wavelength ranges from nm) at every metre for the GAC and GRB drill holes and mostly at every 2 m for the company drill-holes studied. These narrow sampling intervals allow the recognition of subtle changes in down-hole spectral response, which is important when delineating the depth of any boundary, eg. sediment-saprolite unconformity, or the base of weathering. The bedrock samples on the other hand were specifically chosen on an individual basis to help characterise alteration assemblages. This included the sampling of the matrix, veins, clasts and vein selvages Analytical Techniques The Spectral Geologist software allows user defined algorithms to be created which extract quantitative information, such as the absorption intensities over specific wavelength intervals, to assist in the interpretation of individual infrared spectrum (Figure 3.34). Algorithms may also be generated to calculate differences in absorption intensities across a wavelength region. A list of algorithms used in the GILMORE project is given below. PIMA View version 3.1 program allows these algorithms to be displayed as bar graphs or numerical values, in addition to a hull quotient (which is the ratio between reflectance spectrum and the background reflectance hull) colour slice (Figure 3.35). The hull quotient colour slice is created by presenting the entire wavelength range across the spectrum as thin strips of colour, with each strip depicting a small wavelength interval. Different colours represent variations in absorption intensities. Deep absorption features are red; those of intermediate intensity grades through yellow and green, and very weak absorption and background are blue. For the bar graph, consistent scales have been used for all samples to assist in delineating anomalous values amongst the numerous drill holes Interpretations Observations are carried out firstly on the Hull Quotient colour slice (Figure 3.34). A break in colour of a down-hole colour strip indicates a variation in mineral composition, which in most cases is associated with a change in lithology. For example, smectite and kaolinite dominate the sediments whereas an increase in the red hue at the right hand side of the colour slice indicates the presence of chlorite- and actinolite-bearing bedrock. Alternatively, changes in lithology can also be delineated using stacked reflectance spectra. The individual spectra are observed and mineral identification is carried out in conjunction with the use of a mineral reference system (in PIMA View ) called the spectral library, where the shapes of the reflectance signatures and wavelengths of the diagnostic absorption features were compared against the standards. Algorithm bar graphs are examined to further delineate subtle changes in spectral response (Figure 9), eg. changes in the degree of crystalline order of kaolinite, or the intensity of Fe substitution in kaolinite and chlorite (Figure 3.35). These contrasts are useful in establishing the boundaries between sedimentsaprolite, saprock-bedrock, and various alteration zones in the bedrock.

3 Algorithms Since absorption features in the spectral analysis have negative amplitude, the spectral signatures are often referred to as troughs (in contrast to peaks) and the intensity of the response over a wavelength interval is known as depth (in contrast to peak height). Column 2: d@ This algorithm measures the relative intensity of the absorption feature located in the range of 1880 nm to 1960 nm (scale range: 0-0.6, bar graphs). This is commonly called the water trough, which is indicative of the presence of hydrated minerals, notably the phyllosilicates such as smectite. Free water (eg. moist sample) also gives a response (more rounded shape) in this wavelength interval which mask other responses from hydrated minerals. Samples were air dried before analysis. Column 3: d@ This algorithm measures the relative intensity of the absorption feature located in the range of 2180 nm to 2230 nm (scale range: 0-0.6, bar graphs). The Al-OH bond is characteristically absorbed between ~2160 nm and ~2220 nm. The change in the depth and shape of this absorption feature is diagnostic of different clay types (eg. smectite, kaolin and illite) and tends to show changes in their degree of crystalline order (well-defined doublet troughs ). Columns 4 and 5: w@ This algorithm measures the wavelength of the highest absorption intensity in the range of 2180 nm to 2230 nm (scale range: nm, column 4 displays bar graphs whereas column 5 shows numerical values of the wavelengths). A shift in the wavelength of the AlOH peak within this range can indicate a change in mineralogy or variation in composition of a mineral group. For example, in sericite altered bedrock this algorithm can show variation in the composition of sericite (eg. changes in the octahedral coordinated Al) and is thus particularly useful in discriminating amongst paragonite, muscovite and phengite. Paragonite has the shortest wavelength (~2190 nm), is rich in Na and has the most Al. Muscovite has an intermediate wavelength (~2200 nm) due to a decrease of Al. Phengite has even less Al and displays the longest wavelength (~2220 nm). The recognition of phengite (which is quite resistant to weathering) in the regolith could be a useful exploration tool. However, other minerals have absorptions features in this wavelength interval, and the above applies when sericite minerals are present only. Column 6: 2180/2160 The 2180/2160 algorithm is a representation of the degree of crystalline order of kaolinite, and is calculated using two algorithms (scale range: , bar graphs). The 2160 and 2180 algorithms measure the maximum absorption intensities between nm and nm respectively. Subsequently, the gradient of an arbitrary line between these two absorption troughs was calculated using the ratio between values obtained from the 2180 and 2160 algorithms. Well ordered kaolinite has diagnostic double absorption features at ~1400 nm and ~2160 nm. A deeper and sharper trough at ~2160 nm indicates more ordered kaolinite, which is typically found in saprolite. In contrast, kaolinite hosted in regolith sediments and in saprock is disordered with weakly developed trough at ~2160 nm. Hence, the 2180/2160 algorithm is useful in delineating the sediments/saprolite unconformity, which corresponds to a sharp increase in value, and the saprolite/saprock boundary which corresponds to a subsequent decrease in value. Column 7: d@ This algorithm measures the relative depth of the absorption feature located in the range of 2235 to 2245 nm (scale range: 0-0.1, bar graphs). This small range is within the characteristic FeOH absorption trough of ~2230 to 2295 nm, and is associated with Fe substitution for octahedral Al within the kaolinite, which is commonly observed in the weathering of mafic bedrock. This algorithm is only robust in kaolinite-rich regolith materials, as minerals such as muscovite and montmorillonite can overprint and partly obscure this response. For example, saprolite that displays a deep absorption feature in this wavelength interval, which also hosts abundant sericite, indicates the presence of a weathered hydrothermal altered mafic rock. Column 8: d@ This algorithm measures the relative intensity of the absorption feature located in the range of 2240 nm to 2280 nm (scale range: 0-0.6, bar graphs). This is within the characteristic FeOH absorption range between ~2230 nm and ~2295 nm, and is useful in delineating FeOH bonds within Fe-hosting mica

