SWIR Workshop Manual

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Table of Contents

TABLE OF CONTENTS Disclaimer: ............................................................................................................................................... i SESSION 1: INTRODUCTION TO SPECTRAL GEOLOGY .................................................................... 0      

     

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INTRODUCTION ..................................................................................................................................... 0 FIELD SPECTROMETERS ......................................................................................................................... 0 AUTOMATED MEASUREMENT : CORE LOGGING AND HYLOGGING ............................................................ 0 SHORT WAVELENGTH INFRARED (SWIR) SPECTRAL REGION .................................................................. 1 SWIR INFRARED SPECTROMETRY ......................................................................................................... 1 PATTERN RECOGNITION ........................................................................................................................ 2 EXERCISE 1: PATTERN RECOGNITION ....................................................................................................... 3 ABSORPTION FEATURES RELEVANT TO THE SWIR ................................................................................. 4 SUITABLE MINERAL GROUPS FOR SWIR ANALYSIS ............................................................................... 4 UNSUITABLE MINERAL GROUPS FOR SWIR ANALYSIS ........................................................................... 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 A STRUCTURED 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 EXERCISE 4(CONT’): SPECTRA FROM A SERIES OF SAMPLES FROM A SIMULATED DRILL HOLE ....................... 27

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Table of Contents

SESSION 3: SAMPLE MEASUREMENT ISSUES ................................................................................... 29        

   

INTRODUCTION ................................................................................................................................... 29 SOIL SAMPLES .................................................................................................................................... 29 THIN SECTIONS ................................................................................................................................... 30 OUTCROP/SOIL ROCK CHIPS ................................................................................................................ 30 RAB/RC DRILL 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 USEFUL REFERENCES ............................................................................................................................ 57

ii

Session 1: Introduction to Spectral Geology

SESSION 1: INTRODUCTION TO SPECTRAL GEOLOGY  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. The HyLogging system feeds core in core trays under the spectrometer using an automated table and a step and measure system. The output data are therefore systematic readings of the core at millimetre spacing, allowing very detailed down hole mineralogy to be analysed. In addition, high resolution photography of the core is also collected along with the spectral readings. All the photography is tied to the spectral data, so that the exact piece of core can be viewed alongside the corresponding spectral data.

0

Session 1: Introduction to Spectral Geology

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.

ASD HyLog (+ new PIMA)

PIMA and ASD

 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. These absorptions are represented in the reflectance spectrum as minima below the baseline of the spectrum.

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Session 1: Introduction to Spectral Geology

 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.

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Session 1: Introduction to Spectral Geology



Exercise 1: Pattern recognition Below 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.

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Session 1: Introduction to Spectral Geology

 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 CO 3 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; - Clay absorptions, due to weathering/alteration of felspathic components in the sample.

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Session 1: Introduction to Spectral Geology

 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

Hematite and goethite ferric absorption features

Red Peak

Intense charge transfer

He ~860nm

Go ~930nm

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Session 1: Introduction to Spectral Geology

Fe Carbonate ferrous absorptions (illite+Fe carbonate assemblage)

OPX

CPX

Pyroxene ferrous absorptions

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Session 1: Introduction to Spectral Geology

 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 CO 3 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 bands Allocate the spectra from Exercise 1 to their compositional groups using the Spectral Band Figure (see next page).

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Session 1: Introduction to Spectral Geology

1300 1400

1500

Water and OH

1600

(Water = single broad absorption +/- shoulders) (OH = can be multiple sharp absorptions of varying intensities)

1700

OH

1800 1900 2000 Wavelength (nanometres)

(eg sulphates +kaolinite clays + diaspore)

Water

(Single broad asymmetric absorption +/-shoulders)

2100

2200

2300

AlOH FeOH

2365

2296 2306

2220 2230

2160

2040

1860 1880

1720

1550

1350

Note: If the deepest absorption is in the AlOH waveband, absorptions at these wavelengths will include SECONDARY AlOH absorptions of that mineral.

