18 December 1999 at Vandenberg air force base, California, .... King 1958, Forbes 1991, Bierlein et al. .... Before the excavation of the costeans, a 50 Ã 50 m spaced sampling grid of 70 points (10 ..... which was approximately 20 m long and was excavated to a depth of 6 m, as shown in .... The horizon directly below (C9).
APPENDIX 1: HYPERION Hyperion data can be purchased from Geoscience Australia (GA). The following text is from GA’s website: http://www.ga.gov.au/acres/prod_ser/eo1price.jsp
“EO-1 HYPERION AND ALI Products derived from the Hyperion and ALI sensors on board the EO-1 satellite are available from Geoscience Australia through a special arrangement with the NASA and the United States Geological Survey. The EO-1 mission was initially experimental until the end of 2001. However, because of continued demand the satellite operators have extended the mission on an operational basis until at least September 2007. The Hyperion sensor onboard the satellite is the first hyperspectral sensor on an Earth observation satellite. It covers the complete spectral range from 0.4 to 2.5 µm in 220 bands. Such comprehensive spectral resolution permits the performance of very detailed land cover classifications or identifications. The Advanced Land Imager (ALI) was designed to demonstrate improved Landsat spatial and spectral resolution with substantial mass, volume and cost savings. Its proven performance has the potential to reduce the cost and size of future Landsat-type instruments by up to 5 times. Details of the two sensors are shown in the image below. Early in 2002, CSIRO and Geoscience Australia coordinated a bulk purchase of EO-1 data from the United States Geological Survey, predominantly for CSIRO users. This initial bulk purchase involved acquisitions for a limited time until March 2002. Subsequently, the satellite mission has been extended and Geoscience Australia has been able to negotiate the continued supply of EO-1 products to Australian customers under the terms and conditions outlined below. Product specifications
The EO-1 satellite follows the same orbit as Landsat 7 by about one minute. However, unlike Landsat 7, EO-1 can be pointed sideways (even into adjacent Landsat paths) so that customers can define any point on which to centre their acquisition. Note that for the one acquisition, the centre of a Hyperion swath is different to the centre of an ALI swath, as shown in the image
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left. Please note that there may be programming constraints and extra fees incurred for the satellite to be tilted to image an off-nadir pass. Table A1.1: Hyperion and ALI Specifications Parameter
Hyperion
ALI
Swath width (km)
7.7
37
Product Length (km)
42 or 185
42 or 185
Spatial resolution (metres)
30
30
Processing Level
Level (radiometrically corrected only)
Format
HDF CEOS
1R: HDF CEOS; 1G: HDF CEOS or GeoTIFF
Approx file sizes
~200Mb
~300Mb
Media
DVD
DVD
Spectral range (µm)
0.4 - 2.5
0.4 - 2.5
Spectral resolution (nm)
10
Variable
Spectral coverage
Continuous
Discrete
Pan band (metres)
N/A
10
220
10
resolution
Total number of bands
1R
Level 1R (radiometrically corrected only) or Level 1G (radiometrically and geometrically corrected and georeferenced)
Quick Look images Low-resolution Quick Look images are available some time after acquisition from the EDC EarthExplorer tool. However, ACRES Landsat 7 Quick Looks taken 1 minute prior to the EO-1 overpass may also be used for cloud assessment, assuming EO-1 is not pointing outside of the Landsat 7 swath. Landsat 7 Quick Looks are available on the ACRES Digital Catalogue within four hours of acquisition. Terms and conditions data acquisition request (DAR) A DAR allows a customer to provide the information necessary to schedule an acquisition over their area of interest. A new DAR is required for each area or data range. Scheduling conflicts and satellite maintenance may affect instrument availability. A DAR should be placed at least 30 days prior to the required acquisition date to provide the maximum chance of the request being granted. Please allow five business days for a reply to your DAR submission. Each order involves a DAR and subsequent data processing. Please contact ACRES if you wish to submit a DAR. After EO-1 has imaged the DAR location and the data are received, a cloud cover assessment will be performed over the entire image by USGS/EROS. If the image contains less than 20% cloud cover, the DAR will be considered fulfilled and, if applicable, you will be billed the $1,275 DAR fee. A browse image is sent to you by e-mail for your review and approval; upon your approval, the data product will be generated and shipped. If the image contains more than 20% cloud cover, EROS will reschedule the target scene up to two additional times at no extra cost. Typical timelines from down-link to distribution are approximately one to four weeks.
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Orders need to include: • site Lat/Long centre in decimal degrees • area name and country • site name • pointing mode i.e. off-nadir or within nadir • sensor i.e. ALI or Hyperion • scene length, i.e. 42km or 185km • date/s for acquisition window. It is best to provide at least a three month acquisition window to allow adequate time for fulfilment of the DAR.”
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APPENDIX 2: QUICKBIRD QuickBird is a high-resolution commercial Earth observation satellite, owned by DigitalGlobe and launched in 2001 as the first satellite in a constellation of three scheduled to be in orbit by 2008. QuickBird collects the second highest resolution commercial imagery of Earth after WorldView-1, and boasts the largest image size and the greatest on-board storage capacity of any satellite. The satellite collects panchromatic (black and white) imagery at 60–70 cm resolution and multispectral imagery at 2.4- and 2.8-m resolutions. (http://www.wikipedia.org) The following text is quoted directly from the Satellite Imaging Corporation Website: http://www.satimagingcorp.com/satellite-sensors/quickbird.html
“QuickBird satellite images and sensor specifications Because of our relationship with DigitalGlobe, developer and owner of the QuickBird Sensor, Satellite Imaging Corporation (SIC) acquires QuickBird Satellite Imagery worldwide for our customers seeking high-resolution, digital aerial photographs.
QuickBird at Launch; QuickBird in Orbit
About the QuickBird satellite sensor QuickBird is a high resolution satellite owned and operated by DigitalGlobe. Using a state-ofthe-art BGIS 2000 sensor (PDF), QuickBird collects image data to 0.61m pixel resolution degree of detail. This satellite is an excellent source of environmental data useful for analyses of changes in land usage, agricultural and forest climates. QuickBird's imaging capabilities can be applied to a host of industries, including Oil and Gas Exploration & Production (E&P), Engineering and Construction and environmental studies
QuickBird satellite sensor characteristics Table A2.1:Quickbird Sensor
Launch Date Launch Vehicle Launch Location Orbit Altitude Orbit Inclination Speed
October 18, 2001 Boeing Delta II Vandenberg Air Force Base, California, USA 450 Km 97.2°, sun-synchronous 7.1 Km/sec (25,560 Km/hour)
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10:30 AM (descending node) Equator Crossing Time 93.5 minutes Orbit Time 1-3.5 days, depending on latitude (30° off-nadir) Revisit Time 16.5 Km × 16.5 Km at nadir Swath Width 23 meter horizontal (CE90%) Metric Accuracy 11 bits Digitization Resolution
Image Bands
Pan: 61 cm (nadir) to 72 cm (25° off-nadir) MS: 2.44 m (nadir) to 2.88 m (25° off-nadir) Pan: 450–900 nm Blue: 450–520 nm Green: 520–600 nm Red: 630–690 nm Near IR: 760–900 nm
Archived or new QuickBird imagery from the QuickBird satellite sensor For many image requests, a matching image can already be located in the archives of highresolution QuickBird imagery from around the world. If no image data is available in the archives, new QuickBird satellite image data can be acquired through a satellite tasking process. For more information and pricing, please visit the ‘Contact Us’ page on the website.”
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APPENDIX 3: ASTER The following text is quoted directly from the Satellite Imaging Corporation website http://www.satimagingcorp.com/satellite-sensors/aster.html
“ASTER SATELLITE IMAGERY Satellite imaging corporation (SIC) acquires Aster satellite imagery worldwide. ASTER is one of the five state-of-the-art instrument sensor systems on-board terra a satellite launched in December 1999. It was built by a consortium of Japanese government, industry, and research groups. ASTER monitors cloud cover, glaciers, land temperature, land use, natural disASTERs, sea ice, snow cover and vegetation patterns at a spatial resolution of 90 to 15 meters. The multispectral images obtained from this sensor have 14 different colors, which allow scientists to interpret wavelengths that cannot be seen by the human eye, such as near infrared, short wave infrared and thermal infrared.
Aster is the only high spatial resolution instrument on terra that is important for change detection, calibration and/or validation, and land surface studies. Aster data is expected to contribute to a wide array of global change-related application areas, including vegetation and ecosystem dynamics, hazard monitoring, geology and soils, land surface climatology, hydrology, land cover change, and the generation of digital elevation models (DEMS). Satellite imaging corporation (SIC) is an official distributor for aster imagery through USGS.
Archived and new ASTER Imagery For many image requests, a matching image can already be located in the archives of ASTER imagery from around the world. If no image data is available in the archives, new ASTER satellite image data can be acquired through a satellite tasking process.
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ASTER satellite system: sensor characteristics Table A3.1: ASTER Orbit Specifications
Launch date Equator crossing
18 December 1999 at Vandenberg air force base, California, USA 10:30 am (north to south)
Orbit
705 km altitude, sun synchronous
Orbit inclination
98.3 degrees from the equator
Orbit period
98.88 minutes
Grounding track repeat cycle
16 days
Resolution
15 to 90 meters
The ASTER instrument consists of three separate instrument subsystems: • VNIR (visible near infrared), a backward looking telescope which is only used to acquire a stereo pair image • SWIR (shortwave infrared), a single fixed aspheric refracting telescope • TIR (thermal infrared) ASTER high-resolution sensor is capable of producing stereoscopic (three-dimensional) images and detailed terrain height models. Other key features of ASTER are: • Multispectral thermal infrared data of high spatial resolution • Highest spatial resolution surface spectral reflectance, temperature, and emissivity data within the terra instrument suite • Capability to schedule on-demand data acquisition requests ASTER has 14 bands of information. For more information, please see the following table: Table A3.2: ASTER Footprint
Instrument
VNIR
SWIR
TIR
Bands
1–3
4–9
10–14
Spatial Resolution
15 m
30 m
90 m
Swath Width
60 km
60 km
60 km
Cross Track Pointing
± 318 km (± 24 deg)
Quantisation(Bits)
8
± 116 km (± 8.55 deg) 8
± 116 km (± 8.55 deg) 12
Images are orthorectified and ready to use in your preferred GIS or remote sensing software.
ASTER VNIR—Visible and near infrared VNIR data at 15m resolution is currently the best resolution multispectral satellite data available commercially, with the exception of very high resolution data like IKONOS or QuickBird. The VNIR subsystem operates in three spectral bands at visible and near-infrared wavelengths, with a resolution of 15 meters. A comparison with the panchromatic 15m band
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on the LANDSAT 7 ETM+ data shows that ASTER imagery is better both spatially and spectrally.
ASTER band characteristic details Table A3.3: ASTER Band Characteristics
Band
Wavelength in microns
View angle
Visible Near Infrared 15m resolution B1 VNIR_Band1
0.520–0.600
Nadir
B2 VNIR_Band2
0.630–0.690
Nadir
B3 VNIR_Band3N
0.760–0.860
Nadir
B4 VNIR_Band3B
0.760–0.860
Backward
Short-wave Infrared 30m resolution B5 SWIR_Band4
1.600–1.700
Nadir
B6 SWIR_Band5
2.145–2.185
Nadir
B7 SWIR_Band6
2.158–2.225
Nadir
B8 SWIR_Band7
2.235–2.285
Nadir
B9 SWIR_Band8
2.295–2.365
Nadir
B10 SWIR_Band9
2.360–2.430
Nadir
Long-wave Infrared or Thermal IR 90 m resolution B11 TIR_Band10
8.125–8.475
Nadir
B12 TIR_Band11
8.475–8.825
Nadir
B13 TIR_Band12
8.925–9.275
Nadir
B14 TIR_Band13
10.250–10.950
Nadir
B15 TIR_Band14
10.950–11.650
Nadir
“
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APPENDIX 4: MINERAL MAPPING IN THE WHITE DAM REGION OF THE OLARY DOMAIN OF SOUTH AUSTRALIA Ian C Lau, Alan J Mauger, Graham S Heinson, Patrick R James. 15th of May 2008
CSIRO Exploration and Mining PO Box 1130 Bentley WA 6102 Australia Ph: +61-8-6436 8646 Fax: +61-8-6436 8586
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Introduction The following report is a summary of the research performed between February 2001 and August 2004 by Ian Lau as part of a PhD in the Olary Domain, which was funded by the Cooperative Research Centre for Landscape, Environment and Mineral Exploration (CRC LEME). The work was performed under the Mineral Mapping South Australia Project, managed by Dr Alan Mauger of the Primary Industries and Resources of South Australia (PIRSA). The study was located in the eastern Olary Domain of South Australia. The area was selected because of the acquisition of airborne hyperspectral data in 1998 by the former exploration lease holder, Mount Isa Mines. The area had been surveyed to investigate the mineral exploration potential of the newly developed HyMap instrument. The lease consisted of the White Dam copper-gold-molybdenite (Cu–Au–Mo) prospect and the ‘Wilkins’ and ‘Green and Gold’ epigenetic Fe oxide–copper–gold (FeO–Cu–Au) deposits. This work aimed to improve the knowledge of: • spectral properties of regolith materials in relation to airborne hyperspectral imagery • mineralogy of regolith materials across the surface and through the profile • methods used in remote sensing and spectral investigations in regolith-dominated terrains • analytical techniques of remote technology to identify materials and map regolith-landforms. This research also aimed to demonstrate the value of hyperspectral data for deriving mineralogical information in weathered terrains. The final outcome—once these pieces of information had been extracted—was the integration of data and creation of regolith-landform and regolith mineralogy maps. The White Dam Prospect was chosen as the site to perform detailed examination of regolith mineralogy because of an abundance of geochemical and exploration data collected by previous lease-holders. Extensive drilling of the area had defined the extent of Cu–Au mineralisation at depth. However, surface and shallow data pertaining to regolith materials were less understood. The study integrated airborne imagery with spectral measurements of soil and rock samples collected from the surface, drillholes and subsurface costeans. Data were integrated into a geographical information system (GIS) with geophysical datasets and digital elevation models (DEMs), which aided the mapping and interpretation of regolith-landform units. Mineral distribution maps of the surface and the costeans over the prospect were interpreted from the spectral analysis. Regional mineral and surface material maps were created to complement the regolith-landform map of the area covered by the hyperspectral data. Quantitative X-ray diffraction (XRD) analyses were performed on selected samples to validate the spectral results. Diamond drill materials were also examined using the HyLogger-1™ to provide spectral information of fresh materials from deeper in the profile.
