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Disclaimer: Whilst every care has been taken in the production of this manual AusSpec International Ltd. accepts no liability express or implied, by statute, common law or otherwise for the accuracy of the information contained herein. AusSpec International Ltd also reserves the right to change at any time its interpretation of data, analytical approaches and data analysis techniques.
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Disclaimer: ... i
SESSION 1: INTRODUCTION TO SPECTRAL GEOLOGY ... 0
INTRODUCTION ... 0
FIELD SPECTROMETERS ... 0
AUTOMATED MEASUREMENT: CORE LOGGING AND HYLOGGING ... 0
SHORT WAVELENGTH INFRARED (SWIR) SPECTRAL REGION ... 1
SWIRINFRARED SPECTROMETRY ... 1
PATTERN RECOGNITION ... 2
EXERCISE 1:PATTERN RECOGNITION ... 3
ABSORPTION FEATURES RELEVANT TO THE SWIR ... 4
SUITABLE MINERAL GROUPS FOR SWIRANALYSIS ... 4
UNSUITABLE MINERAL GROUPS FOR SWIRANALYSIS ... 4
ABSORPTION FEATURES RELEVANT TO THE VISIBLE-NEAR INFRARED (VIS-NIR) ... 5
THE WAVELENGTHS OF THE MAIN ABSORPTIONS IN THE SWIR ... 7
EXERCISE 2:SPECTRAL ABSORPTION BANDS ... 7
INFORMATION IN THE REFLECTANCE HULL ... 9
REMOVING THE REFLECTANCE HULL ... 9
SESSION 2: A STRUCTURED APPROACH TO SPECTRAL ANALYSIS ... 11
INTRODUCTION ... 11
ASTRUCTURED APPROACH TO SPECTRAL INTERPRETATION ... 11
EXERCISE 3:SPECTRAL INTERPRETATION ... 12
SPECTRAL VARIATIONS WITHIN MINERAL GROUPS ... 13
Introduction ... 13
Influence of Crystallinity ... 13
Influence of Composition ... 13
Crystallinity and its Effect on the Spectral Responses of Kaolinites ... 14
Crystallinity and its Effect on the Spectral Responses of illite ... 15
Composition and its Effect on the Spectral Responses of Sericites ... 16
Composition and its Effect on the Spectral Responses of Chlorites ... 17
Influence of Octahedral Cation on the Spectra of Smectites ... 19
Composition and its Effect on the Spectral Responses of Carbonates... 20
Other minerals to display compositional variations include: ... 22
SPECTRAL MIXTURES ... 23
Introduction ... 23
Non-Linear Mixing ... 23
What to Expect in Mixed Spectra ... 24
How to Interpret Simple Mixtures ... 24
Problematic Minerals in Mixtures ... 25
EXERCISE 4:MIXED MINERALS, CRYSTALLINITY AND COMPOSITIONAL VARIATIONS. ... 26
INTRODUCTION ... 29
SOIL SAMPLES ... 29
THIN SECTIONS ... 30
OUTCROP/SOIL ROCK CHIPS ... 30
RAB/RCDRILL CUTTINGS ... 31
DRILL CORE ... 31
GEOCHEMICAL PULP SAMPLES ... 32
INFLUENCE OF WATER ... 33
Recognising the Presence of Water ... 33
Wet Samples... 33
Drying Wet Samples ... 33
NOISE IN SPECTRA ... 34
Recognising Noise ... 34
Overcoming Noise ... 35
OTHER MEASUREMENT AND SAMPLE ARTEFACTS... 35
ARTEFACT FEATURES ... 35
PARTICLE SIZE EFFECTS ... 36
Powders versus Rocks ... 36
Significance of Particle Size Effects ... 36
SESSION 4: DATA ANALYSIS SOFTWARE TSG PRO (THE SPECTRAL GEOLOGIST) ... 37
INTRODUCTION ... 37
GETTING STARTED ... 37
THE SUMMARY SCREEN ... 37
Overview or Spatial Plots ... 37
SPECTRUM SCREEN ... 38
STACK SCREEN ... 38
LOG SCREEN ... 38
The Pop Up Context Menus ... 39
Spectral Items and Scalar Items ... 39
CREATING NEW SCALAR DATA ... 39
Importing Scalar Data Exercise (optional) ... 39
SCATTER SCREEN ... 40
FLOATER WINDOW ... 40
SESSION 5: APPROACHES TO SPECTRAL ANALYSIS ... 41
INTRODUCTION ... 41
MANUAL INTERPRETATION ... 41
AUTOMATIC MINERAL IDENTIFICATION ... 41
The Spectral Assistant ... 42
User defined Spectral Libraries and Custom Libraries ... 44
SPECTRAL PARAMETERS ... 46
What are Spectral Parameters ... 46
Calculating Spectral Parameters (using TSG)... 46
Exercise 7 Calculating Spectral Parameters: ... 49
Commonly Used Spectral Parameters ... 50
SESSION 6: CASE STUDIES AND CLIENT SPECIFIC EXERCISES ... 56
S
ESSION
1:
I
NTRODUCTION TO
S
PECTRAL
G
EOLOGY
Introduction
Spectral analysis is a means of obtaining rapid and cost effective data on sample mineralogy and on mineral characteristics. As measurement is fast and sample preparation minimal, very large volumes of data covering large numbers of samples can be obtained in a short time. These data can then be used for a number of applications in exploration and mining, such as:
- Delineating alteration systems;
- Understanding alteration-mineralisation relationships; - Target generation;
- Tackling grade control problems;
- Identifying overburden/bedrock boundaries.
Over recent years, spectral analysis has been effectively applied to mineral exploration and characterisation of alteration suites worldwide and in a wide range of geological settings.
Field Spectrometers
The PIMA and ASD spectrometers (Terraspec, Labspec and Fieldspec) are a generation of field-portable instruments that are ideally suited to field-based alteration mapping. The important characteristics of field spectrometers include:
- Laboratory quality spectral data of minerals, permitting the determination of fine spectral details, such as crystallinity variations and elemental substitution.
- An internal light source in most cases, no restrictions on location or time of day; - Each measurement requires no sample preparation (although samples need to be dry);
- Spectra are acquired in ~3-60 seconds, allowing the rapid collection of a large number of analyses in a short time frame;
- Instant display of spectra on PC/palmtop screen;
- Measurements can be made of all types of samples including, diamond drill core, RC and RAB chips, outcrop and soil samples and selective measurements may be made of in situ veins, breccia fragments and small-scale alteration zoning (provided that samples are dry).
Automated measurement: Core logging and HyLogging
In cases where the volume of samples is very large, for example when many kilometres of core are needed to be analysed, automated measurement is more suitable than hand held. One of the options for automated analysis is to use a HyLogging system.
An added advantage of the HyLogging system is that it makes systematic and objective readings of the core. In hand held readings, in contrast, the selection of the locations to measure are controlled by an operator and this still leaves open the possibility of some subtle but potentially significant intersections being missed.