2400 2500

CO3 and/or MgOH (if deepest)

(Otherwise check in AlOH band as it may be a 2ndy AlOH feature)

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Session 1: Introduction to Spectral Geology

 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: 2+

- Ferrous (Fe ) 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.

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Session 1: Introduction to Spectral Geology

The figure below illustrates the method of hull quotient correction.

Hull Quotient Correction

class:biotite/chlorite mineral:Fe2+

Raw Reflectance

A

Hull Line

0.2

Reflectance

B

Hull Correction = A/B

Hull Quotient 600

900

1200

1500

1800

2100

2400

Wavelength in nm

350

1300

2500

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Session 2: A Structured Approach to Spectral Analysis

SESSION 2: A STRUCTURED APPROACH TO SPECTRAL ANALYSIS  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.

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Session 2: A Structured Approach to Spectral Analysis



Exercise 3: Spectral interpretation Interpret the spectra from Exercises 1 and 2 using the search index in the GMEX Spectral Library and the structured approach to spectral interpretation.

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Session 2: A Structured Approach to Spectral Analysis

 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.

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Session 2: A Structured Approach to Spectral Analysis

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.

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Session 2: A Structured Approach to Spectral Analysis

Crystallinity and its Effect on the Spectral Responses of illite

AlOH band Water band

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.

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Session 2: A Structured Approach to Spectral Analysis

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, paragonitic mica in dark blue and phengitic mica in red, and muscovitic compositions mostly in green.

this will colour the

AlOH wavelength shift

High Al

Paragonitic

Muscovitic

Low Al Mg-Fe substitution

Phengitic

Examples of sericite composition applications:  Distinguishing secondary and primary micas  Mapping proximity to mineralisation.

1300

1400

1500

1600

1700

1800 1900 2000 2100 Wavelength (nanometres)

2200

2300

2400

2500

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Session 2: A Structured Approach to Spectral Analysis

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.

Examples of chlorite composition applications: Mapping proximity to mineralisation in VMS and other systems. Distinguishing secondary and primary chlorites

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.

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Session 2: A Structured Approach to Spectral Analysis

2262 2260 2

Wavelength (nm)

2258

R = 0.8619

2256 2254 2252 2250 2248 2246 2244 2242 0

0.2

0.4

0.6

0.8

1

Mg Number

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.

2365 2360

2

R = 0.85

Wavelen gth (nm)

2355 2350 2345 2340 2335 2330 2325 2320 0

0.2

0.4

Fe-rich

0.6

0.8

1 Mg-rich

Mg Number

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Session 2: A Structured Approach to Spectral Analysis

Influence of Octahedral Cation on the Spectra of Smectites

Example of smectite composition applications:

Delineating lithologies in deeply weathered terrain.

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Session 2: A Structured Approach to Spectral Analysis

Composition and its Effect on the Spectral Responses of Carbonates

Example of carbonate composition applications:

Zoning in alteration systems. Industrial minerals: differentiating different carbonates.

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Session 2: A Structured Approach to Spectral Analysis

1 Dolomite

2 Siderite 3 Ankerite

4 Calcite 5 Mn-Carbonate(2360nm) (+ Sericite)

Note: Siderite –has variable wavelengths because of variable substitution i.e. by Mg and/or Mn

6 Reflectance spectrum of Siderite (Spectrum 2)

7 Reflectance spectrum of Ankerite (Spectrum 3) Wavelength (nanometres)

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Session 2: A Structured Approach to Spectral Analysis

Other minerals to display compositional variations include: Biotite: similar wavelength variations to those observed in chlorite, Mg biotites have low values for their FeOH (~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.

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Session 2: A Structured Approach to Spectral Analysis

 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? ........................................................................................................................................

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Session 2: A Structured Approach to Spectral Analysis



Exercise 4(cont’): Spectra from a series of samples from a simulated drill hole

27

Session 3: Sample Measurement Issues

SESSION 3: SAMPLE MEASUREMENT ISSUES  Introduction The 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 (