Regional geology of the study area The study area is situated within the Olary Domain of the Curnamona Province, which lies on the South Australian side of the border, adjacent to Broken Hill. The area is dominated by transported materials; however, the basement lithologies play an important role in the current geomorphology and geological setting. The basement exposures display varying degrees of weathering, which has altered their mineralogical properties.
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The geology of the basement rocks has been well mapped and documented by the South Australian Geological Survey (e.g. Thomson 1970; Clarke et al. 1986; Flint and Parker 1993; Robertson et al. 1998). The geological units consist of deformed Palaeoproterozoic to Mesoproterozoic rocks exposed as inliers between corridors of Neoproterozoic Adelaidean rocks, which are all partially covered by Tertiary and Quaternary sediments (Willis et al. 1983; Clarke et al. 1986; Robertson et al. 1998), as shown in Figure A4.1. The Olary Domain is a highly prospective area for mineral deposits and contains numerous Cu–Au and base metal mineral occurrences. A large number of small mineralisation occurrences have been discovered and documented by detailed summaries in Campana and King 1958, Forbes 1991, Bierlein et al. 1994, Bierlein et al. 1996, Cook et al. 1994, Ashley et al. 1998, Robertson et al. 1998, Skirrow et al. 1999, Leyh and Conor 2000, Skirrow and Ashley 2000 and Williams and Skirrow 2000. Mineralisation associated with quartz-magnetite rocks is common in the Olary Domain, which contains numerous Fe-alteration-related deposits, including the Wilkins and Green and Gold Cu–Au prospects, which are associated with epigenetic quartz-magnetite bearing ironstones (Campana and King 1958). Two discontinuous east–west striking banded iron formation (BIF) horizons are observed in the Green and Gold area with the dimensions of 150 m length by 2.5 m wide and dip steeply to the north. Recent sampling of regolith carbonate accumulations (RCAs) and vegetation from transects over the Wilkins and Green and Gold prospects found anomalous Au concentrations over the mineralisation and elevated values downslope. The Au concentrations returned to background levels across on the opposite side of the catchment area, reflecting the localised occurrence of the anomaly (Hill SM 2003 pers. comm.).
White Dam local geology The area of study occurs at the southwestern margin of the Kalabity Inlier in the eastern Olary Domain (Figures A4.1, A4.2). The Kalabity Inlier is the largest of the Willyama Supergroup inliers and extends to the north towards the Benagerie Ridge, where it is overlain by Mesoproterozoic volcanics, Neoproterozoic rocks and Cainozoic Callabonna Sub-Basin sediments. The MacDonald Fault marks the southwestern margin of the inlier, where the Adelaidean siltstone is faulted against the older Willyama Supergroup basement. Intrusions of granite, pegmatite, amphibolite and dolerite occur throughout the study area and display different fabric orientations. The White Dam area has experienced regional pre- and syntectonic sodic and calcic, as well as localised potassic, alteration, which is evident by widespread quartz-albitites and albitic rocks (Skirrow and Ashley 2000). Retrograde alteration has also occurred in the Mingary shear zones 10 km to the south of the White Dam Prospect. Exploration and mineralisation of the White Dam Prospect The White Dam area was extensively explored in the early 1990s by Mount Isa Mines, with regional soil sampling surveys performed, followed by a detailed survey of the regions containing anomalous Au concentrations. Drilling was performed in late 1996–early 1997, which identified the mineralisation (McGeough and Anderson 1998). Over 900 air core holes have been drilled, along with 140 RC and 12 diamond holes. WD17 (shown in Figure A4.17) was drilled into the hinge of the recumbent fold and intersected the banded leucocratic gneiss and calc-albitite (Busutill and Bargmann 2003). Since 2000, there has been a renewed interest in the prospect. Polymetals Mining Services Pty Ltd and EXCO Resources N.L. have recently performed further infill drilling and excavated six costeans (location shown in Figure A4.4) to explore the extent of the mineralisation, as well as test the suitability of the saprolite for acid leach extraction techniques (Cooke 2003).
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Oxidation of the lower saprock had reached a maximum depth of 85 m over the northern portion of the mineralisation (Busutill and Bargmann 2003), near the alluvial channel. The modern water table lies at approximately 50 m. Minor supergene enrichment processes have occurred above the hypogene zone, leading to the formation of native Cu, chalcocite, malachite, covellite and bornite (Cordon 1998; Chubb 1999). Gold grades are similar for the hypogene and supergene zones: a phenomenon attributed to the disseminated nature of the pyrite in the host rock. The material in the supergene (weathered/oxidised) zone has a lower density because of weathering processes (Busutill and Bargmann 2003). Supergene dispersion and enrichment is thought to have been minimised by the buffering effect of albite and potassium feldspars (Chubb 1999).
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Figure A4.1: Simplified map of the Curnamona Province showing the major groups of lithologies, topographic information and boundaries of domains and sub-regions. The Curnamona Province extends further to the north than shown on the map. The field area of the research is shown by the grey northeast orientated rectangle in the lower central portion of the map. The geology is overlayed on a true-colour Landsat TM image.
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Figure A4.2: Digital elevation model of the study area generated from data supplied by PIRSA. The original elevation data was derived photogrammetrically and used to ortho-rectify the aerial photography in the production of ortho-photographic images. The southern areas of higher topographic relief are associated with slightly weathered bedrock, the flat central regions with plains of alluvial materials and the northern region with low hills of highly weathered saprolite.
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Surface mineralogy of the regolith Mineralogy of regolith materials from the White Dam region were investigated through the analysis of field samples using VNIR and SWIR spectroscopic and XRD techniques. Surficial materials were collected within the HyMap coverage area, with a detailed examination of the materials over the White Dam Prospect. Figure A4.3 shows the distribution of sampled localities on a regional scale and Figure A4.4 shows the surface samples collected at the White Dam Prospect. The mineralogy was determined through the examination of spectral measurements, which were validated using quantitative XRD analysis. A comparison was made between the spectral mineralogical interpretations from the data collected in the field and airborne hyperspectral data.
Figure A4.3: Sample localities across the White Dam area. The small transparent circles represent sites where a GPS measurement and site descriptions were recorded. The filled circles represent sites where photographs were taken and the large transparent circles represent sites where a sample was collected and spectral measurements were recorded. Sites were used to validate remotely sensed imagery and characterise the regolith-landforms.
Sampling of the White Dam Prospect Surface samples Before the excavation of the costeans, a 50 × 50 m spaced sampling grid of 70 points (10 samples per east–west traverse) was collected by Brown, A.D. 2003 (pers. comm.) over the White Dam Prospect (Figure A4.4). Soil samples consisted of scrapings of the top surface, which were collected without the larger surface lag material and vegetation debris. The samples were collected from topographically elevated mounds, away from the downslope paths of drill spoils. Larger fractions (>20 mm) of surface lags and other obviously transported materials were not collected, because they were not representative of the underlying soil and could contain contamination from another source. Stakes placed by EXCO Resources N.L. and Polymetals Mining Services Pty Ltd—marked with AGD 66/AMG 54 coordinates and elevation information—were used to orientate the sampling grid. Spectral measurements were not collected in situ because of the unavailability of a spectrometer at the time the field work was undertaken.
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Figure A4.4: Sample points overlain on the White Dam Prospect 1:2,000 Regolith-landform map compiled by Brown and Hill (2003), displaying the costean locations (red font) with respect to the regolith-landform units. Arrow shows the orientation of Figure A4.12. Surface soil samples (black font) increase in numerical order from left to right and to the south.
Sample preparation and measurement The surface and costean samples were separated into separate datasets and analysed independently. A spectral library of representative spectra of the soil measurements was imported into the CSIRO-developed software, ‘The Spectral Geologist’ (TSG™) version 4b, for analysis. A spectral library of the costean samples was imported as a separate file, with an information log consisting of geographic coordinates, elevation, depth, regolith-landform unit, Munsell colour, Munsell code, XRD results, quantitative XRD results (where analysis was performed on the corresponding sample) and a description of the sample from field and photographic interpretations.
Results and data analysis of surface samples ASD spectral analyses of surface samples The results of TSG™ discussed throughout the text refer to the results of the estimated mineral abundances from interpretations made by The Spectral Assistant (TSA™) algorithms on the measured spectra in comparison to inbuilt spectral libraries. These results are referred to as ‘mineral/material’TSA mineralogy abundances. Calculations made from spectral parameters by the use of indices or band ratios are referred to as TSG™ results, as they are calculated using the software. For example, kaoliniteTSA would refer to the abundance of kaolinite calculated by TSA™ from the comparison of the spectrometer data with the spectral absorptions in the TSA™ mineral library. SmectiteTSG refers to the calculation of the abundance of smectite minerals calculated from a TSG spectrum using the spectral characterisitics of clay minerals.
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Figure A4.5: Collective ASD measurements of the soil-grid samples. Each group of ten spectra represents a west to east traverse. Samples were collected 50 m apart. WD01 represents the most northwestern sample and WD70 was collected in the southeastern corner of the grid. The lithic fragments found in the samples WD68 and WD70 are shown in (viii).
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KaoliniteTSA, montmorilloniteTSA, illiteTSA, Fe2+ goethiteTSA and hematiteTSA were found to be the most abundant minerals. Minor minerals consisted of ankeriteTSA, magnesiteTSA and topazTSA, all of which corresponded to lithic fragments from the surface lag component of samples WD069 and WD070 (Figure A4.5 viii). All samples—except for WD058, WD069 and WD070—were identified by TSA™ as having kaoliniteTSA as the secondary mineral that contributed to the spectral features of the SWIR region. The three samples that did not contain kaoliniteTSA were collected from the regolith-landform unit (RLU) mapped as slightly weathered saprolite on an erosional rise (SSer1): corresponding to the areas of more prominent surface lags of lithic material. Analysis of gravel-sized lithic fragments produced absorption features that contrasted with the soil materials: reflecting the primary mineralogy of the spectrally active materials in the local basement rocks. Creation of mineral distribution maps The spectral calculations and TSA™ mineral abundances were exported to a spreadsheet and edited for import into ArcView™ (version 3.3). A point shapefile containing the spatial information and mineral abundances was created in ArcView™ and used in ArcGIS™ (version 8.3) to generate surface grids. An inverse weighted distance (IWD) technique was used on the approximately even-spaced grid points to produce a surface distribution map of the spectral calculations and TSA™ mineral abundances of the soil samples. Negative and null values in the distribution maps created by the gridding technique were removed in ArcView™. Selective results of the surface soil ASD measurements are shown in Figure A4.6 and Figure A4.7.
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Figure A4.6: Surface mineralogical distribution over the White Dam Prospect, interpolated from ASD measurements and analyses of soil samples. Saprolite is exposed in the SE corner of the area and displays high-interpreted abundances of hematiteTSA, as does the central portion of the area, which corresponds to the surface projected mineralisation outline (not shown). The area above mineralisation also displays a higher abundance (although, still small) of chlorite/epidoteTSG related spectral features. These highs are associated with the northeasttrending alluvial erosional depression and could be a collection of transported ferromagnesian minerals derived from the outcrop upslope to the south.
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Figure A4.7: Surface mineralogical distribution over the White Dam Prospect, interpolated from ASD measurements and analysis of soil samples. Comparisons of Al-OH-related wavelength features and minerals. Overall, the abundance maps display minor to high correlations.