Short Wavelength Infrared (SWIR) spectral region
The spectrometers discussed above measure the spectra of rocks and minerals in the short wavelength infrared (SWIR), from 1300-2500nm. They all measure the reflected radiation from the surface of a sample. The ASD and HyLogging spectrometers also measure in the visible-near infrared (vis-NIR) wavelengths. A new generation of PIMAs will be available by 2009 which will also measure the full visible-NIR-SWIR range.
The figure below illustrates the vis-NIR-SWIR wavelength range relative to the visible and Mid-Infrared (MIR) wavelengths.
As most common alteration minerals have their absorption features in the SWIR, this spectral region will be discussed in most detail in this manual. The visible-NIR absorptions will be discussed also, where necessary.
SWIR Infrared Spectrometry
SWIR infrared spectrometry is a useful technique for mineral identification because many minerals have
characteristic spectral signatures or spectra. This is because a mineral spectrum is dependent on
various crystallographic factors unique to each mineral species.
When a sample is illuminated by the light source from the spectrometer, certain wavelengths of light are absorbed by the minerals in the sample, as a result of sub-molecular vibrations. This vibration is the result of bending and stretching of molecular bonds in the minerals.
Although the molecular vibrations have primary (or, more correctly, fundamental) absorption features in the Mid-Infrared, the absorptions that we see in the SWIR are related to harmonics of these fundamental vibrations.
PIMA and ASD ASD HyLog (+ new
Pattern Recognition
A mineral spectrum can be thought of as that mineral’s “signature”. This is because, for most minerals, their absorption features together form a distinctive pattern characteristic of a particular mineral group. With practice, these signatures can be recognised by simple pattern recognition, which allow different minerals to be identified.
Exercise 1: Pattern recognitionBelow are plots of 13 'unknown' spectra. Compare these signatures to the 7 library spectra (A-G) and label the unknowns with the letter of the matching library spectrum.
Absorption Features Relevant to the SWIR
The majority of the absorption features in the SWIR are related to the bending and stretching of the bonds in:
- Hydroxyl (OH); - Water (H2O); - Carbonate (CO3); - Ammonia (NH4).
It is the hydroxyl anion that produces the majority of the diagnostic absorptions in the SWIR mineral spectra, because its crystallographic position and environment varies between most minerals. The OH vibrations also form combinations with what are called lattice vibrations and absorptions related to the vibrations between:
- AlOH; - FeOH; - MgOH.
The carbonate anion produces characteristic SWIR absorption features in carbonate spectra.
In contrast to OH and CO3, the absorption features associated with water and ammonia often do not differ between minerals, and therefore are not always diagnostic.
Suitable Mineral Groups for SWIR Analysis
The molecules OH, water, AlOH, FeOH, MgOH, CO3 and NH4 are found as major components in:
Phyllosilicates (e.g. clays, chlorite and serpentine minerals); Hydroxylated silicates (e.g. epidotes and amphiboles); Sulphates (e.g. alunite, jarosite and gypsum);
Carbonates (e.g. calcite, dolomite, ankerite and magnesite); Ammonium-bearing minerals (e.g. buddingtonite, NH4-illites).
It is the minerals in these groups that can be detected in the SWIR spectra.
Unsuitable Mineral Groups for SWIR Analysis
These are minerals that do not have structural OH, water and CO3 do not display any diagnostic absorption features in the SWIR wavelength region. These minerals include quartz and feldspar. The spectra of samples dominated by these other minerals can, however, display absorptions associated with non-diagnostic secondary components. For example:
- Broad water bands, associated with fluid inclusions;
Absorption Features Relevant to the Visible-near infrared (vis-NIR)
Whereas the absorptions in the SWIR are associated with molecular bonds, those observed in the visible-NIR are associated with sub-atomic transitions.
The majority of the absorption features commonly observed in the vis-NIR are related to the electronic transitions in iron (both ferric (Fe3+) and ferrous (Fe2+).
In general, most iron bearing minerals will have ferric and/or ferrous absorption features in the vis-NIR. Other commonly observed transition elements in minerals that also give rise to features in the vis-NIR include copper and manganese.
In summary, minerals with diagnostic absorption features in the vis-NIR include:
Iron oxides, goethite, hematite: ferric features
Pyroxenes (opx and cpx), olivines: ferrous features
Hydroxylated silicates with Fe, such as chlorite, biotite, epidote: ferric and ferrous features;
Sulphates, jarosite: ferric iron;
Iron carbonates: ferrous iron, with intensity dependent on iron content of carbonate. Leads to the Fe2+ slope seen in the SWIR;
Cu-oxides and carbonates;
Mn carbonates and silicates;
Visible wavelengths can also include features associated with rare earth elements
He
Go ~930nm Hematite and goethite ferric absorption features Red Peak Intense charge transfer
Pyroxene ferrous absorptions
OPX
CPX Fe Carbonate ferrous absorptions
(illite+Fe carbonate assemblage)
The Wavelengths of the Main Absorptions in the SWIR
The absorption features of OH, water, AlOH, FeOH, MgOH and CO3 commonly occur in characteristic wavelength bands.
These are:
- OH ~1400nm (also ~1550nm, ~1750-1850nm in some minerals) - Water ~1400nm and ~1900nm
- Al-OH ~2160-2220nm - Fe-OH ~2230-2295nm - Mg-OH ~2300-2360nm
- CO3 ~2300-2350nm (and also at 1870nm, 1990nm and 2155nm)
The wavelength positions of these absorptions can also give valuable information on the composition of the mineral, particularly those of the AlOH, FeOH, MgOH and CO3 absorptions (see Spectral Band Figure on the next page).
These absorption features are significant because in noisy or weak spectra they allow identification of at least the mineral group/composition (e.g as an MgOH mineral, or AlOH clay).
Exercise 2: Spectral absorption bandsAllocate the spectra from Exercise 1 to their compositional groups using the Spectral Band Figure (see next page).
1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 Wavelength (nanometres)
Water and OH (Water = single broad absorption +/- shoulders) (OH = can be multiple sharp absorptions of varying intensities) OH (eg sulphates +kaolinite clays + diaspore) Water (Single broad asymmetric absorption +/-shoulders) AlOH FeOH CO3 MgOH (if deepest) and/or (Otherwise check in AlOH band as it may be a 2ndy AlOH feature) 1 3 5 0 1 5 5 0 1 7 2 0 1 8 6 0 1 8 8 0 2 0 4 0 2 1 6 0 2 2 2 0 2 2 3 0 2 2 9 6 2 3 0 6 2 3 6 5
Note: If the deepest absorption is in the AlOH waveband, absorptions at these wavelengths will include SECONDARY AlOH absorptions of that mineral.
Information in the Reflectance Hull
The raw spectral data are termed reflectance spectra. In addition to displaying absorption features, the reflectance spectra are influenced by absorptions out of the SWIR range. These are commonly due to:
- Ferrous (Fe2+) iron absorptions around 1000nm;
- Strong water and carbonate absorptions around 2700nm.