4 such as chlorite and biotite. The (column 7) and (column 8) algorithms overlap in wavelengths as the former is useful in analysing regolith materials whereas the later is useful in identifying primary minerals in bedrock. Therefore an assessment of the state of weathering of the materials is required before interpreting the results. Columns 9 and 10: w@ This algorithm measures the wavelength of the deepest point in the range of 2240 to 2280 nm (scale range: , column 9 gives a bar graphs display whereas column 10 shows numerical values). The FeOH absorptions of chlorite and biotite change with varying amounts of Fe and Mg in the sample. With both these minerals a decrease in Fe will result in the shortening of the wavelength of this absorption feature, eg. Fe-poor chlorite produces absorption troughs at 2240 nm whereas those of Ferich chlorite display 2260 nm signatures. This algorithm is therefore useful for differentiating chlorite and biotite from unmineralised/unaltered bedrock to those obtained from veins and altered bedrock. Columns 11: d@ This algorithm measures the relative depth of the absorption feature located in the range of 2300 to 2360 nm (scale range: 0-0.6, bar graphs). These wavelengths are the absorption response from MgOH bonds found in chlorite and biotite. However, carbonates and epidote have similar strong absorptions in these wavelength intervals and can overprint the weaker MgOH absorption troughs. This algorithm is primarily used as an aid in identifying features in bedrock/alteration zones. Columns 12 and 13: w@ This algorithm measures the wavelength of the deepest point in the range of 2300 to 2360 nm (Scale range: , bar graphs and numerical values). Similarly to the FeOH absorption feature, changes in Mg concentrations of chlorite and biotite cause a shift to the MgOH absorption wavelength, with an increase in Mg content resulting in a shortening of wavelength Petrological studies and whole rock geochemistry A range of bedrock lithologies within the Gidginbung Volcanic Belt has been collected and analysed using petrological and various geochemical analyses. The primary aim is to characterise the saprolith (ie. mineral composition, texture, weathering intensity) across a range of different bedrock compositions to determine the potential influx of salt (as exchangeable Na + on clay minerals) from the weathering of Na-metasomatised lithologies. A total of 122 thin sections and 104 whole-rock geochemical analyses have been carried out. Of these, 8 thin sections and 5 geochemical analyses were taken from a single drill core (GDH01) to observe mineralogical and geochemical changes from bedrock through saprock to saprolith within a hydrothermally altered (sericitised and silicified) volcanic unit. The remaining samples mainly comprise of drill cuttings taken from company drill holes. Furthermore, 120 bedrock and saprolith samples have been collected for XRD analysis (refer to method on XRD analysis), with samples taken up section within individual holes and from different lithologies and various AEM signatures. In addition, 19 samples obtained from mafic-ultramafic lithologies were analysed for platinum group elements (PGE) as these lithotypes may have important implications for mineralisation models within the volcanic belt. Another 24 samples from various primary and altered lithologies have been chosen for Sm-Nd and Pb isotope analysis to constrain the age of mafic magmatism and determine the relationship between mafic, intermediate and felsic igneous lithologies Petrophysics (Munday) Down-hole Gamma log and Detailed Radioelements Study Samples were selected from project core holes GDH01, 03 and 04B on the basis of gamma (total count) logs, grainsize characteristics and regolith type. These were analysed for K U and Th on the basis of their natural gamma emissions by Dr B. Dickson at the CSIRO Exploration and Mining laboratories at North Ryde, Sydney. The aim of the study was to determine the relative importance of these radionuclides in generation of gamma emissions measured in downhole gamma logs and to help determine relationships between grainsize and gamma log. Each sample was about 10 cm of quarter core, averaging 200g. The samples were oven dried, and weighed. Loosely disaggregated samples were placed in 7 cm diameter metal canisters and allowed to stand for three weeks before the samples were analysed by counting gamma emissions at different

5 wavelengths. This delay enables radon ( 222 Rn), which may be lost during sample crushing and drying, to re-establish equilibrium with its parent 226 Ra. Standards for calibrating the analyses were potassium sulfate and two, internationally calibrated concrete samples doped with U and Th (Lovborg et al., 1978). Results were presented as % K, and ppm U and Th in the samples. These abundances can be used to calculate the relative gamma emissions of each radionuclide. To assess the relative importance of each radionuclide in the generation of the bulk gamma signal as is measured in the downhole gamma log, the K and Th are converted to U equivalents (eu) in terms of their gamma output, and all three figures converted to API (American Petroleum Institute) units. 1% K = 2.43 ppm eu 1 ppm Th = 0.49 ppm eu 1 API unit = eu Thus 1% K gives about 20 API units, 1 ppm Th about 4 API units, and 1 ppm U about 8 API units.

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