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Conclusions of the ASD soil sample measurements The soil samples displayed subtle spectral features with small variations in the depths and inflection features of absorptions. These were attributed to minor changes in the mineral abundances of the samples, which were related to the location of the sample in the landscape. Soil samples collected from alluvial regions displayed high Fe-oxide intensitiesTSG, which were reflected by higher abundance of goethiteTSA. The alluvial channel and surrounding region displayed a high Al-OH intensityTSG, which was accompanied by high kaoliniteTSA abundance and crystallinityTSG. The outcome of the crystallinity indexTSG was surprising, because the materials of the channel are of a transported origin. The transport of highly crystalline kaolinite would lead to the destruction of the crystal lattice and, hence, a low crystallinity. Therefore, the kaolinite in the alluvial region may be derived from the in situ weathering of feldspars and other lithic materials after transport and deposition. An alternative explanation of the higher crystallinityTSG of the materials in the alluvial channel involves the presence of drill spoil materials at the surface and their transport to the areas lower in topography. The presence of drill spoils can be seen on the 1997 airphotographs (Figure A4.17) and the 1998 HyMap imagery (Figure A4.25 viii). Observations of the site over the duration of the project (2001 to 2004) noted a decrease in the abundance of spoil material (from the pre-1997 drilling) at the surface. It is proposed that the spoils have been denuded by erosive processes since their formation, which has led to the dispersion of the materials to the lower-lying regions in the area; however, this is unlikely to be the cause of the higher kaolinite abundances in the channel. Spectral measurements of samples collected in 2001 showed the mineralogy of the spoils to vary from muscovite-rich to highly crystalline kaolinite with low abundances of Fe-oxides. Further SEM analysis on the soil samples in the alluvial channel would be required to determine their origin and if the kaolinite has been formed in situ. The bedrock exposure displayed a hematiteTSA and montmorilloniteTSA spectrally interpreted mineralogy. The saprolite was also found to have high abundances for the chlorite/epidote indexTSG and longer Al-OH wavelengthTSG features. The sheetwash slopes adjacent to the saprolite exposures displayed similar mineralogical characteristics, which were attributed to the presence of lithic material derived from the neighbouring basement. Sheetflow-dominated regions to the north of the polygon labelled Aed (in the centre of the area) were influenced by the transport of material from upslope. Minor alluvial additions from flood events were seen in the sheetflow RLUs close to the drainage features. This resulted in smectiteTSG-dominated mineralogy and a low crystallinityTSG for kaolinite. XRD analysis of surficial materials XRD is an analytical tool involving the measurement of the characteristic angle of scattered X-ray radiation from the interaction with electrons in atoms of crystalline minerals (Jenkins 1974). A suite of samples were selected for XRD analysis from the surface and the surficial portions of the profiles collected from the costeans (Figure A4.4). Additional samples selected from the costeans were chosen to verify the mineralogy identified from spectral measurements with the ASD FieldSpec Pro™ spectrometer. The samples selected for XRD consisted of a northwest trending traverse of the 50 m spaced grid, which was surveyed to coincide with the projected mineralisation at the White Dam Prospect. Clay separation analysis was performed on two samples to validate the nature of the smectite mineralogy of the soil samples. Results of such analyses suggested that the analysis of bulk fractions was sufficient. - 21 -
Surface data XRD analyses summary A total of 35 surface soil samples were analysed and found to contain quartz, kaolinite, albite, orthoclase, mica/illite and hematite. The samples did not contain the minerals jarosite, aragonite or gypsum, as found in the saprolite and transported materials of the costeans. Only seven samples were found to have calcite, and ten samples contained the mineral amphibolite: with both of these minerals occurring in small abundances (0–3%). On average, the samples were found to have quartz (44%), albite (25%), smectite (15%), orthoclase (6%), mica/illite (5%), kaolin (3.75%) and hematite (2%). Variation for orthoclase was minimal throughout the samples. Comparison of XRD analyses results for adjacent samples Results of the quantitative XRD were tabulated with their corresponding GPS coordinates and saved as a (.dbf) file and imported into ArcView™. Samples WD040 and WDTR06D0 were located within 5 m of each other, as shown in Figure A4.8. Other samples from the surficial layer of the costean samples (WDTR06B0 and WDTR06C0) (16;5, 13;14 mica/illite; smectite, respectively) contained similar mica/illite concentrations as WDTR06D0. Surface soil sample WD030 possessed similar values to WD040 (5;13 mica/illite; smectite), as shown in Table A4.1. Other samples in the region did not possess detectable amphibole concentrations.
Figure A4.8: Distribution of surface samples used in the quantitative XRD analysis. Circles represent surface-grid soil samples and boxes represent costean samples collected within 100 mm of the surface.
Samples WD039 and WDTR04BC0 were taken within 5 m of each other (Figure A4.8) and possessed similar concentrations of the minerals kaolinite (WD039 3%; WDTR04BC0 3%), albite (23;28), orthoclase (8;5), mica/illite (5;4), hematite (2;1) and amphibole (2;1). However, there were differences in the abundances of calcite (0;1), quartz (44;36) and smectite (13;21). The results for the XRD analysis around 460230 mE 6449100 mN are displayed in Table A4.1 for comparison.
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6 19 16 13 5
Calcite
6 5 10 4 7
Amphibole
23 27 25 21 25
Hematite
Orthoclase
4 1 3 6 4
Smectite
Albite
38 43 40 41 44
Mica/illite
Kaolin
WD040 WDTR06D0 WDTR06B0 WDTR06A0 WD030
Quartz
Sample name
20 3 5 14 13
1 1 1 1 2
1 0 0 0 0
0 0 0 0 0
Table A4.1: Quantitative XRD results for samples collected from the surface around the location of 460230 mE 6449100 mN.
4 4 5 2 3 4
Calcite
4 5 8 7 5 6
Amphibole
13 28 23 28 30 22
Hematite
Orthoclase
9 3 3 4 5 3
Smectite
Albite
24 36 44 32 29 53
Mica/illite
Kaolin
WDTR04A0 WDTR04BC0 WD039 WDTR04C0 WDTR04D0 WD014
Quartz
Sample name
45 21 13 24 23 10
1 1 2 1 2 2
0 1 2 2 2 0
0 1 0 0 1 0
Table A4.2: Quantitative XRD results for samples collected from the surface around the location of 460180 mE 6449080 mN. Spectral plots of some of the samples corresponding to the XRD samples of Table A4.1 and Table A4.2 are shown in Figure A4.9.
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Figure A4.9: Spectra corresponding to XRD samples in Tables A4.1 and A4.2. Spectra in plot (a) are surface samples collected from the 50 × 50 m grid. Plots (b) and (c) are from surface samples from costeans WDTR04 and WDTR06, respectively. The continuum removed plots all have very similar VNIR and SWIR features, with only small variations observable in the 1.9 μm spectral region because of differences in water abundances.
The similar abundance of amphiboles was encouraging, because amphibole was found in the spectra of samples at depth in the costean, and suggests that variations of smectite may be able to be seen in the ASD FieldSpec Pro™ spectra. Examination of the corresponding spectra demonstrated it was difficult to recognise the XRD abundances of the spectrally active minerals. This may be because of the difference in material that is being analysed by the two techniques. Samples WDTR04C0, WDTR04D0 had similar mineral abundances to WDTR04BC0. WDTR04A0 possessed different concentrations of the minerals (smectite:45%, quartz:24%, albite:13%) and the absence of amphibole. WD014 and WDTR04D0 had very similar values for all minerals: quartz (53;53), kaolin (3;4), albite (22;18), orthoclase (6;6), mica/illite (4;6), smectite (10;11) and hematite (2;1), except amphibole (0;1) and calcite (0;1). Surface distribution maps using ArcView Spatial AnalystTM Grids were plotted of the surficial XRD results using spline and tension type methods using ArcView Spatial AnalysisTM. Minerals that lacked detectable concentrations in all of the samples (for example, calcite and amphibolite) displayed distribution maps that strongly reflected the sample points where such minerals were present. Only one sample (WDTR1D0) in the northwestern portion of the sampling transects displayed calcite. This was seen in costean WDTR01, where calcite was found at greater depths throughout the profile than the southern costeans: reflecting the greater thickness of the transported cover sequences. Samples in the southeastern portion of the sampled area were located closer to bedrock exposures and proximal to numerous amphibolite dykes trending in a northeasterly direction. Field mapping of the WDTR06, WDTR04 and WDTR02 costeans demonstrated a large abundance of regolith carbonate at shallower depths in comparison with WDTR01 and WDTR05, where the transported cover sequences were much thicker.
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A large population of rabbit warrens were found in the southeastern portion of the sampled area. Excavation of the burrows by the rabbits had brought powdery carbonate material to the surface. The soil in this region was a pale-yellow-brown colour, which contrasted the surrounding RB soil in the region. The mica/illite results had a slightly higher abundance at the eastern edge of the sample grid, with a low directly to the north and west of WDTR06. Low abundances were recorded for the alluvial plain and the absence of mica/illite at the surface was seen in the northeastern corner of the grid (Figure A4.10i). The lack of mica/illite in the alluvial regions was attributed to the high attrition rate of muscovite by alluvial transport (Ollier 1984). The presence of mica/illite in the central regions may be related to transported material from the southwest. The analysis of more samples in this region is required to confirm if this distribution correlates with the ASD results, which indicated a higher abundance for illite in the southwestern region of the sampled area. Smectite had a poorly correlated, and almost inverse, relationship with mica/illite, as shown in Figure A4.10 iv and i. The high abundance of smectite-clays between the northern ends of WDTR06 and WDTR04, was associated with an area depleted in feldspars and mica/illite and enriched in kaolinite. This region coincided with the projected location of the amphibolite dyke. The distribution of albite displayed an almost perfect inverse relationship with kaolin, with high abundances recorded for the saprolite exposure and a low percentage around the northern end of WDTR04, as shown in Figure A4.10v. The albite low at WD001 correlates with the highest abundance of kaolinite in the channel, which was attributed to the weathering of the feldspar materials. An elevated abundance of albite was found in the soil sample WD002, which was collected close to the alluvial channel (ACa). This may be because of the abundance of lithic fragments in the channel and surrounding alluvial area. Orthoclase displayed a similar inverse relationship with kaolin (Figure A4.10ii) and a complementary relationship with albite. The distribution of orthoclase was marginally more abundant in the saprolite region, with a maximum of 8% at WD039 (Figure A4.10ii). The percentage of orthoclase was stable over the rest of the area, with an average abundance of 6%. The orthoclase high coincided with a maximum concentration of amphibole (2%).
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Figure A4.10: Surface distribution of minerals over the White Dam Prospect interpolated from quantitative XRD analysis for: (i) mica/illite (ii) orthoclase (iii) kaolinite (iv) smectite and (v) albite of soil samples. Abundances are in percent, with dark areas having a low abundance and light areas a high abundance. The blue dots indicate where samples were used in the gridding process.
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Figure A4.11: Surface distribution of minerals over the White Dam Prospect interpolated from quantitative XRD analysis for: (i) amphibole (ii) calcite (iii) quartz and (iv) hematite soil samples. Abundances are in percent, with dark areas having a low abundance and light areas a high abundance. The blue dots indicate where samples were used in the gridding process.
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Kaolin displayed a high concentration to the northeast of WDTR04 and to the west of WDTR06, with decreasing values in the areas of deeper transported cover (Figure A4.10 iii). An anomalous high abundance occurred in the northwestern corner of the sampling area, which may be related to the weathering of transported materials deposited by the northeast trending ephemeral creek. Quartz displayed a low concentration in the eastern portion of the sample area, increasing to the west and northwest. The region between WDTR04 and WDTR06 displayed a low abundance of quartz, as shown in Figure A4.11 iii, which was influenced by the small concentrations of quartz in samples collected from WDTR04. Quartz displayed lower abundances for the saprolite areas, contrasted by a gradational increase from the southeastern corner to similar moderate to high abundances for the rest of the area. This dispersion pattern closely matched the thickness of transported material seen in WDTR04 and WDTR06, which displayed an increase to the north—as shown in the section. The dispersion of the mineral hematite was uncorrelated with any other mineral, with the greatest hematite occurrence (4%) situated in the southeastern region of the sampled area, corresponding to the saprolite exposure (Figure A4.11 iv). The abundance decreased to the north to 1–2%, with an incidence of 3% in the alluvial RLUs WD002 (Aap) and WD044 (Aed). Significant low abundances occurred in the zone above the amphibolite (WDTR04D0 and WDTR06A), to the east of WD003 and in the central portion of the area. It was unclear if the XRD results were identifying hematite or goethite. The appearance of the XRD results display similarities to the Fe-oxide intensity map, generated by the ASD measurements. Considering that hematite was the only mineral identifiable in the soil samples that had a diagnostic signature in the VNIR, the presence of Fe-oxide spectral features was expected in the ASD FieldSpec measurements. Amphibole displayed a restricted distribution to near WDTR04 and WDTR06 and regions to the south of these costeans (Figure A4.11 i). The lack of significant numbers of samples containing amphibole, low abundance and the limited suite of samples make the results for this distribution map inconclusive. However, results show there was a slightly elevated abundance in the alluvial depression (Aed). Calcite was identified in two of the five soil samples. The samples with the above zero abundances of calcite occurred in the southeastern corner of the grid area (sample WD069 Figure A4.11 ii). Comparisons of the distributions of calcite, amphibolite and quartz show similar patterns, with quartz displaying an inverse relationship to the other two minerals. This distribution is related to the distance from the bedrock exposures, which also corresponds to increasing thicknesses of transported cover and depths to the saprolite. Calcite (Figure A4.11 ii) was found to coincide with amphibole (Figure A4.11 i) in four of the five occurrences of the carbonate mineral. The remaining four amphibole occurrences were not associated with calcite. Calcite and amphibolite distribution was highest in the southeastern region of the sample area and decreased to the north, away from the saprolite exposure. The lower abundance of calcite was attributed to the increasing thickness of the transported profile with greater distance from the basement outcrop. The increased profile thickness corresponded to a greater depth to the fragmented RCA hardpan, which predominantly occurred at the interface between the saprolite and transported material. In areas of thicker transported cover, the profile containing an abundance of powdery carbonate mottles at approximately 2 m depth, whereas in regions of shallow transported cover, the depth to the fragmented hardpan was within 1 m. The fragmented regolith - 28 -
carbonate hardpan consisted of a greater volume of material than the mottles or nodules and therefore could possibly have contributed more material to the upper soil horizons. The carbonate material may have been translocated to the surface by the processes of eluviation or bioturbation, where the RCAs occurred at shallow enough depths to be incorporated in these processes. The weight of influence that the distribution of minerals related to surface sheetflow process or subsurface pedogenic associated processes is unclear from the surface XRD results. An investigation into the abundances and distribution of minerals in the regolith profile was conducted using spectral and XRD analyses.