The influence of these out-of-range absorptions extend into the SWIR wavelength range and affect the overall background shape (or continuum) of the spectrum. This background curvature of the reflectance spectrum is known as the “reflectance hull”.
Significantly, the influence of ferrous iron on the reflectance spectrum provides an added dimension of spectral information. This allows Fe2+ iron-bearing minerals to be distinguished from non-iron-bearing equivalents in cases where these minerals are otherwise spectrally identical (e.g. actinolite and tremolite).
Removing the Reflectance Hull
Although the reflectance hull contains useful information, the curvature tends to distort the spectral absorption features in the SWIR. This can make determination of the wavelength positions of the longer wavelength absorptions difficult, particularly those on the steepest parts of the reflectance spectrum. It is advisable to process the spectra to remove the reflectance hull and to enhance the spectral absorption features in the SWIR. This enhancement is achieved by applying a base-line correction to the spectral data.
The correction commonly used with the SWIR spectral data is: - Hull quotient, in which the hull and reflectance spectra are ratioed The result of this processing is a "hull quotient” spectrum.
The features in the SWIR part of the spectrum are best viewed using the hull quotient corrected spectra. However, the features in the visible-NIR part of the spectrum are often best viewed as reflectance spectra, as they are broad and the hull quotient correction can typically distort their wavelengths. In particular the steep absorption slope in the iron oxide spectra, which can provide useful information on the iron oxide intensity, will be removed by this process.
The figure below illustrates the method of hull quotient correction. class:biotite/chlorite mineral:Fe2+ Wavelength in nm R e fl e c ta n c e 600 900 1200 1500 1800 2100 2400 0 .2 A B
Hull Correction = A/B
1300 2500 Hull Line Raw Reflectance Hull Quotient 350
S
ESSION
2
:
A
S
TRUCTURED
A
PPROACH TO
S
PECTRAL
A
NALYSIS
Introduction
Although the spectral signatures or patterns of minerals are characteristic, it is cumbersome to identify an unknown spectrum by comparison with spectra in a large spectral library. It is useful therefore to have a methodology to follow.
Interpretation of SWIR mineral spectra is based on the following important points: - Most minerals have a characteristic spectrum between 1300-2500nm;
- Most minerals have major diagnostic absorption features between 2050-2450nm;
- Most minerals can be grouped spectrally according to the wavelength position of the deepest absorption feature between 2050-2450nm.
A Structured Approach to Spectral Interpretation
The following steps provide a method for easy spectral interpretation, using the GMEX spectral library provided in the course. These steps are summarised in the figure on the next page.
1. Obtain the best spectrum of your sample (hull corrected and smoothed if necessary) and get the best display of the spectrum on paper or on the PC screen.
2. Look at the 2050-2450nm spectral region.
3. Identify the deepest absorption in the 2050-2450nm spectral region and note its wavelength position.
4. Look this wavelength up in the spectral search index (on the CD) and identify which spectral group the spectrum belongs to.
5. Go to that spectral group in the spectral library.
6. Looking at the 2050-2450nm spectral region, take into account other absorption features and compare the unknown spectrum with each of the spectra of this spectral group.
7. Take into account other absorption features between 1300-2050nm and cross check your identification to confirm similarities with the library spectrum, and to make a final distinction between spectrally similar minerals.
Most spectra will be identified after these steps have been carried out. However, if it is still not possible to make a full identification then you may be looking at a mixed spectrum, comprising overlapping absorption features of different minerals.
Exercise 3: Spectral interpretationInterpret the spectra from Exercises 1 and 2 using the search index in the GMEX Spectral Library and the structured approach to spectral interpretation.
Spectral Variations within Mineral Groups Introduction
Subtle spectral variations, such as wavelength shifts and variations in the shapes of the absorption features, may be observed within the spectra from a mineral group. These may be attributed to:
- Crystallinity variations; - Compositional variations;
Influence of Crystallinity
Crystallinity variations, in mineral groups such as the sericites and kaolinites, are typically represented by subtle variations in the shapes of the absorption features.
- Poorly crystalline minerals, for example, often display relatively broad absorption features with poorly developed secondary absorption features.
- In contrast, highly crystalline minerals typically have well-developed absorption features, which are often sharp and well-defined.
Influence of Composition
Compositional variations in mineral groups such as the sericites, chlorites and carbonates, are typically represented by shifts in the wavelength positions of diagnostic absorption features, with the overall characteristic spectral signature of the mineral remaining generally unchanged.
Crystallinity and its Effect on the Spectral Responses of Kaolinites
Examples of kaolinite crystallinity applications:
Delineating zonation in regolith profiles,
Distinction of weathered versus altered kaolinites,
Zoning in an alteration system.
Kaolinite crystallinity variations in weathered profiles: note change in the shape and wavelength of the 2160nm secondary kaolinite diagnostic absorption. This absorption gets weaker with decreasing crystallinity, eventually appearing as a shoulder in the most poorly crystalline kaolinites.
NOTE: TSG Plotting: colour mapping/scaling for kaolinite crystallinity (slope measurement) min=0.98, max =1.1, this will colour the low crystallinity in dark blue and high crystallinity in red.
Crystallinity and its Effect on the Spectral Responses of illite
Examples of sericite crystallinity applications:
Mapping clay alteration in epithermal systems. Distinguishing secondary and primary micas.
Changes in crystallinity are mostly observed in the changing relative depths of the water and AlOH absorptions. Illites typically have mixed layers of smectite-white mica, as crystallinity increases the smectite layers decrease and this is paralleled by the decrease in the depth of the water absorption.
NOTE: TSG Plotting: colour mapping/scaling for white mica (illite) crystallinity: min=0.8, max =2.2, this will colour the low
crystallinity in dark blue and high crystallinity in red. Water band
Composition and its Effect on the Spectral Responses of Sericites
White mica (illite, sericite etc) composition can be determined using the wavelengths of the diagnostic AlOH white mica absorption. This occurs at different wavelengths depending on the white mica octahedral Al content.
White mica AlOH wavelength
Paragonite (high Al, including paragonitic illite) 2180-2190nm Muscovite (“normal” potassic including illite) 2200-2210nm Phengite (low Al, Mg-Fe mica, including phengitic illite) 2216-2228nm
NOTE: TSG Plotting, colour mapping/scaling for white mica composition: min=2200, max =2216nm, this will colour the
paragonitic mica in dark blue and phengitic mica in red, and muscovitic compositions mostly in green.
Paragonitic
Phengitic Muscovitic
Examples of sericite composition applications:
Distinguishing secondary and primary micas Mapping proximity to High Al Low Al Mg-Fe substitution AlOH wavelength shift
Composition and its Effect on the Spectral Responses of Chlorites
Chlorite composition can be determined using the wavelengths of the FeOH and MgOH absorptions of chlorite. These occur at different wavelengths depending on the chlorite Mg:Fe ratio. Note biotite also shows similar variations.
Chlorite FeOH wavelength MgOH wavelength
Mg Chlorite 2240-2249nm 2320-2329nm
Int Chlorite 2250-2256nm 2330-2348nm
Fe Chlorite 2257-2265nm 2349-2360nm
NOTE: TSG Plotting, colour mapping/scaling for chlorite composition: min=2249, max =2256, this will colour the Mg chlorite
in dark blue and Fe chlorite in red.
Examples of chlorite composition applications: Mapping proximity to mineralisation in VMS and other systems. Distinguishing secondary and primary chlorites
Important NOTE: the chlorite MgOH can be affected by the presence of carbonate which overlaps the chlorite MgOH
absorption. It is therefore usually more reliable to use the FeOH absorption for determination of chlorite composition.
R2 = 0.8619 2242 2244 2246 2248 2250 2252 2254 2256 2258 2260 2262 0 0.2 0.4 0.6 0.8 1 Mg Number Wa ve le n gth (n m ) R2 = 0.85 2320 2325 2330 2335 2340 2345 2350 2355 2360 2365 0 0.2 0.4 0.6 0.8 1 Mg Number Wa ve le n gt h (n m ) Fe-rich Mg-rich
Influence of Octahedral Cation on the Spectra of Smectites
Wavelength (nanometres)
Dolomite
Siderite
Ankerite
Calcite
Reflectance spectrum of
Siderite (Spectrum 2)
Reflectance spectrum of
Ankerite (Spectrum 3)
Mn-Carbonate
(+ Sericite)
1
2
3
4
6
7
5
(2360nm)Note: Siderite –has variable wavelengths because of variable substitution i.e. by Mg and/or Mn
Other minerals to display compositional variations include:
Biotite: similar wavelength variations to those observed in chlorite, Mg biotites have low values for their FeOH (~<2249nm) and MgOH (<2330nm) absorptions, normal and Fe biotites have longer wavelength values (~>2250nm and 2340-65nm).
This distinction can be important in identification of different phases of biotite i.e. background/regional from alteration/potassic.
Alunite: The longer wavelength alunite OH absorption changes in wavelength depending on whether the alunite is potassic or sodic.
This distinction can be important in identification of different phases of alunite and possibly distinguish supergene from primary alteration alunite.
K Alunite has the diagnostic absorption <1480nm Na Alunite has the diagnostic absorption >1490nm.
Na Alunite
Spectral Mixtures Introduction
The way that the spectral responses of minerals mix is complex. Considered simply, spectral mixing can be viewed as a linear process. For example,
Mixed Spectrum = X% Mineral 1 + Y% Mineral 2 + Z% Mineral 3
In this simple scenario the intensities of the spectral absorption features could be considered to reflect the proportions of each mineral in the mixture.
Unfortunately very few mineral mixtures approximate linear mixing. This is because the spectral response of a mineral mixture is influenced by various non-linear factors, which include:
- varying absorption coefficients between different minerals. - multiple scattering within the volume of the mixed minerals;
Non-Linear Mixing
In non-linear mixing, the intensity of the absorption features often do not correspond directly to the proportion of a particular mineral in the mixture. For example,
- clays will always dominate the spectrum in mixtures of carbonates and clays,
- carbonates are typically spectrally dominated by other spectrally absorbing minerals (e.g. chlorite and sericite),
- talc will typically dominate in a mixed spectrum.
In addition, minerals that do not absorb in the SWIR (quartz and feldspar) will always be misrepresented in the SWIR spectra. For example,
- clays will always dominate the spectra of mixtures of feldspar and clays, even if present in only minor proportions.
source detector
Multiple scattering
30-100 microns
What to Expect in Mixed Spectra
Typically in mixed spectra, overlap of the spectral absorptions of more than one mineral usually produces a spectrum which is often dominated by one mineral, but which displays the spectral characteristics of other components.
The characteristics of a mixed spectrum include:
- Additional absorption features along with those of the spectrally dominant mineral; - Additional shoulders or inflexions on the dominant mineral spectrum;
- Increased depth of absorptions, relative to the normal depths of the features in the dominant mineral spectrum;
- Broadening of absorption features; - Wavelength shifts in absorption features.
How to Interpret Simple Mixtures
One of the easiest ways to identify the components of a mixed spectrum is to consider the spectral responses of a suite of samples from the same region.
These responses should represent mixtures of varying proportions of the mineral components in these samples. Given that some of the spectra will be dominated by one or other of the mineral components, this allows the interpretation of these “end-member” components in a mixed spectrum.
This type of interpretation is best achieved by comparing the suite of spectra on a stack plot.
Starting with a suite of samples from the same region, the following steps may be followed to interpret the spectral mixtures in the suite:
1. Identify the spectrally dominant mineral, using the same procedure as applied for pure minerals. 2. By comparison with the library spectrum, identify the wavelength positions of any additional
absorption features and/or approximate positions of inflexions,
3. Note absorptions that appear deeper relative to the other absorptions, when compared to the relative depths represented in the library spectrum.
4. Refer to the other “end-member” spectra, in samples above or below your unknown, to identify the minerals that may be causing these additional features.
Problematic Minerals in Mixtures
Opaque minerals, such as magnetite and sulphides, have an adverse effect on the spectrum of a sample if:
- Finely disseminated in the sample; - Present in proportions >5-10%.
The effect on the spectrum is to: - Significantly lower the reflectance;
- Weaken the spectral absorption features of other minerals in the sample.
Carbonaceous material in rocks such as black shales will have a similar effect on their spectral responses as that caused by finely disseminated opaque minerals.
Exercise 4: Mixed minerals, crystallinity and compositional variations.On the next page you have been provided with a series of spectra from samples from a simulated drill hole that intersects a number of alteration zones.
1. Identify the main mineral zones that are evident down hole and identify the dominant mineral in each zone.
... ...
2. Identify which spectra are mixed spectra, and what the components are in the mixtures. ... ...
3. Which mineral zone displays variation in mineral crystallinity and for what minerals? ... ...
4. Which minerals display variations in composition within any of the mineral zones? ...
S
ESSION
3
:
S
AMPLE
M
EASUREMENT
I
SSUES
IntroductionThe type of sample used for analysis can influence the quality of spectral data acquired and also how the data are interpreted. The following notes provide information on the data characteristics, advantages and problems associated with common sample types.
There are some basic rules that should be followed when analysing any sample. 1 Ensure that the sample is dry.
2 Ensure that the surface of the sample is clean – i.e. not dusty or coated with dirt or lichen or dry vegetation.
2 Never measure through plastic (bags or containers). Always direct on sample surface or through a thin (<=1mm) Petri dish.