Subsurface mineralogy of the regolith White Dam Prospect Costeans Six costeans were excavated over the White Dam mineralisation in early July 2003 by EXCO Resources N.L. to evaluate cost-effective methods of extracting the mineralisation from the saprolite (Cooke 2003). The costeans were dug in a north–south orientation: traversing a series of different RLUs (Brown and Hill, 2003). Two costeans were located near the northeasterly trending creek and the other four were closer to the bedrock exposures, in the southeastern area of Figure A4.4. Figure A4.12 is an oblique air photograph taken from the northwest, showing the spatial locations of the costeans and surrounding regolith-landform features.
Figure A4.12: An oblique air photograph looking east-southeast over the White Dam Prospect area, taken in July 2003, after recent rainfall. The six north–south orientated costeans can be seen in the middle distance. The northeast-flowing creek occupies the foreground. Bright patches in the distance and left portion of the photograph are ponded water on the depositional landforms.
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Costean descriptions Detailed logs of the regolith stratigraphy and properties were recorded for five of the six costeans. Regolith carbonate morphology, ferruginisation and regolith materials were recorded for the whole section, as well as sample locations and sample colour. An example of the stratigraphy of the costeans is given by WDTR05 (Figure A4.4 and Figure A4.12) which was approximately 20 m long and was excavated to a depth of 6 m, as shown in Figure A4.13. The upper most layer consisted of PSA (0.2 m), which overlayed a red-brown (RB) pedal unit (0.5 m), a massive yellow-brown (YB) unit (0.5 m) and coarse gravel-lag layers within a YB matrix (1–1.5 m). Below this, a sequence approximately 0.5–1.0 m thick of small mottles of powdery RCAs overlayed moderately to heavily weathered bedrock. The regolith carbonate mottles overlying the basement occurred at various depths along the profile and ranged in size from 7 mm to 50 mm in diameter. Weathered basement materials consisted of a pallid zone, with ferruginous-rimmed mottles and pale-yellow-green cores. With increasing depth, the profile graded into a pale grey saprolite, which displayed an obvious fabric.
Figure A4.13: North-orientated view of costean WDTR05, excavated over the White Dam Prospect in June 2003. The east–northeast trending alluvial channel occurs in the background. The bench level is approximately 2.5 m and the base of the costean is 6 m from the surface.
Figure A4.14 shows the cross sections for each of the trenches. WDTR01 was approximately 30 m long and excavated to a depth of 6 m through PSA (0.2 m), RB pedal (0.5 m) and YB/lithic gravel deposits (1.5 m). The costean showed a more developed regolith carbonate horizon in the southern end, where the mottles were up to 100 mm in diameter. Gravel-lag layers occurred as slightly concave-upwards lens of sub-rounded, poorly sorted stained quartz, with clasts 5–15 mm in diameter. Bands of heavy mineral sands were found in the lower portion of the channel sequence. The saprolite was generally very friable and broke up - 30 -
into a sandy powder on impact with a hammer. Sub-vertically orientated carbonate veins were found in the saprolite where infilling of fractures or replacement of readily weathered materials had occurred. Weathering was more prominent around the margins of the veins. Some of the sub-vertical structures in the saprolite contained red-brown material, which appeared to be soil infilling fractures and joints after burial of the bedrock. Preferential weathering of Fe-rich minerals (such as biotite) in the saprolite has caused Fe-stained layers parallel to fabric. WDTR02 was excavated to a depth of 6 m through PSA (0.2 m), RB Pedal (0.5 m) and YB layers (2 m), with a well developed horizon of RCA mottles up to 150 mm in diameter. Minor gravel-lag horizons were observed in isolated locations throughout the section. WDTR03 was the deepest of the costeans, with a bench at 3 m below the surface. The powdery RCAs occurred as a discontinuous, undulating horizon with a fragmented hardpan of regolith carbonate directly overlying the saprolite. This costean was not sampled. WDTR04 was 30 m in length and excavated to a depth of 4 m. The powdery RCA mottles— seen in the costeans to the northwest—were not observed in this section. Small accumulations (~50 mm) of powdery regolith carbonate material occurred approximately 0.8 m below the RB pedal layer. The saprolite consisted of quartzo-feldspathic gneiss, pegmatite and an amphibolite dyke in the southern end of the costean. WDTR06 was the shortest and shallowest costean at 15 m in length and 3 m depth. It displayed a condensed regolith profile without a clearly definable layer of powdery RCAs. Nodular carbonate, approximately 10 mm in diameter, occurred between the RB pedal unit and the saprolite. The saprolite was capped with a 0.1–0.2 m thick hardpan of laminar regolith carbonate that had infiltrated the fractured and jointed saprolite to a depth of about 1 m below the hardpan–saprolite interface. A prominent amphibolite dyke—also found in the southern-most portion of WDTR04—occurred in the northerly end of this costean. As with WDTR04, there was an abundant amount of pegmatitic saprolite material throughout the section. Costean sample collection Samples were collected at 0.25 m intervals down the profile at spacings of 10 m (Figure A4.15). Where an obvious physical change in the materials of the costean had occurred within the 10 m lateral spacings, an extra profile was taken at a 5 m interval. This was performed to prevent omission of key features. In WDTR02, the sample spacing was 5 m apart, with additional profiles at 2.5 m where sudden changes in the regolith materials occurred. Approximately 550 samples were collected from five of the six costeans.
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Figure A4.14: Sections of the five analysed costeans from the White Dam Prospect showing the regolith stratigraphy, regolith carbonate morphology, extent and style of the ferruginisation and the colour of the materials sampled. Vertical: Horizontal=1.
205
Figure A4.15: Example of sample spacing from the costeans excavated over the White Dam Prospect. Samples were collected laterally every 10 m in profiles, with 0.25 m vertical spacing. Infill sampling was performed at a lateral distance of 5 m if a significant change in the overlying transported materials or saprolith occurred.
Results and Data Analysis of Subsurface Samples The de-stepped ASD FieldSpec data were checked for errors, which consisted of examining for spectra with low albedo or measurements that misrepresented the bulk sample. The corrected ENVI spectral library of the entire 3400 measurements were imported into TSG™ with the data logs and analysed for dominant mineralogy. Minerals used in the TSA™ algorithm for the VNIR were restricted to hematiteTSA, Fe2+ goethiteTSA, Fe3+ goethiteTSA and jarositeTSA. Sulphide minerals were omitted from the TSA™ algorithm because they were not visually observed during sample collection. A similar analytical technique to the one described above for the surface soil ASD measurements was performed on each of the sampled costeans’ spectral measurements. Mineralogical results from TSA™ were treated as abundance percentage and scaled between 0 and 100 %. TSG™’s calculations were scaled as high or low values, because they were dependent on the wavelength features that were under scrutiny. Spectral measurements were subdivided and gridded to represent the individual vertical faces of the northerly trending costeans. Conclusions of the ASD measurement of White Dam Prospect costeans Spectral analyses of regolith materials collected from the costeans at the White Dam Prospect were able to: • identify regolith materials and stratigraphy on their spectral properties and mineralogy • correlate the mapped regolith profiles with spectral abundance maps. • identify a PSA unit, which was characterised by low: montmorilloniteTSA, carbonates, kaoliniteTSA, Al-OH intensityTSG, kaolinite crystallinityTSG, Al-OH wavelengthTSG, goethiteTSA, low Mg- and Fe-OH absorptions features and crystal field absorption CFATSA, and medium to high hematiteTSA • identify an RB pedal unit—high: hematiteTSA and illiteTSA, low: kaoliniteTSA, low Mg- and Fe-OH absorptions features, carbonates, CFATSA • identify an YB unit— minor: opalTSA, high: smectiteTSG, hematiteTSA, low: talcTSG, goethiteTSA • identify gravel horizons, high: opalTSA, gypsumTSA, muscoviteTSA, montmorilloniteTSA • identify RCAs—high: dolomiteTSG, Mg- and Fe-(OH) absorptions features, palygorskiteTSA muscoviteTSA and illiteTSA, SWIR/VNIR reflectanceTSA
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•
•
• •
identify grey and pallid saprolite—high: kaoliniteTSA, Al-OH intensityTSG, CFATSG, kaolinite crystallinityTSG, Al-OH wavelengthTSG and goethiteTSA, low: hematiteTSA and low Mg- and Fe-OH absorptions features. amphibolite-derived saprolite—high: nontroniteTSA, jarositeTSA, magnesium claysTSA, hornblendeTSA, montmorilloniteTSA and can have high Al-OH wavelengthTSG and goethiteTSA; low: hematiteTSA, goethiteTSA, kaolinite, Al-OH intensity, CFA, kaolinite crystallinity, Al-OH wavelengthTSA pegmatitic saprolite—gypsumTSA and sideriteTSA albitic saprolite—Fe-tourmalineTSA.
XRD analyses of the costean profiles Samples from selected horizons of costean profiles were chosen for quantitative XRD analysis to interpret the variation in mineralogy of the different regolith sequences with depth. A total of eleven profiles were analysed, with between five and eight samples for each profile. The profile of WDTR02K was selected as an example of the average mineralogical distribution of the typical materials of the regolith over the White Dam Prospect. This section of the costean was also sampled by Brown A.D. (2003 pers. comm.) for geochemical analysis. The profiles from the costeans WDTR01 and WDTR05 represent the regolith profiles with a greater thickness of transported material and were characterised by the presence of regolith carbonate mottles. Samples from the profile of WDTR05C contained regolith carbonate, and were analysed to determine the amount of variation in mineralogy in these materials. A homogenised sample was collected from the wall of the costean, rather than the individual accumulations of regolith carbonate, in an attempt to get an average abundance for the horizon. Another prominent feature of WDTR05C was the presence of lithic gravels between the intervals 0.75 and 2.75 m below the surface. Individual XRD profiles The surface sample (PSA Unit) of WDTR05C (C1) (Figure A4 16) displayed a similar mineralogy to the average of the samples of the surface grid, with a slightly higher proportion of mica/illite. Mica/illite was absent in the RB unit, which contained less quartz and an increase in smectite. Unlike most other RB horizons sampled, there was an abundance of calcite. The reason for the high percentage of calcite at shallow depths in this profile was unknown. The layer below the RB unit contained an abundance of gravel clasts and displayed a higher percentage of mica/illite, kaolinite, smectite and quartz, with less albite than the RB unit and no calcite. At 2 m below the surface (C8), the profile contained a low abundance of carbonate material and lithic gravels. The mineralogy was similar to the surface material, with a higher abundance of smectite and hematite. The horizon directly below (C9) contained small carbonate mottles and had a low abundance of calcite, with less smectite than C8. The abundance of calcite increased down the profile for the next two samples (0.5 m), which occurred within the mottled RCA zone. The RCAs contained an increase in the abundance of kaolinite and mica/illite, with less albite, orthoclase and quartz.
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Figure A4.16 Profiles of the quantitative XRD results from the costeans WDTR01, WDTR05 and WDTR02. The intervals of depth are not to scale.
The sample C13 was collected 3 m below the surface in the pedolith/saprolite material that contained carbonate veins penetrating the fractures of the in situ materials. The mineralogy was similar to the overlying horizons. The materials in this portion of the profile represent pedolith derived from in situ materials that weathered to clays (smectite and kaolinite/halloysite). The calcite abundance reflects the regolith carbonate veins throughout the material. The abundance of lithic gravels in the middle section of the overlying transported sediments was shown by the consistent abundances of albite, orthoclase and quartz, as well as the lack of calcite. The materials in the upper section of the sequence between the carbonate mottles and the RB unit were different from the materials of the YB unit found in the costeans to the southeast. For the profile WDTR02K, the PSA unit displayed high amounts of quartz and albite with low abundances of smectite, kaolin and mica. No calcite was found in this unit. In the RB unit, the proportion of kaolin, mica and smectite increased, whereas albite, orthoclase and quartz decreased. The upper layers of the YB unit displayed similar proportions to the RB Unit, with a slightly lower abundance of kaolin and higher hematite and calcite. At 1.5 m below the surface (K6), a large abundance of gypsum was found: relating to a region of large carbonate mottles and highly weathered pedolith. The gypsum-rich sample also contained calcite with a lower abundance of quartz, smectite and albite than the samples closer to the surface. The in situ materials displayed a much higher abundance of kaolin and smectite in the upper regions (sample K10). This was accompanied by a percentage of albite. The saprolite in this profile displayed an abundance of carbonate veins infilling fractures. The sample at K14 displayed a high abundance of calcite, with a lower abundance of quartz, orthoclase and smectite than the samples above. Discussion of hematite XRD results Throughout the XRD samples, there was very little variation in the abundance of hematite (1– 3%), with only one location found to have a high abundance (9%). The lack of variation in the abundance of Fe-oxides in the profiles contrasted with the results obtained from spectral methods, which showed a large variation in the absorption features related hematite and goethite. The cause for the variation is attributed to the selection of material analysed by each technique. Spectral measurements interact with the surficial materials in a sample, whereas XRD analysis examines the crystallographic structure of a bulk sample. Surface rinds may exist on grains, which may be opaque or transparent. Fe-oxides often occur as thin surface coatings on regolith materials: causing a red-brown or yellow-orange-brown colouration where ferruginisation has occurred. The ferruginous coatings may constitute only a small abundance of bulk mineralogy and therefore have only small percentages in the quantitative XRD analysis.