3 Do not measure sample surfaces that have been coated with clear lacquers or impregnated with resin.
4 Ensure the spectrometer’s sample window is clean before each measurement.
5 If measuring through a Petri dish ensure that the glass is not scratched, this can occur after measuring a large number of samples (~700 depending on the mineralogy of the pulp samples). The dishes need to be replaced if too scratched, or the spectra will be degraded.
The following notes discuss the advantages and disadvantages of spectral analysis of different sample types.
Soil Samples ADVANTAGES
In regions where little or no bedrock is exposed soil samples can provide valuable spectral information that can be used to map lithology and alteration.
Spectral data from soil samples can be helpful in interpreting associated geochemical data sets.
Spectral analysis of soil samples is often a very cost effective and rapid method of producing surface mineral/alteration maps.
DISADVANTAGES & TIPS
Spectra from soil samples often contain higher noise signals and weaker mineral signatures than other sample types (e.g. rock chip samples).
In areas where the bedrock has been subjected to strong or intense weathering, soil samples can contain few if any unweathered minerals and can be dominated by kaolinites or smectites.
Thin Sections ADVANTAGES
Spectral analysis may provide useful information not available from petrographic studies alone (i.e. on mineral composition and crystallinity).
DISADVANTAGES & TIPS
Thin sections themselves are not suitable for spectral analysis because they are too thin and (more importantly) the resins and glues used to make them produce their own distinctive absorption features which results in very complex spectra. The best spectra can be obtained from the thin section off cuts.
Spectral absorption features associated with glue and resins on the sample surface or impregnated through the sample may be present when analysing offcuts or stubs.
Some rock saws use kerosene or similar hydrocarbon based cutting fluids. These fluids can produce absorption features if present in the sample.
Polished sections are often not suitable for spectral analysis due to their high sulphide content.
Outcrop/soil Rock Chips ADVANTAGES
Rock chip samples generally produce strong spectral signatures compared with soils.
Rock chip samples are usually a cost effective and rapid way of producing mineral/alteration maps from their spectral data.
DISADVANTAGES & TIPS
Rock chip samples may not be able to provide adequate spatial coverage across the project area and so in this sense soil samples have a clear advantage.
As with all near surface samples, rock chip samples will have suffered some degree of weathering.
Rock chip samples are inhomogeneous (unlike geochemical pulps) and so more than one measurement may be required to characterise the sample/location.
RAB/RC Drill Cuttings ADVANTAGES
RAB/RC Drill cuttings are quite homogenous and so the spectral signature of these samples represents an average signature for the sampled interval.
Spectral analysis results can be compared directly with the equivalent geochemical data.
DISADVANTAGES & TIPS
The spectral response will be less responsive to subtle alteration indicators the longer/wider the sampled interval. This is a potential problem where alteration related minerals are not pervasive but may be restricted to veins, vein selvages or fracture surfaces. The concept of dilution applies to spectral analysis in the same way as it does to geochemical analysis.
Samples may be wet when drilled. If possible, one way to dry the samples is to leave them open for a few days prior to measuring them (provided that the climate is dry and not excessively humid). Don’t wait until the samples have to be measured before discovering that they need drying.
Under wet drilling conditions a significant proportion of the fines may be washed out of the sample in certain rock types. It is sometimes useful to test this by drying and then analysing the fines captured from expelled water.
Drill Core
ADVANTAGES
Highly specific measurements of matrix material, clasts, veins, vein selvages and fracture surfaces are possible with this sample type.
DISADVANTAGES & TIPS
Analysis of core often takes more time than equivalent RC samples because notes need to be taken describing where each measurement is made and of what feature.
It is generally more difficult and time consuming to make accurate comparisons of specific core measurements and geochemical data. This is because geochemical assays are usually carried out over selected intervals of core.
Some contamination can occur on the surfaces of core samples associated with drilling muds or spilt lubricating oils, hydraulic fluids or diesel fuel.
Geochemical Pulp Samples ADVANTAGES
Pulped samples are homogenous and so their spectral signature represents an average signature for the sampled interval.
Pulped or crushed sample types (eg. RC/RAB cuttings and Geochemical pulp samples) can liberate mineral species that may be preferentially developed along fractures, veins or as spotting in the sample. This can enhance alteration signatures where these minerals are spectrally responsive (e.g. clays, micas etc.).
Spectral analysis results can be compared directly with the equivalent geochemical data.
In core samples, the presence of water absorption features associated with fluid inclusions can easily be confused with adsorbed water in smectites and illite/smectites. This factor can make crystallinity and smectite proportion determinations difficult. In pulp samples however, free water in fluid inclusions is largely removed allowing more accurate determinations of smectite proportion and illite crystallinity to be carried out. It is rare that fluid inclusions are small enough to be preserved in pulp samples.
DISADVANTAGES & TIPS
Some laboratories may over grind or over heat the pulps and degrade the mineral responses, this is not common and dependent on the laboratory.
The spectral response will be less responsive to subtle alteration indicators the wider the sampled interval represented by the pulp sample. This is a potential problem where alteration related minerals are not pervasive but are restricted to veins, vein selvages or fracture surfaces. The concept of dilution applies to spectral analysis in the same way as it does to geochemical analysis.
Although producing generally brighter spectra than equivalent core or cuttings, the spectra of pulp samples often tend to display weaker absorption features because of increased scatter at the surface of the powder.
Where a sample contains sulphides or magnetite their liberation through crushing can cause significant decreases in data quality.
The best sample to use is a split from the first jaw crush stage in the pulp preparation where the samples are fine chips, before they are actually powdered.
Important
In all cases, only compare the spectral variations of similar sample types (e.g. drill core with drill core, pulps with pulps, outcrop with outcrop, float with float and soil with soil). This will ensure consistency in your interpretations and in the output data.
Influence of Water
It is important to consider the influence of water because it occurs in two forms, as: - Free water (in wet samples);
- Bound water.
Recognising the Presence of Water Water absorptions will occur in the spectra of:
- Wet samples;
- Samples with water-bearing fluid inclusions (e.g. in silica or carbonate); - Minerals with structural water (e.g. smectite and gypsum).
Water has two diagnostic absorptions: one near 1400nm and the other near 1900nm.
These absorptions are typically broad and asymmetric, when compared to the sharper and more symmetrical OH absorptions. In addition, water in wet samples has a distinctive broad rounded minimum in the 1900nm absorption band compared with the sharper minimum of water bound within a mineral lattice.
Wet Samples
It is recommended that all samples be dry before spectral analysis, as the water bands will tend to swamp any diagnostic absorptions in the 1350-2000nm spectral region. This may be significant if attempting to determine proportions of smectite in a sample, for example.
The “free” water bands of wet samples are easily distinguished from “bound” water absorptions (e.g. in smectites) as they typically display very rounded and broad minima
Drying Wet Samples
There are several methods of drying the samples in the field or site: - In the sun;
- Drying cabinet (~60 degrees C overnight); - Quickly over a small gas burner;
- Microwave (
The last two options are not generally recommended as a rule because of the high temperatures that can arise, but are useful if there is no alternative.