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Analysis of the drill core with the HyLogger-1™ Introduction Sixteen diamond drill core holes drilled over the White Dam mineralisation by MIM Exploration before 1998 were selected for spectral analysis using the CSIRO-developed Automated Core Logger instrument, subsequently named the HyLogger-1™ (Huntington et al. 2004, 2006; Keeling et al. 2004). The location of the drillholes is shown in Figure A4.17. The HyLogger-1™ is a spectral measuring instrument adapted from the airborne OARS line scanner that has been mounted above a motorised moving table (Figure A4.6). A linescanning charge couple device (CCD) camera has been mounted on the frame to simultaneously record a colour image of the core. Before carrying out measurements, the core was cleaned and the depths of each segment were logged. The depth information was entered into a database that allowed each spectral measurement to be recorded with a reference of its spatial position, along with the position of the core in the tray and the tray number. Measurements were taken every 10 mm along the core over the wavelengths 0.416– 2.5 μm, with a total number of 189 bands. The spectral measurements and the corresponding depth information were imported from ENVI into TSG™ version 4b and analysed with TSA™ algorithms (Berman et al. 1999), which estimated the mineral composition. The VNIR and SWIR were analysed independently, with the output consisting of an estimate of the dominant mineral and secondary mineral for the two regions. Presentation of the line-scanned image with accompanying spectral information allowed the rapid visual evaluation of the entire length of the drillhole. The summary screen and scatterplots were used for the on-screen display and analysis of the results of the whole drillhole. The software facilitates the importation of accompanying geochemical and geophysical information, which allowed the rapid assessment of similar mineralogical zones, which may host mineralisation. The digital format also allows the calculation of user specified information extraction formulas (scalars). The diamond drill cores were selected to gain a perspective of spectral properties and mineralogy of the regolith, and fresh materials of the White Dam Prospect. A number of holes had previously been analysed extensively geochemically and geophysically (Busutill and Bargmann 2003), although only a limited dataset was available for comparison with the spectral interpretations. The core was transported from Challenger Geological Services in Edwardstown, South Australia to the PIRSA Core Library at Glenside, South Australia in September 2003, where it was cleaned ready for depth logging. The depth measurements were entered into the database and each core tray scanned. The raw data were processed by Mauger, A.J. 2004 (pers. comm. 2003) and the line scanner images colour balanced. The final exported TSG™ datasets, tray photos and ENVI spectral libraries were supplied for analysis and interpretation. Interpretation Of the sixteen drill cores that were analysed, only five consisted of material extending from shallow depths (Table A4.3). The remaining eleven cores consisted of previously drilled RC holes, which were collared at depth and diamond drilled. Shallow holes
Hole name WD15 WD16 WD17 WD18 WD19
Start depth 2.8 2.7 2.8 2.3 2.0
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Finish depth 96.0 87.0 94.0 71.0 137.0
Deep holes
WD29 WD31 WD61 WD69 WD71 WD111 WD176 WD191 WD193 WD194 WD195
60.0 61.0 104.0 73.0 85.0 150.0 115.0 144.0 136.0 149.0 137.0
201.0 185.0 177.0 146.0 157.0 465.0 248.0 198.0 146.5 198.0 196.0
Table A4.3: Diamond drillholes over the White Dam Prospect selected for HyLogger-1™ analysis. Location of the drillholes is shown in Figure A4.17.
Mineralogical analyses of the near-surface regolith and the fresh basement using the HyLogger-1™ and ASD instruments A comparison of the mineralogy of the fresh and weathered core was performed to determine the relationship between the deep and subsurface minerals. A comparison was also made between the material in the costeans, which was analysed with the ASD, and the corresponding material from the top of hole material from the diamond drill cores, which was measured with the HyLogger-1™ instrument. At depths greater than 250 m, the mineralogy of the White Dam drill core consisted of muscovite and illite, as well as minor phengite, magnesium chlorite, intermediate chlorite and epidote. The mineralogy of the intervals above 250 m consisted of a greater abundances of kaolinite/halloysite, with minor intervals of montmorillonite. The mineralogy from the surface to approximately 90 m depth predominately consisted of kaolinite/halloysite, with lower abundances of illite, montmorillonite and muscovite. Above 90 m, there was almost no chlorite, epidote, biotite, white micas or hornblende—marking the extent of the oxidised zone. The mineralogy of the material at the start of drillhole WD19 (between 2 and 5 m depth) consisted of kaolinite and Fe2+ goethite, with minor illite and montmorillonite (Table A4.4). The surface regolith materials have a red-brown (2 m) colour, which grades into a yellowbrown-green (2.5 m), then into a yellow-grey (3 m) and finally into a grey (6 m) colour: representing the gradation from the transition zone of transported and in situ clayey pedolith, mottled pedolith, goethitic pedolith and into highly weathered pallid saprolite. The saprolite becomes progressively less weathered with depth, although intervals of highly weathered material exist throughout the hole until approximately 90 m. The mineralogy of WD19 was representative of a majority of the diamond drillholes from the White Dam Prospect. Where the lithologies were mafic, a different profile was observed. Drillhole WD15 (Table A4.5) represents the mineralogy of a profile with an amphibolite near-surface lithology.
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Figure A4.17: (a) Location of the diamond drillholes analysed by the HyLogger-1™ core scanner (a). (b) Perspective view and (c) 3D drillhole projections of the White Dam Prospect.
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Table A4.4: HyLogger-1™ interpretation of kaolinitic saprolite profile from WD19. Depth TSA_A_Min.1 TSA_A_Min.2 TSA_B_Min.1 TSA_B_Min.2 D=2.06058 D=2.28184 D=2.52418 D=2.74544 D=3.00127 D=3.22780 D=3.24887 D=3.51754 D=3.74704 D=4.20536 D=4.75094 D=5.00381 D=5.24911 D=5.50192 D=5.50704 D=5.75280 D=6.01291
Gypsum Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite
Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite
Illite
Hematite
Hematite
Hematite Hematite Hematite
Hematite
Table A4.5: HyLogger-1™ interpretation of mafic saprolite profile from WD15. Depth
TSA_A_Min.1
TSA_A_Min.2
TSA_B_Min.1
TSA_B_Min.2
D=2.80358 D=2.93970 D=3.00418 D=3.25493 D=3.45552 D=3.75642 D=4.00761 D=4.25119 D=4.50910 D=4.75448 D=5.00522 D=5.25597 D=5.50672
Illite Illite Hornblende Hornblende Hornblende Hornblende Hornblende No TSA result Montmorillonite No TSA result Hornblende Dolomite Kaolinite
Gypsum Gypsum Dolomite Gypsum e
Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite No TSA result Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite
Hematite
Gypsum Gypsum Dolomite Gypsum Gypsum Dolomite
Table A4.6: ASD interpretation of a kaolinitic saprolite profile. Depth TSA_A_Min.1 TSA_A_Min.2 TSA_B_Min.1 D=0.00 D=0.25 D=0.50 D=0.75 D=1.00 D=1.25 D=1.50 D=1.75 D=2.00 D=2.25 D=2.50 D=2.75 D=3.00 D=3.25 D=3.50 D=3.75 D=4.00 D=4.25
Illite Illite Montmorillonite Muscovite Muscovite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite
Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite No TSA result Fe2+Goethite
Kaolinite Kaolinite Kaolinite Kaolinite Kaolinite Palygorskite Muscovite
Montmorillonite
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Hematite
Hematite Hematite Hematite
Copper Clay
TSA_B_Min.2 Hematite Hematite Hematite Hematite Hematite Hematite Hematite
Jarosite
Table A4.7: ASD Interpretation of a Mafic Saprolite Profile. Depth
TSA_A_Min.1
TSA_A_Min.2
TSA_B_Min.1
TSA_B_Min.2
D=0.00 D=0.25 D=0.50 D=0.75 D=1.00 D=1.25 D=1.50 D=1.75 D=2.00 D=2.25 D=2.50 D=2.75 D=3.00
Montmorillonite Illite Muscovite Montmorillonite Nontronite Hornblende Hornblende Kaolinite Kaolinite Hornblende Hornblende Nontronite Nontronite
Kaolinite Kaolinite Kaolinite Siderite Kaolinite Kaolinite
Fe2+Goethite Hematite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite Fe2+Goethite No TSA result Jarosite Jarosite
Hematite Fe2+Goethite Hematite
Hornblende
Nontronite Palygorskite
Galvanised Fe
Fe2+Goethite
The mineralogy of the material in the near-surface zone measured with the HyLogger-1™ was consistent with the mineralogical interpretations from the regolith materials in the costeans that were analysed with the ASD (Tables A4.6 and A4.7). Although the spectral resolution of the HyLogger-1 was considerably lower than the ASD and the profiles are not from the exact same locations, there is a good resemblance of absorption features for similar regolith materials. The kaolinite-dominated saprolite profiles of the HyLogger-1 and ASD were dominated by the minerals kaolinite and Fe2+ goethite, with minor abundances of clays and in the upper region. These clays represent the pedolith and transported regolith materials. The spectral plots corresponding to the mineralogical interpretations in Tables A4.4–A4.7 clearly show a change in the spectral characteristics of different regolith materials— representing variation in the regolith morphology (Figure A4.18). Figure A4.18 compares near-surface profiles of regolith materials measured with the ASD and HyLogger-1™ instruments. The upper of the two plots is of a typical profile consisting of surface soil underlain by transported materials, which has RCA materials in the lower portion of the transported layer. This grades into the highly weathered in situ material of clay-rich pedolith, which overlies kaolinite-rich pallid saprolite. The lower plot is from a profile through mafic saprolite. The mafic materials consist of hornblende, nontronite and kaolinite. Although TSA™ interpretations of the mineralogy are slightly different (especially for the mafic minerals) the overall mineral groups and spectral features are similar for the HyLogger-1™ and ASD near-surface profiles. The cores analysed by the HyLogger-1 do not contain material from the upper 2 m of the profile and therefore have less information on transported and pedolith materials (Figure A4.18). The spectra of the first two samples of the kaolinitic and mafic derived cores is most probably back-fill or RAB material that has fallen down the hole during the preparation for diamond drilling. This material coincides with transported regolith and soil and corresponds to the surface material analysed with the ASD (red spectra in Figure A4.18). The brown spectra represent transported materials that grades into the pedolith, shown by the pink spectra. The pedolith from the kaolinite saprolite profile contains a higher abundance of kaolinite and a lesser amount of montmorillonite. The pedolith from the mafic profile contains both kaolinite and mafic minerals, shown by the presence of 2.3 μm absorption features. The mafic profiles display a decreasing abundance of kaolinite with depth—representing a decreasing level of weathering. This is shown more so in the ASD profile than the HyLogger-1™ spectra.
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Regolith carbonate occurred in the kaolinite derived saprolite profile at a depth of 1.25 m corresponding to a broad, shallow absorption feature in the 2.3 μm region (Figure A4.18). This feature was identified as palygorskite by TSA™ (Table A4.6). The results of the comparison of the near-surface profiles validate the spectral characteristics of the HyLogger-1™ instrument for identifying common regolith materials and minerals. The mineralogy is well interpreted by TSG™ in kaolinised-pallid saprolite areas, but TSG is less able to differentiate mafic materials. This may be due to the darkness of the mafic minerals or the lack spectral resolution required to identify the characteristic absorptions of different mafic minerals.
Figure A4.18: HyLogger-1™ (left) and ASD (right) profiles from (top) quartzo-felspathic-derived saprolite and (bottom) mafic-derived saprolite. Samples were collected in 0.25 m intervals down the profile, through the topsoil, transported material and pedolith, into the saprolite. The thickness of the transported cover over the quartzo-felspathic derived saprolite is thicker than the mafic saprolite. An increase in kaolinite crystallinity can be seen down the felsic profile (top) as the samples become less weathered. In the mafic profile (bottom) the abundance of kaolinite decreases with depth in the saprolite (green spectra)—corresponding to the top of the saprolite/pedolith material being more weathered than the saprock.
Conclusions of the HyLogger-1 spectral interpretations The HyLogger-1 demonstrated the mineralogical variability of the materials in the White Dam Prospect regions. The deep core samples showed that the fresh bedrock materials predominantly contained ferromagnesian minerals, such as biotiteTSA, phlogopiteTSA, hornblendeTSA and epidoteTSA, as well as muscoviteTSA. Weathered minerals were also found at depth including kaoliniteTSA, illiteTSA, montmorilloniteTSA, Fe2+ goethiteTSA, jarositeTSA and magnesium claysTSA. The weathering of materials at depths greater than 100 m was related to shearing and faults, where fractured rock had been weathered by penetrating water. Mineralisation and sulphides promoted higher rates of weathering. Ferruginous and sodic alteration was also seen at isolated intervals throughout the drill core. In the shallow intervals, the spectra of materials—and subsequent mineralogy—closely resembled those of the costean samples, with dominant mineralogy consisting of weathered assemblages. Where amphibolite was present, the mineralogy consisted of hornblendeTSA, nontroniteTSA, montmorilloniteTSA, magnesium claysTSA, palygorskiteTSA and opalTSA. Grey
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saprolith and pedolith materials were dominated by kaoliniteTSA, Fe2+ goethiteTSA, illiteTSA and gypsumTSA. In the upper portion of the core (>3.5 m), the presence of regolith carbonate material was noted, with a relative increase in the abundance of carbonate minerals. Carbonates were also seen in the deeper intervals (>20 m). However, these minerals are more likely to be related to the bedrock processes and alteration than the formation of the regolith. The HyLogger-1 results were found to correlate with the costean samples.