Examples of the 3 Different Types of Water Signature
Noise in Spectra
It is important to recognise noise as noise can produce artefacts which could be misinterpreted as absorption features.
Recognising Noise
Noise can be recognised as narrow (<4nm), weak, sharp features.
Often noise can be observed between 2400-2500nm where the reflectance signal is weakest.
Noise is typically observed in dark low reflectance samples.
Another type of noise typically occurs if fluorescent lights are on during measurement. This results in periodic noise throughout the spectrum.
Most importantly, re-measure sample and observe if noise features have disappeared or have changed wavelengths.
Overcoming Noise
Use higher enhancement/integration modes, and re-measure the sample to obtain a better spectrum. Alternatively try a different sample type, or part of sample, if available.
ASD/Terraspec spectra can often be very noisy, especially at longer wavelengths. If this is the case, then increase the number of averages and the time taken to measure your sample. In addition, to improve ASD spectra, warm up light source before measuring for at least an hour.
Note that some samples that contain opaques or carbonaceous material will always have noisy spectra.
Other measurement and sample artefacts Spectral can also be influenced by
1. plastic features, recommended that samples are not measured through plastic bags 2. resin features: clean surfaces only, not impregnated.
3. oil spill: oil spill on sample surfaces or mixed into sample will cause spectral features.
Artefact features
Artefact features are additional features in a spectrum that are not related to the sample mineralogy. The presence of these will lead to misidentification.
The most common artefact features are due to impurities such as kerogen oil (from drill rig, diesel spill or other sources), dry vegetation or wood, resin impregnations, or plastic.
Other artefacts can be introduced into the spectrum by an unusual overall curvature to the spectrum, which may be related to dark samples or poor illumination.
Example of kerogen/oil spectra.
Particle Size Effects
It is important to consider the influence of particle size as this can influence the brightness of the spectrum and the intensity of absorption features.
Powders versus Rocks
Mineral/rock powders typically have higher reflectance (i.e. are brighter) in the SWIR than their equivalent solid rock samples.
In addition, the absorption features in the reflectance spectra of powders are typically weaker than in rock spectra. This is due to a decrease in the depth of penetration of the radiation as a result of greater surface scattering.
Significance of Particle Size Effects
The influence of particle size on spectral response can be significant when comparing drill core and pulp spectra, soil and outcrop spectra, and weathered surfaces and fresh surfaces.
S
ESSION
4
:
D
ATA
A
NALYSIS
S
OFTWARE
TSG
P
RO
(T
HE
S
PECTRAL
G
EOLOGIST
)
Introduction
The purpose of this session is to familiarise you with the routine analysis techniques that you are likely to use when analysing spectral data. It is not intended to serve as a comprehensive description of the functions available in TSG. A comprehensive user manual of all the TSG functions is embedded in TSG for users who wish to look more in depth into the TSG functions.
TSG is used here as a vehicle to convey the analysis concepts discussed in the workshop. Getting Started
In this session we will examine a range of analysis techniques using TSG and spectral data that you are already familiar with from Exercise 4.
1) Start TSG
2) Open the file “Exercise 4 TSG Pro”. The Summary Screen
When you first open a dataset TSG presents summary information on the spectral data in the form of an Assemblage Histogram and a Feature Frequency Chart.
Overview or Spatial Plots
The Overview Plot is driven by the spectral analysis algorithm called The Spectral Assistant. The histogram is designed to give you an overview of the mineral species or classes likely to be present in your data set. It can be a valuable indication of the assemblages or suites of minerals (eg. phyllic, propylitic, argillic, felsic, mafic, ultramafic, etc.) present, before launching into the detail analysis of a large number of individual spectra.
The TSA SWIR or VNIR (vis-NIR)) results can be shown in the Overview Plot. In addition, you can use the “Level” drop down list to control how the results of TSA are reported so that either specific mineral names or general mineral classes are reported. For example dickite can be reported either as dickite (mineral) or Kaolinite Group (class).
The spatial plot will show a plot of the TSA results as they are represented down hole and can be a quick summary of the broad mineral trends down hole as detected by TSA.
Colours in the Overview Plot
Spectrum Screen
This screen is used to display and examine single spectra in detail. You can also use this screen to compare (overlay) spectra from the system library or different parts of the current data set using the built in reference libraries for the SWIR and vis-NIR, and also the “2nd” options. It is also possible to overlay other TSG files that have been attached as “Aux” files (using the “aux” option and “attach aux”). Spectra can be selected from the file list and displayed using any of the available spectral layers eg. Hull Quotient, reflectance etc.
Because all of the main screens are linked, selecting a different part of the data set in the Log or Stack Screens will immediately update the active spectrum in the Spectrum Screen. All of the information displayed in the Log Screen that is associated with the current spectrum (eg. geochemistry, coordinate data, spectral parameters etc.) can also be displayed in the Spectrum Screen.
With the exception of the Smooth option and the Reset button all of these on-screen controls can be accessed from the pop-up menu and the Spectrum options under the View Menu.
The automated TSA results are shown at the bottom right of the spectrum, together with a verbal expression of the error levels (i.e “very likely”, “probable”, “possible” or “I’m guessing here”). These qualitative expressions are based on the quantitative calculation of the errors of the fit in the TSA results, the quantitative values can be displayed and viewed in the Log Screen.
Stack Screen
The Stack Screen is used to display numbers of ‘stacked’ spectra in either line or image/colour slice format. It allows detailed analysis of portions of the data set while still providing access to the Log Screen data. In particular it is useful for identifying repeating patterns or blocks of similar spectra as well as gradually changing trends through a dataset. Selecting a spectrum in the Stack Screen will update the Spectrum Screen while zooming to a particular portion of the data set will also update the Log Screen view.
With the exception of the 'Gap' option all of the Stack Screen on-screen controls can be accessed via the View Menu and the pop-up menu.
Log Screen
This screen is one of the most powerful screens in TSG. It allows other data sets associated with a suite of spectra to be integrated and analysed alongside your spectral data. The Log screen is designed to display spectral data, geochemical data, comment fields and any other types of numerical and text fields relevant to the data set in a format which most exploration geoscientists will probably find familiar. The concept is based on a drill hole log but is equally effective when dealing with surface traverse or geochemical sample grid data. It is possible to display very large amounts of data using the Log Screen. Users can display up to 32 Log Columns and can remove or add them at will. Users can adjust their width, move their screen position, scale and change the colours of the displays within them. Text, spectral image logs (colour slices), bar graphs, line graphs can all be displayed in the Log Columns. Most importantly, the information displayed in the Log Screen can be displayed for the active spectrum in the Scatter, Spectrum and Stack Screens by using the icon or the Logs status bar option on the pop up menu when in any of these screens.
The Pop Up Context Menus
The pop up menu is activated from the right mouse button. Almost all of the display, analysis and output functions available in the different TSG Screens can be controlled using the pop up menu.