Airborne mapping of the regolith minerals Before meaningful results could be obtained from the HyMap data, pre-processing was required. Atmospheric correction experiments are discussed in detail, followed by the process of information extraction, which involved the use of indices and un-mixing techniques. This was performed with the intention of producing seamless distribution maps of surface materials and to aid regolith interpretations. Atmospheric correction conclusions Analysis of hyperspectral imagery for phyllosilicate minerals and alteration products requires a technique with the greatest accuracy in the greater than 2.0 μm region; therefore an empirical line fitting (EL) correction was considered the most ideal calibration technique. If the location of the imagery was in an inaccessible area, the model-based technique should be able to produce sufficient results to generate identifiable spectra. However, the calculation of offsets to correct for the inefficiencies of the radiative transfer model (RT) would be required to visually improve the spectra. The effects that the input parameters will have on the outcome should be considered, and care should be taken, before applying an atmospheric correction program. A modified radiative transfer-empirical line fitting (RT-EL) technique was found to produce the best results for the White Dam HyMap data, using a combination of field-derived and within-scene data to correct the HyCorr processing. The results contained the smoothest spectra with the least amount of noise and the most identifiable absorption features— especially in the VNIR region. Information extraction and spectral un-mixing Two types of processing were performed on the HyMap data. The first involved the processing steps of noise removal, data reduction and feature extraction—often referred to as ‘hourglass’ processing—due to the removal of redundant data Figure A4.19. The second method involved the ratioing of bands and masking of features using spectral indices. A flowchart of the processing steps performed in the extraction and presentation of information from the HyMap imagery is shown in Figure A4.20. Figure A4.19: Diagrammatic representation of the ‘hourglass’ processing technique of mineral map production, using un-mixing techniques. The term comes from the decrease in data, and corresponding file size, through the processing steps.
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Figure A4.20: Information extraction flow chart, detailing the un-mixing techniques used on the HyMap imagery.
End member extraction from the HyMap imagery The HyMap datasets were individually examined and processed to determine the end members for each swath. The combined HyMap dataset was found to have corresponding end members that could be identified in the other swaths. There were also a small number of unique individual end members that were not found to be prevalent within the whole dataset. The end members were split in the VNIR and SWIR subsets for the process of performing unmixing on selected ranges of wavelengths. The end members of the five swaths were examined for unique and representative spectra, which were used to build the combined SWIR and VNIR libraries. The spectra of the combined end members are shown in spectral plots in Figure A4.21 and Figure A4.22.
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Figure A4.21: Combined SWIR end-member spectra, extracted from the HyMap imagery.
Figure A4.22: Combined SWIR and VNIR end-member spectra, extracted from the HyMap imagery.
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A total of 85 end members were extracted from the five swaths during the individual processing. Many of the end members were replicated in the other runs, showing a homogeneity of materials across the runs and indicating that the pre-processing had been successful. Duplicates and spectra with similar shapes but lower albedo were removed from the subsequent processing steps after re-evaluation of the extracted end members: reducing the number to sixteen (Table A4.8). Some of the spectra displayed goethite mineralogy in the SWIR, with deep crystal field absorption (CFA) and charge transfer state CTS features and Al-OH features in the SWIR. Others displayed the same SWIR features, but different VNIR features. There was a group of spectra that displayed vegetation influences in the VNIR and some in the 1.7μm region of the SWIR. Nine spectra were selected to represent the VNIR region (Table A4.9). Absorption features at 2.35μm, which may have been related to RCA or dry vegetation, were identified in a number of spectra. A few of the end members displayed mixed or un-identifiable features and were not named. Table A4.8: Combined SWIR end members from the White Dam HyMap dataset.
Swath
End member
Description
1
1
Highly crystalline kaolinite
1
6
Dry-vegetation
1
11
RCA-goethite. Quarry material
1
17
Fe-/Al-OH Goethite/hematite
1
21
?Chlorite/calcite- Al-OH hematite
2
2
RCA
2
8
Shadow/road/water/cloud (dark materials)
2
9
Green vegetation
3
21
Muscovite-phengite
3
23
Very low crystalline kaolinite (regolith materials)
3
26
Muscovite (Fe-rich)
4
1
Muscovite-rich soil
4
5
Kaolins
5
1
Low crystalline kaolinite
5
5
Soil
5
6
RCA, ?Mafics/amphibolite
One spectrum displayed an almost exact match to illite (Clark 1993) as well as a good match for muscovite (Clark 1993). The identified spectra consisted of: kaolinite, poorly ordered kaolinite, kaolinite smectite, muscovite, illite, goethite, hematite, dry vegetation, green vegetation, road tar (asphalt), regolith carbonate and soil spectra resembling ASD measurements. The combined end members were selected to represent the main components of the five swaths, with the intention of classifying the most prominent materials within the area. The end members were identified as consisting of different forms of kaolinite, muscovite and regolith carbonate materials, as well as soils, Fe-oxides, vegetation and human-made materials. Table A4.8 and Table A4.9 document the swath from which the end members were extracted and briefly describe the constituents. The two sets of combined end members were used to perform un-mixing on the individual swaths. The generated end members were
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used in their origin swath for Spectral Angle Mapper (SAM) and mixture tuned match filtering (MTMF) un-mixing processes to determine their distribution. The SAM processing was performed using a 0.1 radian classification cut-off. The SAM classification was found to be dominated by the WD003#20 (Hematite CFA) and WD004#1 (muscovite-rich soil) end members, displaying little variation in the VNIR and SWIR classifications. The results of SAM—along with the other types of un-mixing techniques that were less successful than MTMF—are not presented. Table A4.9: Combined VNIR end members from the White Dam HyMap dataset Swath
End member
Description
1
6
Dry vegetation and soil
1
12
RCA—muscovite and goethite
1
16
Tin roof
2
8
Road/shadow/water (dark materials)
2
9
Green vegetation
3
20
Hematite CFA
4
8
Average soil
4
16
Soil and vegetation
4
19
Road
Information extraction of the HyMap imagery using spectral indices Spectral indices involve the use of ratios of selective wavelength features on high-resolution spectra. An example of an index is the NDVI (normalised difference vegetation index), which uses information in the VNIR region to interpret green vegetation. Similar indices exist for Fe-oxides, clay minerals and other combinations. The use of spectral indices should be employed with masks that remove the effects of unwanted components, such as shadow. A NDVI can be constructed to determine pixels that contain vegetation, which can be used to create a vegetation mask. The mask can be inverted to display the pixels that were not heavily influenced by vegetation, which may be of geological interest. Similar masks can be generated to aid the discrimination of white mica minerals from kaolin minerals. A flowchart of the spectral indices performed on the HyMap imagery is shown in Figure A4.23, with a detailed description of the parameters of the indices found in Table A4.10. Before the use of spectral indices, masks were generated for each swath of pixels for each swath with very high and low albedo, which corresponded to cloud, vegetation, shadow and cloud-shadow. Cloud-shadow was easily identified within the imagery by visual inspection, with the affected pixels displaying a reduced albedo, while retaining the overall spectral shape of the materials. It was this characteristic that made it critical that these pixels were masked out, because these spectra would have had adverse effects on the results of MTMF and the index processes if left in the processing.
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Figure A4.23: Spectral indices flow chart showing the sequence of processing required to extract mineral information from the HyMap data.
Table A4.10: Description of the spectral indices used in the extraction of information from the HyMap imagery. Name
Ratio
Masks
Threshold
Leaf/surface index
Bands 48+59/51+53= (1.119 μm+1.279 μm)/ (1.164 μm+1.193 μm)
1.03 or 1.05
Cellulose index
Bands 100+110/104+105 =(2.205 μm+2.184 μm)/ (2.078 μm+2.096 μm)
0.95 or 0.97
Fe-oxide abundance
Bands 23+46/31+35 = (0.761 μm+1.190 μm)/ (0.886 μm+0.919 μm)
A1 and A2
1.05 and 1.055
Fe-oxide abundance
Bands 23/31
A1 and A2
0.98
Hematite:goethite
Bands 33/35 = 0.886 μm/0.919μm
B1 orB2
Goethite:hematite
Bands 35/29 = 0.919 μm/0.868 μm
B1 or B2
Hydrated Fe-oxide
1.265 μm*1.289 μm/1.334 μm*1.347 μm Bands 58+59/63+64 = 1.265 μm +1.279 μm /1.334 μm +1.347 μm
C1 or C2
Al-OH abundance
Bands 106+113/110+111
A1 and A2
Kaolinite abundance
Bands 109/108 = 2.167 μm /2.150 μm
A1 and A2
0.952
E1 White mica Al-rich
Band 110/111 = 2.184 μm /2.201 μm
White mica Al-poor
Band 113/113 = 2.219 μm /2.236 μm
A1 and A2 F1 A1 and A2 F1
MgOH/CO3 index
Band 115+122/118+119
A1 and A2 E1
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1.015+ or 1.02+ (95%)
Table A4.11: Vegetation indices trialled on the HyMap imagery for the creation of vegetation masks Name
Bands
Wavelengths
Leaf/surface water features
Bands 48+59/51+53
(1.119 μm+1.279 μm)/(1.164 μm+1.193 μm)
NDVI
Bands 13 and 24
0.685μm and 0.854μm
Photochemical reflectance index
Bands 3 and 5
0.533 μm and 0.563 μm
Red edge inflection
Variable
0.7 μm and 0.74 μm
Cellulose mask
Bands 100+110/104+105
(2.205 μm+2.184 μm)/(2.078 μm+2.096 μm)
NDVI traditional Rouse
Bands 12 and 23
0.671μm and 0.837 μm
.
Summary of indices performed on the HyMap imagery The pre-processed data were evaluated for areas of green and woody vegetation using (1) and (2) indices. These regions were used to mask the dataset for subsequent processing. The vegetation masks were retained as a classification product. Areas of minimal vegetation— identified from the use of vegetation masks—were examined for Fe-oxide, Mg-OH/CO3 and Al-OH abundances. The Fe-oxide abundance was used to highlight the areas for further processing. A mask was generated of the higher abundance areas and the hematite:goethite ratio was applied. The areas representing goethite were used in a mask to determine areas of hydrated Fe-oxides. The Al-OH abundance index was used on vegetation-masked data, which in turn is used to determine areas of kaolinite and white mica. The Mg-OH/RCA index was designed to determine the areas where RCA occurred. Examining the HyMap spectral response of regions where RCA were observed in the field, showed a strong Al-OH absorption in the 2.2 μm region that was accompanied by a broad, asymmetric 2.3μm absorption. The use of indices appeared to be a better option for mapping Fe-oxides than the use of unmixing techniques. Indices for other materials, such as regolith carbonate, could be attempted using CO3 feature at 2.3μm. The use of indices for separating regolith clays and smectite from outcrop white micas (such as muscovite) has been shown to be successful with this dataset and could be used to map outcrop. Vegetation indices proved to work extremely well, with different types of vegetation and vegetation communities being able to be discriminated. However, the masking of vegetation can prove to be a problem because vegetation occurs on outcrop in many of the saprolite exposures in the region. This was due to colonisation of fractures in silicified/ Na–Ca-altered rocks and the growth of vegetation on well-drained soils overlying intensely weathered saprolite.
Results and data analysis of HyMap imagery HyMap mineralogical maps Surface mineralogical maps were constructed from the reflectance-corrected airborne hyperspectral imagery for a region of similar extent to the soil sampling grid. The spectral response for each pixel on a 10 m spaced grid was extracted as an ENVI spectral library and recalculated to reflectance in nanometres. An accompanying spreadsheet was generated with the pixel geo-coordinates and the files imported into TSG™ version 4b. The same spectral algorithms as those used on the surface and costean ASD Field Spec Pro™ measurements - 49 -
were applied to the HyMap data and exported for use in ArcView™. The files were gridded using the method described earlier in this document for the surface ASD Field Spec Pro™ data and a standard violet to red (low to high) colour table was applied. Visual interpretation of the HyMap imagery The 5 m resolution of the HyMap imagery (Figure A4.24 i) produced a highly comparable image to the 1.25 m ortho-image (Figure A4.24 v). However, the HyMap imagery showed a larger contrast between features, which allowed greater differentiation between the RLUs. Although the three bands displayed similar wavelength ranges in the RGB images, the HyMap data possessed many more bands—with smaller bandwidths—which allowed better discrimination of spectral features in the visible region. The HyMap data were georectified to the orthoimagery using a triangulation method, which displayed only minor distortions of approximately 1–2 pixels (5–10 m). The drill spoils on the surface appeared prominently in both images. In the HyMap image, the alluvial plain and saprolite regions had a high contrast in comparison to the colluvial-dominated regolith units. Gridded HyMap mineral distribution maps The gridded results, displayed in Figure A4.24 vi and Figure A4.25, were found to primarily identify ferruginous and Al-OH minerals. A very limited distribution of green vegetation and carbonate minerals was also identified. The SWIR/VNIR reflectance calculationTSG (Figure A4.24 ii) was found to highlight pixels that contained drill-spoil materials. The saprolite in the southeastern corner and the alluvial units in the northwestern area were found to have low values for SWIR/VNIR reflectanceTSG, whereas the colluvial units displayed moderate values. This pattern may be due to the greater abundance of quartz within these units and the deeper absorption features for the kaolinite-rich saprolite and alluvial channel. Drill spoils occurred as bullseye highs throughout the region for different minerals and indices. Vegetation and carbonates The identification of carbonate at 640150 mE 6449050 mN was a significant result, because a thick hardpan of regolith carbonate occurred at shallow depths in this region. The carbonate was associated with a northeast-trending amphibolite dyke, which was found in the costean WDTR04 and WDTR06. The region was covered by less than 1 m of transported material, which may have allowed some carbonate to be present at the surface. This region coincided with rabbit warrens discovered during ground truthing. The warrens were constructed in the soft, friable substrate and overlying RCA hardpan. The indurated material had been brought to the surface as a result of the excavation. Figure A4.24 viii shows the areas that were found to have green vegetation and carbonate spectral features. The presence of vegetation was shown by the green circles whereas the predicted carbonate occurrences had a purple halo. The pixel that produced the green vegetation anomaly occurred at the fringe of the depositional and erosional plains on the western margin of the area. The pixel spectrum possessed a moderately weak green vegetation feature in the VNIR region. A weaker feature of similar appearance could be seen for many of the other pixels located in the vicinity of dark pixels, but were not classed as vegetation by TSA™. The dark pixels that occurred in the alluvial channel at 459800 mE 64492025 mN, were unexpectedly not identified as vegetation by TSA™. Re-examination of the pixels used in the gridding for the process found the spectra relating to the northwestern corner to contain abundant noise: probably representing the shadow component of trees, rather than the green foliage of the top of the canopy.