Spectral Items and Scalar Items
The Log Screen allows you to display two different types of data. These are referred to as Spectral Items and Scalar Items.
Spectral Items relate to your spectral data. A number of layers or formats of spectral data can be displayed including Hull Quotient, Reflectance, Normalised Hull Quotient and Normalised Reflectance spectral data. (see the View menu or the list at the bottom of the pop up menu )
Scalar Items are numerical or text values related to the spectral data. The most commonly used types of scalar data are assay data, coordinate data, numerical rock codes, geophysical data and spectral parameter data (derived from the spectral data).
Creating New Scalar Data
TSG can store over 400 different scalars in a data file. These scalars may be derived from external data sources, spectral data (spectral parameters) or arithmetic calculations involving combinations of existing scalars.
New scalars can be derived from a number of different sources including:
1. Import scalar from .CSV (Comma Separated Value ASCII format) file or from the clipboard;
2. From the results of Arithmetic Expressions using existing scalars (often used for geochemistry and spectral parameter ratios);
3. Construction from spectral data (i.e. spectral parameters).
Importing Scalar Data Exercise (optional)
This method of creating a new scalar dataset is usually used to import assay and coordinate data. We will start by creating a .CSV file and entering some imaginary assay data.
1) Using the 'Export' function from either the pop up menu or the 'Edit' menu select 'to csv (scalars)' or 'to csv (params)'. Give the new .CSV file a name and write it to the TSG data folder.
2) Using a spread sheet program (e.g. MS Excel) open this file, create a new column of imaginary assay data, (make sure you give the new column a heading) save this file in .CSV format overwriting the original, and then close the file (if you don't close the file TSG will be denied permission to access it).
3) Activate the first of the blank columns in your Log Screen (left mouse button). Now select ‘Import' from the 'File' menu, which opens the new scalar dialogue.
4) Give the new scalar a name or leave this option blank if you want to use the data column heading from the .CSV file as the scalar name.
Scatter Screen
Alongside the Log screen the Scatter Plot screen forms the heart of TSG's data analysis capabilities. The Scatter Plot screen allows you to assign any scalar values to X or Y axes and a third scalar to the colour scale (Z) in up to 12 simultaneously displayed graphs. Graph windows can also display frequency histograms for any selected scalar.
Many TSG datasets are composed of a variety of data types including spectral data, geochemistry and coordinate data. The Scatter plot screen is designed to allow you to display and analyse all of these data types as an integrated data set. In addition, each plot window in the Scatter screen is linked so that the selected data point/sample/spectrum in one screen is highlighted in all the other screens currently on display.
By assigning coordinate data to X and Y axes the Scatter Plot screen can be used to display sample spatial distribution in plan or section. This allows the spatial distribution of geochemistry and alteration mineralogy to be simultaneously investigated. This functionality combined with the Floater also allows the simultaneous analysis of hundreds or even thousands of spectra while at the same time allowing you to keep track of individual sample's spectral or mineralogical characteristics.
Floater window
The floating spectrum window (or Floater) is one of TSG most useful features. It is a resizable floating spectrum display window that allows the user to view the spectrum of the 'active' or 'selected' sample from the Scatter, Spectrum, Log or Stack screens. This means that while examining scatter plots containing many hundreds of samples showing geochemistry and other data you can still keep track of the spectral characteristics of each sample. The Floater is automatically updated as your cursor moves over and selects new data points or spectra in the Scatter, Spectrum, Log and Stack screens
The floater window also has additional functions and can be operated on in different modes other than spectrum. These include TSA (The Spectral Assistant) mode, Aux Match mode linescan and picture mode.
The TSA mode allows the user to observe the fit between the TSA calculated match and the unknown spectrum.
Aux match mode allows the user to match the unknown spectrum to a user defined spectral library (another TSG file). This user library is termed the “auxiliary” file, and can be attached to the current TSG file for overlay and 1:1 comparison. The aux match mode gives the goodness of fit between a library spectrum and an unknown spectrum.
Linescan mode allows you to view the linescan image data for the active spectrum in HyLogging data sets – which will show the exact piece of core that was measured.
Picture mode allows the user to display a .bip or .jpg image of a sample or core tray. Both The Spectral Assistant and Aux match methods are discussed in more detail in the next section.
S
ESSION
5
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A
PPROACHES TO SPECTRAL ANALYSIS
Introduction
There are three main approaches to spectral analysis, these are:
Manual Interpretation
Mineral identification software o Automated
o User defined training libraries
Spectral parameters / digital mineralogy
Each approach is appropriate to different stages of an analysis process and dependent on the number of spectra/samples in a project.
Manual Interpretation
This approach describes the method of individually interpreting each spectrum. This approach requires an experienced operator and can recognise between 1-5 minerals in each spectrum. It is important at the early stages of a project, and for relatively small data sets of <500 spectra, but is generally slow and therefore expensive for large data sets. In addition, the results are often written descriptions, and subtle variations in mineral species such as crystallinity and composition are often missed and it is difficult to track subtle variations spatially
Automatic Mineral Identification
In this approach automated algorithms are used to interpret the spectral data. These algorithms are either dependent on an inbuilt training library (such as The Spectral Assistant) or on a user defined spectral library. A large number of spectra can be processed very fast using this approach, and the data are output in digital form that are easily compared with other data sets in TSG.
However, it is important to note that the method is very dependent on the quality of the training library being used and whether the spectra in the library are representative of the spectra in a project area. As automated methods are largely based on probabilities, the reported interpretations must be considered only to be the most likely results but not necessarily the only, or even the correct, results. This issue is less of a problem when dealing with very large data sets as the errors become part of a certain percentage of noise in the results, but becomes more significant with small data sets.
There is also a danger of users becoming reliant on the automated output without cross checking the results to see if they are happy with the output. It is vital for users to bring in geological knowledge to the results, and not treat the data blindly.
The Spectral Assistant
The Spectral Assistant (TSA) is an algorithm built into TSG for automated spectral unmixing and as an aid to mineralogical identification. TSA uses its training library either to match the spectrum against a single mineral OR to create a simulated mixture of two minerals that most closely resembles the input spectrum (whichever case has the lowest errors). TSA runs automatically in the background when creating a new TSG dataset, and the results are written into the TSG file as Protected System Scalars (i.e. they can be viewed and used in TSG but cannot be directly modified). These scalars are shown in the figure below.
TSA has been trained using a Training Library of over 500 samples, representing 45 “pure” minerals and 11 different non-mineral artefact spectra. The samples have been collected from many sites around the world, in an attempt to represent the diversity of samples of the same mineral.
However, before copying a table of TSA results straight into a final report, you should understand that the word "Assistant" was not included in the method's name just because it has a nice ring to it. It is a reminder that even TSA v6.1, which is a state-of-the-art automated spectral unmixing technique, is not perfect. TSA results should be authenticated by a human - ideally an expert. At least results should be checked against the reference spectra provided in the TSG reference libraries, to ensure consistency.