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Figure A4.24: HyMap surface mineralogical maps of: (iii, iv, vii) Fe-minerals and (viii) carbonate and green vegetation. (i) Shows the distribution of pixels used in the analysis and gridding process over a HyMap TCC and (v) displays the ortho-imagery with the surface sample collection points that were measured with the ASD FieldSpec, and the 1:2000 regolith-landform boundaries (courtesy of Brown and Hill 2003). The locations of the north–south orientated costeans are shown in red. (ii and vi) Ratios of spectral parameters display a slight correlation to the colour of the surficial materials. ‘HyMap’ refers to the abundance of the mineral, whereas ‘TSG’ are calculated from TSA™ algorithms.
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Ferruginous materials Distribution of the Fe-oxide abundance indexTS (Figure A4.24 iii) showed a close correlation between the bare soil areas (orange-red and yellow regions of (Figure A4.24 i), displaying high values in these regions. The drill spoils demonstrated a low value: reflecting their greyblue appearance on the RGB TCC image and lack of ferruginisation. The bare soil areas in the colluvial units were related to regions where surface erosion had occurred and where the vegetation density was low. A clearing in the central portion of the image at 460000 mE 6449050 mN, is thought to be where a drill pad and temporary infrastructure was located during drilling. This area has significantly less vegetation cover than the surrounding regions and therefore a higher Fe response. Although the alluvial channel has a greater abundance of larger trees, it generally has less chenopods (Atriplex sp. and Maireana sp.) and cryptogram cover (lichen, moss, copper burrs and other low vegetation) because of the instability of the substrate. This results in more areas of bare soil, and therefore, a higher abundance of Feoxide-related responses. The Fe2+ intensity calculation (Figure A4.24 iv) used similar parameters to the CFA indexTSG (not shown) and displayed a relatively different distribution than the Fe-oxide Intensity IndexTSG (Figure A4.24 iii). The red reflectance peak/CFA index (Figure A4.24 vi) produced a distribution that mapped out the darker areas of the imagery, associated with vegetation. The CHpd5 RLU and the southern portions of CHpd4, CHep1, Aed4 and CHer2 all displayed low values for this calculation. This was due to the absorption at red wavelengths by vegetation obscuring the soil materials. The dry and arid vegetation spectra would also cause a slight increase in the reflectance in the NIR wavelength region. The presence of vegetation would influence the CFA wavelength and the intensity of the feature. The distribution of the mineral hematiteTSA was found to be associated with portions of the previously mentioned HyMap grids. The central region of the grid (Figure A4.24 vii) displayed a high abundance: similar to the Fe-oxide intensityTSG (Figure A4.24 iii) and a high for the area mapped as a sheetflow-dominated depositional plain (CHpd4). The areas that were suggested to be highly affected by vegetation in the red reflectance peak/CFA indexTSG (Figure A4.24 vi) had moderate to low abundances of hematite. This was attributed to the vegetation denuding the hematiteTSA features. White micas and chlorite/epidote distribution The distribution of smectite (Figure A4.25i) was found to be similar to the inverse of Feoxide intensityTSG and phengiteTSA (Figure A4.25ii). PhengiteTSA was found to be closely associated with selective drill spoils. The drill spoils highlighted by phengite are attributed to white mica minerals of the basement and slightly weathered saprolite. The chlorite/epidote indexTSG (Figure A4.25iii) was also associated with a number of the same drill spoils. However, the index also identified numerous locations separate to the previously highlighted drill spoils (e.g. white mica). The spectra for these pixels displayed a more pronounced absorption feature in the 2.25 μm region. Because of the lower spectral resolution of the HyMap imagery compared with the ASD, the exact cause of this feature cannot be directly identified. The mineralogy was either due to the presence of Fe-rich mineral—such as chlorite, biotite or phlogopite—or Fe-substitution in kaolinite. The most likely explanation would be because of the presence of biotite or phlogopite, because it was one of the primary minerals in the fresh basement and was found extensively in the deep sections of the drillholes examined by the HyLogger-1. Kaolinite and Al-OH indices distributions The kaolinite crystallinityTSG calculation (Figure A4.25v) identified many of the point localities as either bullseye highs or lows. Some of these bullseyes correspond to the points highlighted in the phengiteTSA and chlorite/epidoteTSG images, though there are a number of - 52 -
new points. The three images suggest that there was a great deal of variation in the mineralogy of the drill spoils, which may be sampling alternative lithologies and regolith materials at different depths. The collective results give an indication that the basement rocks consist of white mica, highly crystalline kaolinite and biotite (or chlorite/phlogopite). The occurrence of amphibole minerals in outcrop and the costeans suggests the possibility of the presence of Mg-OH minerals. This was not seen directly seen in the HyMap imagery, although the presence of ‘carbonate’ features could be attributed to hornblende or magnesium clays. The second kaolinite crystallinityTSG calculation (Figure A4.25vi) did not identify any highpoint localities, although there were several distinctive low regions. The CHep2 and CHpd4 units displayed the greatest area of low crystallinity, whereas CHpd5 and SSer1 displayed moderate values. The low at 460120 mE 6449000 mN corresponded to a group of dark pixels in the HyMap data and ortho-image. Ground-truthing of this locality identified a cluster of western rosewoods (Alectryon oleifolius) along the margins of the saprolite exposure. The Al-OH intensityTSG was found to be similar to the Fe-oxide abundanceTSG distribution, with the saprolite regions having a low value (Figure A4.25iv). The alluvial regions displayed moderately high values: reflecting the sparser vegetation in these regions. The Al-OH wavelengthTSG (Figure A4.25vii) showed highs for the saprolite (SSer) and the area adjacent to the alluvial plain (Aap and CHpd3). A number of point localities with high wavelengths existed in the southwestern portion of the image. From the SSer, there was a gradual decrease in the Al-OH wavelength to the north. This trend represented a colluvial dispersion pattern of decreasing kaolinite abundance and the dispersion of material from the drill spoils. The CHfs RLU displayed a slightly longer Al-OH wavelengthTSG, which may be attributed to the collection of dispersed materials down slope and into Aed4. The materials in the depression were then transported by alluvial processes during rainfall events to CHfa and re-deposited. This trend is seen in the halloysite/kaoliniteTSA abundance map of the region, which has a high abundance in the RLU corresponding to CHfs (Figure A4.25viii).
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Figure A4.25: HyMap surface mineralogical maps of Al-OH minerals and associated parameters. The waste spoils from the diamond drillholes can be seen to display variations in mineralogy, from (ii) phengitic to (iii) having more chlorite/epidote. ‘HyMap’ refers to the abundance of the mineral, whereas ‘TSG’ are calculated from TSA™ algorithms.
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Integration of mineralogical results and interpretations of the regolith mineralogy of the White Dam Prospect Figure A4.26 shows a summary of the spectral interpretations of an average profile from the costeans. The PSA unit was found to occur throughout most of the area and displayed similar features to the red-brown materials, but with broader and shallower absorptions. This was attributed to the higher abundance of quartz and feldspars, which do not have strong spectral features. The XRD results showed the PSA unit on average to have high abundances of orthoclase, albite and quartz, with varying concentrations of smectite and mica/illite. HyMap spectra were found to have a very close resemblance of the PSA material, as shown by the blue spectra in Figure A4.26(a and c). The HyMap data were unable to produce coherent data in the 1.4 μm and 1.9 μm regions because of atmospheric water (Figure A4.26 b). The presence of Fe-oxides was mapped, although the discrimination between hematite and goethite could not be determined because of noise in the HyMap data at diagnostic wavelengths (Figure A4.26 a). The CFA and CTS were clearly defined in the HyMap imagery, with comparable spectral responses to the laboratory measurements. RCAs were difficult to distinguish using spectral techniques in both the field spectrometer and the HyMap imagery. The carbonate nodules from the profiles exhibited kaolinitic absorption features on the outer rind, but, once crushed or dissected, displayed carbonate absorptions. When bulk samples were measured, there was an absence or a minor Mg-OH absorption at 2300 nm, which coincides with secondary Al-OH absorption features. In general, mixed soil samples with a higher abundance of RCAs produced spectra of a higher albedo (larger reflectance percentage), unless the outer surface of the nodules were completely coated with fine soil material. Surface soil samples measured with the ASD FieldSpec showed little variation in their spectra. Persistent spectral signatures derived from the presence of poorly ordered kaolinite, muscovite/illite, smectites and hematite were observed. The presence of quartz—identified by XRD analysis of the soil samples—weakened and diluted the absorption features. The surface samples displayed a near symmetrical absorption feature at 2.207 μm, which was related to Al-OH (Figure A4.26 f). An inflection in the reflectance spectra—when displayed with the hull-quotient removed—occurred on the absorption at 2.156–2.177 μm and 2.227– 2.245 μm as a result of the mixture of clay minerals (kaolinite–illite–smectite) in the soils. The presence of these minerals was confirmed by XRD analysis of the PSA materials. Water absorptions occurred at 1.912 μm and 1.415 μm: the latter feature was also related to hydroxyl features in minerals. The depths of these features can be related to the abundance of free water interlayered with clay minerals and the distinction of pedolith and saprolith. In the VNIR regions, the CFA of hematite was clearly definable by a broad 0.896 μm absorption and the CTS varied from 0.584 to 0.600 μm. Shifts in the wavelengths of these features are related to the size fraction of the Fe-oxides, the ratio of hematite to goethite, the abundance of opaque minerals and the substitution of Fe2+ and Al3+ (Cudahy and Ramanaidou 1997). The ASD FieldSpec measurements of the costean profiles exhibited a variation in spectral properties with depth. The upper samples displayed similar features to the surface traverse samples, which were termed the PSA unit. In the areas close to exposures of saprolite, the PSA unit was only a few centimetres thick. These areas were mapped as erosional rises (Brown and Hill 2003), demonstrating that slope-angle closely corresponds with the thickness of material of the upper-most layers for these RLUs.
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Figure A4.26: Profile summarising the spectral properties of the regolith materials collected from the costeans at the White Dam Prospect. (a, b, c) A HyMap spectra from a pixel in a PSAdominated region of the White Dam Prospect is shown at the top of the profile as a comparison. Characteristic features were identified for each of the different regolith horizons, allowing the mapping of saprolite, in situ pedolith and three types of transported materials. Differentiation of the in situ materials (g–p) was able to be performed. See text for a detailed explanation of the figure.
Below the PSA unit, a discontinuous transitional layer appears to be a mixture of the uppersoil and the underlying RB pedal unit. The RB pedal unit could be differentiated from the PSA unit by the deeper 1.415 and 1.912 μm water features as well as the stronger 2.207 μm absorption, which was related to the greater abundance of smectite. This was identified by the XRD analysis, which also noted a lower abundance of quartz in the RB unit. The CTS occurred at shorter wavelengths and the CFA was marginally deeper. A shift in the CTS was observed between crushed peds and unconsolidated sample material: to longer wavelengths and a more-rounded shoulder feature in the hull-quotient displayed reflectance spectra. This was attributed to the differing grain sizes of the Fe-oxides (Crowley 1986; Hunt et al. 1971). - 56 -
In the Al-OH region, the 2.229 μm shoulder for pure kaolinite is represented as an inflection in the right-hand side of the peak. In the RB and following units, there is an increase in the depth of the 2.162 μm absorption feature and the presence of a 2.229 μm shoulder: creating an inflection. An increase in the depth of 2.169 μm, and a decrease in the 2.229 μm Al-OH absorptions, occurs with increasing depth in the profile. The slope of the shoulders becomes less smooth, with increasingly prominent inflections on both sides. The unconformity between the top of weathered saprolite and the lower pedolith can be determined by the change in crystallinity of the mineral kaolinite. The crystallinity of kaolinite can be determined by the depth of the 2.162 μm absorption, with well-ordered kaolinite possessing a deeper absorption. Poorly ordered kaolinite is indicative of pedogenic in situ soils and transported materials. The weathered basement in the lower portion of the profiles displayed highly crystalline kaolinite spectral signatures with deep 2.206 μm, 2.162 μm Al-OH absorptions and a 2.229 μm shoulder. The spectra demonstrated a weak 1.912 μm feature—representing a lack of water—and a 1.414/1.399 μm doublet, with a lack of Fe-oxide absorptions. The presence of goethite in the weathered saprolite was distinguishable from hematite by the deep broad 0.990 μm CFA and the presence of a 0.671 μm absorption related to the crystal field splitting energy. A change in the absorption features in the 0.400–1.500 μm region due to Fe-oxides can be used the mark the boundary between the upper soil layer (Post European unit) and the underlying pedal RB soil unit. The boundary between the basement and the overlying soil layers can be identified by a change to well-ordered kaolinite crystallinity as well as a shift in the charge transfer shoulder of Fe-oxides at approximately 0.600 μm to shorter wavelengths in the saprolite. The presence of the mineral goethite in the weathered saprolite occurred at various depths, which is shown by a shift in the 0.896 μm CFA to longer wavelengths.