TSA Spectral Weights
A singleton (one mineral) result always has a weight of 1, meaning that the result accounts for the matched mineral and nothing else. In this case, the single mineral is reported in TSA Mineral 1, with the TSA Mineral 2 slot reporting NULL (as there is no 2nd mineral).
Amongst the best fitting mixtures of 2 minerals, the 2 weights are constrained to be positive and to sum to 1, and so can be interpreted as relative spectral proportions.
IMPORTANT NOTE: The proportions reported by TSA should not be interpreted literally as representing the weight or volume proportions of the components of the mixtures. This is because the fitting procedure is based on some idealised assumptions, the most fundamental of which is that there is log-linear mixing of pure spectra whose absorption features have standardised intensities.
In addition, it should be noted that although TSA has been trained to recognise most of the more common minerals, these are still based on a limited class of minerals that generate spectral responses in the SWIR (in the form of distinctive absorption features). Thus unexpected minerals and those that are not "SWIR-active", such as quartz and feldspar, will not contribute to the estimated total bulk composition of the sample. For instance, a sample containing 60% quartz, 20% standardised kaolinite and 20% standardised illite with a good signal-to-noise ratio is likely to be identified by TSA as a spectral mixture of about 50% kaolinite and 50% illite.
TSA Error
TSA reports a goodness-of-fit (or error) measure for each match, termed the Standardised Residual Sum of Squares, or SRSS. This value is saved as the TSA Error.
In real-world samples, it is unlikely that there will ever be a zero-error match. The samples in TSA's training library are not perfectly "pure" - they are real-world samples themselves, which vary. Even if all of the training samples were pure, a pure kaolinite sample, say, would probably not get a zero-error fit on account of the natural crystal variations in what we loosely call "kaolinite", and on account of optical effects (like scattering) that are impossible to control with a field spectrometer.
Therefore there is a range of SRSS scores over which TSA results should be taken seriously. In general, a result with an SRSS (TSA Error) score of 1000 or less should be considered viable. However, results with large SRSS scores, even around 2000, may still be worth a look, but not without critical scrutiny.
Possibility of Erroneous results
As indicated previously, before copying a table of TSA results straight into a final report, you should take care to authenticate TSA’s results and remember that it is wise to understand that these are the most “likely” results and are possibly not the “only” results. Note that TSG provides the means to add your own interpretations and comments into the sample headers, and also to build your own class scalars that can be based on TSA results and your own interpretation.
Under some circumstances, including issues quite beyond the algorithm’s control, TSA can produce erroneous results. Known causes may include any of the following:
1. The sample contains one or more minerals or mineral variants that are not in TSA's training library (see TSA Training Library).
2. The sample's composition is too complex for TSA to deal with as the spectrum comprises a mix of more than two minerals.
3. The sample's spectrum is too poor, either too dark, noisy or "aspectral" (featureless). (Note, however, that TSA will flag many of these cases.)
4. The sample contains a spectrally ambiguous component. (This problem may occur in conjunction with one or more of the above problems.) Some minerals have very similar spectral signatures - especially minerals from the same "family".
Examples include:
a) Illite and muscovite spectra (same family) differ only in the strengths of their water features; b) The features that distinguish epidote from chlorite (different families) may be masked if a strong water feature is also present.
5. The sample's spectrum was not measured correctly, due to a spectrometer miscalibration. Miscalibration of the instrument may result in a slight shifting of important absorption features. TSA is particularly sensitive to such shifts because it is designed to discriminate between certain minerals whose main spectral difference is a slightly different location of one important absorption feature (e.g. Muscovite from phengite).
User defined Spectral Libraries and Custom Libraries
A user defined or “Custom Library” can be simply other TSG file attached to a current file of unknown spectra. You can select and attach any other TSG file you wish to your current data file. In TSG Custom Libraries are termed Auxiliary (or Aux) libraries.
The idea behind Custom or Aux libraries is that it allows the building of characterised datasets that relate to specific projects, alteration styles or geologic environments. Other data files can also be used as custom libraries simply to compare the mineralogical similarities between two data sets, these might be two drill holes from the same project or they could be from new targets suspected to be similar to known mineralisation.
The big advantage of Custom Libraries is that they can comprise spectra of mixtures of spectra and actual examples of the spectra from a project area rather than pure mineral spectra from museum or private collections. This is in contrast with TSA, which has a library of single mineral spectra.
For best results Custom libraries need to be created by a spectral expert, as they need to be fully representative of the spectral variations in the project area or deposit style.
A Custom Library is usually a project specific TSG file built as a result of a detailed pilot study of the spectral characteristics of a project area. This type of Custom Library often comprises spectra that have been interpreted in detail and are representative of the full spectral variation in a project area. Often such a library is a customised version of a more general library, such as the GESSL ("Geological Environment Specific Spectral Library") library.
Deposit Specific Spectral Libraries
Only contain
spectra from
project area
Spectra are
representative of
mineral assemblages in
project area.
Mixed mineral spectra
Output of results
in company
terminology.
Deposit specific
classifications
can be used.
A geological environment specific spectral library (GESSL) is one that contains spectra representative of the spectral variations specific to a particular geological setting, such as porphyry and epithermal environments.
Using Aux Files as Custom Libraries
To attach an auxiliary TSG file (Custom Library) select Attach aux… from the Spectrum Screen File menu. A file selection window will be displayed allowing you to select the TSG file you want to be attached as the auxiliary (Custom Library) file.
Once an Aux file has been attached the spectra in this file can be compared with the spectra in your current TSG file in a number of ways. One way is to interactively overlay the spectra for visual comparison. This method allows you to visually compare two spectra for spectral similarities and differences. To overlay Aux spectra click on the Aux control at the top of the spectrum screen.
However, although useful, the overlay method is slow when dealing with a large data set. A more automated approach is to use the Aux Match function in the floater screen.
The Aux Match function allows users to automatically search and match the spectra in the Aux file that are similar to those in their TSG file. It also allows users to compare the matching results and to save the results of the search match as TSG scalars. The searching, comparing and saving functions available with Custom Libraries are all accessed using the Floater window. So, the first step in using custom libraries is to open the
Floater window by clicking on the Floater Icon .
Then select Aux Match mode from the Floater Mode menu. When the Aux Match mode is enabled the Floater window will display an Auxiliary file sample list at the left hand side. This file list will be sorted in order of the best (top) to the worst (bottom) match with the selected or active spectrum in your dataset. The colour scale shown with the file list gives an indication of how good the match with the spectrum is, hot colours indicate good matches, cool colours indicate poorer matches. A colour scale is also shown on the right hand side of the Floater Screen. To test and improve the match and output the results, users have a number of options:
Finally to output the results of the automatic Aux Matching users need to Update Aux Matches. This option is available in the Mode menu in the Floater screen and will cause TSG to write the best current match results for every sample in the dataset to the CustSample and CustScore protected system scalars, based on the