Conclusions The primary objective of this research was to understand the mineralogical and spectral properties of the regolith materials in the White Dam area. This was undertaken by the utilisation of remotely sensed data, proximal spectral techniques and XRD analyses. A major component of the work involved the construction of a regolith-landform map of the region and the validation of the remotely sensed data that was used in the creation of the map. Remote sensing of the regolith Understanding of the regolith-landforms and associated materials was a critical element in the processing and interpretation of the remotely sensed data. In turn, the process of regolithlandform mapping was greatly aided by the use of remotely sensed datasets. Radiometrics was found to be an important dataset for mapping regolith-landform features, as well as identifying areas of potential alteration. The coarse resolution of the airborne gammaray spectroscopy data was more suited to large scale features; therefore small saprolite exposures may not be detectable. However, there is a large and reasonably consistent variation in the radioelement abundances of regolith-materials that allows a significant proportion of information to be detected from radiometric datasets. Radiometrics has the potential to play an important role in regolith-landform mapping when integrated with optical and other remotely sensed datasets. Multispectral sensors were useful for regional overviews and generalised interpretations of the regolith-landforms and their constituent minerals. Three-dimensional draping and bandratio techniques were found to improve the ability of multispectral imagery to discriminate features over colour-composite images of un-processed single bands. However, multispectral imagery was unable to resolve spatial features at a detail required for this project, and the
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limited spectral resolution was only able to identify groups of materials, such as clays, Feoxides and vegetation. Hyperspectral remote sensing and mineral mapping of the regolith This research showed that the mineralogy of surficial materials can be determined from remotely sensed spectral methods—aided by proximal ground control measurements—in the White Dam area. Pre-processing and atmospheric correction of airborne hyperspectral imagery played an important role in the effectiveness of the information-extraction processes. Selective atmospheric-correction methods enhanced the overall spectral response of the scene: improving the ability to identify mixed end members. The development of semiautomated software for the application of a similar technique to the modified-radiative transfer/empirical line calibration technique performed in this study would greatly enhance the quality and interpretability of the HyMap imagery over less-focussed atmospheric correction methods, such as HyCorr or ACORN. Mixture tuned match filtering Traditional mineral mapping processes were performed on the HyMap data and were able to extract end members of regolith and other surficial materials from within the scene. The mixture tuned matched filter un-mixing process was successful at classifying regolith materials, such as the presence of kaolinite, goethite, green vegetation, dry vegetation, muscovite, muscovite-rich soils, sheetwash-related soils, large metal objects (>3 m), hematite and RCAs. Mineral mapping using mixture tuned matched filter unmixing methods allowed specific minerals to be identified and mapped throughout the swaths. However, because of the homogeneity of the regolith-landforms, the distribution of the end members was found to be scattered and sparse. Even though the thresholds used in the creation of the mineral distribution maps were broad, using generous cut-offs for the matched filter score, the distribution maps were incomplete and displayed a scattered appearance. Spectral indices Spectral indices performed on masked data were effective at identifying the key regolith mineralogical features of the HyMap imagery and proved less time consuming than unmixing processes. The key features extracted using indices were: green vegetation and cellulose; Fe-oxides abundance, hematite and goethite proportions; the abundance of Al-OHbearing minerals, which included kaolinite and white micas; and the presence of carbonate or Mg-OH- bearing minerals. Spectral indices were found to be more suited to extracting mineralogical and regolithlandform information in homogeneous regolith-dominated environments, such as the Curnamona Province. In spite of the improved ability to map the overall features of the HyMap dataset, spectral indices were prone to missing small-scale anomalies that were highlighted by the mixture tuned matched filter technique. Interpretations of the remotely sensed data Mineralogical interpretations of the regolith from the remote hyperspectral data divided the White Dam area into alluvial regolith-dominated, in situ regolith-dominated and bedrockdominated terrains. Alluvial regions were characterised by large abundances of vegetation and soils with a hematite-rich mineralogy. Highly weathered areas of in situ material consisted of goethite and various forms of kaolinite, whereas the bedrock-dominated regions displayed white mica/muscovite mineralogy. Areas flanking bedrock exposures commonly consisted of shallow muscovite-rich soils containing regolith carbonate associations, with both materials able to be mapped by the HyMap data using the techniques discussed earlier.
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A majority of the slightly weathered bedrock exposures in the MacDonald Ranges—derived predominantly from Adelaidean metasediments—did not have an obvious spectral signature in the VNIR–SWIR region. Detection of these exposures by remotely sensed imagery was only seen where the rocks had been altered or weathering products occurred. Bedrock that displayed poorly defined spectral features was discriminated from shadow and vegetation through the use of modified traditional hyperspectral mineral mapping techniques. Surficial mineralogy of the White Dam Prospect Examination of the spectral properties of samples collected from the surface of the White Dam Prospect were found to display very little information regarding the underlying regolith, which was attributed to the predominantly quartz and feldspar mineralogy of the postsettlement alluvium materials. Differing regolith horizons displayed variable spectral properties: reflecting compositional changes in their mineralogy, which was also shown in the XRD analyses. However, the proportions of minerals determined from the spectral and XRD analyses were not very well correlated, which was attributed to the physical characteristics of the materials that were being measured by the different techniques, as well the problem of heterogeneity of the samples. A fine red-brown transported material generally concealed the underlying regolith in lowlying and depositional regions, whereas colluvial and sheetflow transported lithic material from saprolite exposures was found to occur on erosional landforms and flanking elevated areas. Anthropogenic effects strongly influenced the regolith-landforms in the region and changed the surface features. A large proportion of the constituent minerals of surficial materials, such as quartz and feldspars in post-settlement alluvium, were aspectral in VNIR–SWIR wavelength regions. However, the weathering of feldspars and other silicate minerals typically produced spectrally distinct minerals that could be detected by proximal and remote hyperspectral sensors. Weathering halos were observed around bedrock exposures by the processed HyMap imagery: consisting of white mica-rich cores and kaolinite material on the flanking slopes, which corresponded to the colluvial and sheetflow transported weathered lithic fragments. Similar patterns were observed in the gamma-ray spectroscopy data, with a relationship of Th and K highs for mica-rich exposures in shear zones that were surrounded by Th highs, pertaining to the dispersion of weathering products downslope. Regolith carbonate accumulations Detection of RCAs in remotely sensing imagery based on un-mixing results was ambiguous, because carbonate spectral features were typically overshadowed by Al-OH (clays and kaolin) minerals. It was possible to identify regolith carbonate by mapping ‘bright’ pixels from the VNIR wavelengths, because the carbonate often occurred at the surface because of lagomorph (rabbit)-related disturbances. Such occurrences were seen as bright circular patches in the photography and in the HyMap imagery. The use of spectral indices was found to be more successful at mapping RCAs than the mixture tuned match filtering process. The constraint on this technique was the size of the warren and the spatial resolution of the HyMap data. Subsurface predictions from the remotely sensed data The identification of highly weathered saprolite in regolith-dominated regions is an important discovery because this material may give information relating to the presence of underlying mineralisation or geochemical signatures useful in exploration. The presence of saprolite exposures at the surface also suggests that the local depth to the basement is shallow. Although gamma-ray spectroscopy and hyperspectral sensors collect information about the surface materials, subsurface information can be inferred from knowledge of the regolithlandforms and related processes. Knowledge of the dispersion of materials in each regolith-
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landform is required when interpreting the spectral data because this influences the ability to estimate the thickness of the cover and the composition of the underlying regolith materials. Examination of the costeans enabled the generalised regolith profile to be interpreted for the study area. From these analyses, it was concluded that 1 m thickness of cover was enough to conceal the underlying materials from the airborne spectral sensors. The surface cover of a sheetwash-derived pale-red-brown layer occurred throughout the region and was the dominant material observed in multispectral and hyperspectral data. Although small abundances of materials may have existed at the surface, they were not detectable using airborne spectral instruments. In the depositional areas, the top layer of soil was often overlying an older transported horizon of 1–3 m thickness, which unconformably covered in situ highly weathered saprolite or pedolith materials. The process of overland flow and lateral dispersion of material was found to have a significant role in the distribution of materials and their abundances at the surface in areas of cover. The movement of lithic material from exposures on the crests of rises downslope to the low-lying depositional areas resulted in the surface material containing small abundances of lithic materials in a matrix of transported and in situ soil. Where cover was greater than 2 m, calcite was not found in the surface soil samples because of the RCAs occurring lower in the profile. Areas where a powdery morphology RCA material occurred in the profile had a lower abundance of calcite then where the RCAs formed a hardpan. Overall, the presence of RCAs was unreliably mapped by spectral methods, with photographic interpretation the best method for identification, unless gypsum was present. The regolith-landforms blanketed by this material did display some detectable variation in spectral characteristics because of differences in vegetation and surface processes. This allowed the mapping of different RLUs using the remotely sensed datasets. The hyperspectral and, to a lesser degree, the multispectral datasets enabled a greater differentiation of RLUs and materials than the ortho-photography because of their extended utilisation of the EM spectrum, which allows the differentiation of materials based on their electro-magnetic and molecular properties, and not just on colour. In areas of active erosion, the underlying materials were observable in the remotely sensed data because of the dissection of the profile. Transport of lithic material from the process of alluvial and colluvial transport was observed in channels and downslope from bedrock exposures. The spectrally derived mineralogy from the HyMap imagery of the material near to the exposures was consistent with fresh basement and slightly weathered saprolite measured with the HyLogger-1 and ASD instruments. The mineralogy of these areas consisted of white micas, illite, montmorillonite, hematite and minor goethite and kaolinite. The basement exposures and channels in the regolith dominated areas displayed a spectral mineralogy correlating with the moderately to highly weathered saprolite samples. This consisted of kaolinite and goethite, with minor illite and montmorillonite. Subsurface mineralogy of the White Dam Prospect Useful information was extracted from the HyLogger-1™ data with regard to the nature of the mineralogy of the deeper materials from White Dam Prospect. The subsurface mineralogy of fresh-bedrock consisted predominantly of aspectral minerals that did not have a diagnostic signature in the VNIR–SWIR wavelength regions. Mineralised intervals of the core were found to be more weathered than the surrounding materials, which was related to the acidleaching conditions caused by the oxidation and weathering of sulphides. The weathering products of mineralised zones displayed distinctive spectral signatures compared with the slightly weathered surrounding rock. Further investigation into the highly weathered material associated with mineralisation is required to characterise the spectral properties and determine - 60 -
the relationship with un-mineralised, highly weathered regolith materials with a similar mineralogy. Final conclusions of the regolith-landform mapping From this research, we can conclude that although most of the regolith from the Olary Domain is highly similar and can be characterised from ground and airborne spectral measurements, there are detectable changes in the landform units, as well as the measurement of different portions of the regolith profile. The ability to detect saprolite features in this environment aids regolith-landform mapping and exploration. The depth of the transported cover sequence can be inferred from the vegetation and spectral mineralogy of the surface minerals. A correlation was found between the spectral interpretations of the mineralogy and regolith materials from the airborne hyperspectral data and the results from measurements of surface samples collected from the same areas. However, the mineralogy of the subsurface materials was found to vary greatly and was dissimilar to the material blanketing most of the surface of the regolith-landforms that covered a larger portion of the area. Further analysis of the subsurface regolith profile found that these materials were able to be identified at the surface throughout the HyMap swaths. Recommendations and future work Before the completion of this research, there had been a number of large, multi-swath regions of Australia mapped with the HyMap sensor (Musgrave Block—Stamoulis et al. 2001; Mauger et al. 2002; Broken Hill Domain—Robson et al. 2003; Kalgoorlie—Cudahy et al., 2005; Mt Isa Domain and Central Queensland – Jones et al. 2008) . These large datasets were acquired in less-than-ideal conditions and required considerable adjustment to the swaths to allow seamless mosaicing of the mineral map products. As the application of airborne hyperspectral remote sensing for regional investigations continues to expand, more research will be required to improve the methods for the production of seamless mosaics. This was one area that was not extensively covered by this research and was found to be an issue when the final mineral maps were produced.
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Postscript: Spectral analysis of the costean samples The de-stepped ASD FieldSpec data were checked for errors, which consisted of examining for spectra with low albedo or measurements that mis-represented the bulk sample. The corrected ENVI spectral library of the entire 3400 measurements were imported into TSG with the data logs and analysed for dominant mineralogy. Minerals used in the TSA algorithm for the VNIR were restricted to hematiteTSA, Fe2+ goethiteTSA, Fe3+ goethiteTSA and jarositeTSA. Sulphide minerals were omitted from the TSA algorithm because they were not visually observed during sample collection. A similar analytical technique to the one described for the surface soil ASD measurements was performed on each of the sampled costean’s spectral measurements. Mineralogical results from TSA were treated as abundance percentage and scaled between 0 and 100 %. TSGs calculations were scaled as high or low values, because they were dependent on the wavelength features that were under scrutiny. Spectral measurements were subdivided and gridded to represent the individual vertical faces of the northerly trending costeans. The figures A.27, A.28, A.29, and A.30 below include additional information to Figure A4.14 on the in situ regolith materials of the costeans, along with regolith carbonate morphology, type of ferruginisation, colour of the in situ materials and the regolith stratigraphy. The following figures are gridded mineralogical abundances sections, calculated from spectral measurements collected from costean samples. Sections denoted ‘ASD’ are mineral abundances, whereas ‘TSG’ denotes an index calculation based on absorption feature depths.
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