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NEWSLETTER

SEG SEG NEWSLETTER HSOCIETY OF ECONOMIC GEOLOGISTSHOCTOBER 1999 NUMBER 39

Alteration Mapping in Exploration:Application of Short-Wave Infrared(SWIR) Spectroscopy

AUDREY J. ROBITAILLE

PETRASCIENCE CONSULTANTS INC.

3995 W. 24TH AVENUE

VANCOUVER, B.C. • CANADA V6S 1M1

PHOEBE L. HAUFF

SPECTRAL INTERNATIONAL INC.

P.O. BOX 1027

ARVADA, COLORADO 80001

ANNE J. B. THOMPSON (SEG 1990)

PETRASCIENCE CONSULTANTS INC.

3995 W. 24TH AVENUE

VANCOUVER, B.C. • CANADA V6S 1M1

EMAIL, [email protected]

ABSTRACT

Alt er at io n mi ner al as se mb la ge s are im por ta nt to the

understanding of and exploration for hydrothermal ore deposits.

Conventional mapping tools may not identify fine-grained minerals

or define important compositional variations. Field portable short- wave infrared (SWIR) spectrometers solve some of these problems

and provide a valuable tool for evaluating the distribution of

alteration assemblages. Spectrometers such as the PIMA-II allow

rapid identification of minerals and mineral-specific variations at a

field base. Mineral assemblages, integrated with other exploration

data, are then used to target drill holes and guide regional

exploration programs. Data collection must be systematically

organized and carried out by a trained operator. Analysis of data

sets requires the use of spectral reference libraries from different

geological environments and may be aided in some cases by

computer data processing packages. Integration of results with field

observations, petrography, and X-ray diffraction analysis is necessary

for complete evaluation. The PIMA (portable infrared mineralanalyzer) has been used successfully in the high-sulfidation

epithermal, low-sulfidation epithermal, volcanogenic massive sulfide

(VMS) and intrusion-related environments. Case studies from these

systems demonstrate the ability to rapidly acquire and process SWIR

data and produce drill logs and maps. The resulting information is

critical for targeting.

INTRODUCTION

Field portable SWIR spectrometers are becoming increasingly important to exploration. Spectrometers typically are employed todetermine the mineralogy of altered rocks and hence assist inclassifying ore systems, identifying alteration patterns, and locating

ore. In addition to its early use in remote sensing, development othe PIMA-II in 1991 allowed direct use of SWIR on rocks, greatlyenhancing i ts practical application to exploration. SWIRspectroscopy detects minerals such as phyllosilicates, clayscarbonates, and selected sulfates and is also sensitive to variations in

individual mineral species.SWIR field spectrometers are used in numerous deposi

environments, including high- and low-sulfidation epithermalporphyry, mesothermal, sediment-hosted gold and copper, uranium

VMS, and kimberli te deposit s (Table 1, page 16). In additionspectrometers aid regolith mapping, both for determination obedrock composition and for differentiation of residual andtransported regoli th. Publications on the results of SWIRspectroscopy are sparse, reflecting the confidential nature of moscompanies’ programs and the lack of academic work applied to fieldmapping. A selection of recent papers and abstracts, howeverhighlight the work that is currently underway: Stewart andKamprad, 1997, and Shen et al., 1999 (regolith mapping); Zhang eal., 1998 (uranium); Passos and de Souza Filho, 1999 (Archeangreenstone); Denniss et al., 1999, and Huston et al., 1999 (VMS)Martinez-Alonso et al., 1999, and Kruse and Hauff, 1991 (epithermaclays); and Crowley, 1996 and 1999 (evaporites).

SWIR field spectrometers fill an important gap in exploration databy helping to map alteration consistently throughout a mineralizedsystem. Determining alteration mineralogy routinely during anexploration program aids rapid evaluation and therefore increasesef ficiency.

Alteration Mapping

Determination of the type and distribution of alteration minerals is a routine part of exploration to page 16 .

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16 S E G N E W S L E T T E R Nº 39 • OCTOBER ’99

for hydrothermal mineral deposits and is useful in the assessment of exploration properties and the construction of deposit models.Typically, alteration maps are based on macroscopic fieldobservations, supported by limited petrographic or X-ray diffraction

studies. Alteration studies at the deposit scale are limited, or relianton detailed but necessarily restricted sample suites.Lithogeochemistry is used in some environments to evaluatealteration but only works well where lithologies and their petrology are well understood. Lithogeochemistry is difficult to apply toextensive areas of clay alteration, where protoliths are hard toidentify during the exploration program.

Fine-grained alteration minerals commonly are grouped as“argillic” or “phyllic” (Thompson and Thompson, 1996). Suchdescriptions ignore the mineralogy and lose valuable informationregarding the nature of the alteration. The importance of usingminerals and mineral assemblages was noted by Rose and Burt(1979) and subsequent authors, but this approach is not always

applied during exploration. Classification of alteration by mineralogy involves field observations that may be aided by SWIR spectroscopy (Table 2). The use of SWIR spectrometers at a field base allowsmineralogy to be mapped or placed on cross sections. The resultantinterpretation can be applied in real time to guide drilling, andultimately can be integrated with other data to develop targets,models, and regional guides.

Field observations must be made in a careful and systematicmanner. Care is needed in determining the relationship amongminerals prior to assigning them to a single assemblage orinterpreting their relationship to other types of alteration. A series of steps should be followed in order to make realistic interpretations of the observed hydrothermal alteration. These steps are:

1. Determine the minerals present, based on field observations;2. Determine their distribution at the outcrop and hand specimen

scales;3. Employ SWIR analysis carefully, analyzing a variety of locations

on each sample and using systematic sampling techniques;

4. Use the above data to establish the relationships among themain minerals;

5. Outline the distribution at the map scale;6. Use petrography of selected samples to further define

relationships of minerals;7. Augment with X-ray diffraction (XRD) analysis if necessary;8. Use scanning electron microscope (SEM) with energy dispersive

system (EDS) to determine variations in individual minerals andassist with interpretation in fine-grained material;

9. Refine and reevaluate, continually, the interpretation andintegrate the results with other geologic, geochemical, andgeophysical data sets.

SWIR analysis aids exploration from regional to property scalesFor example, in complex zoned intrusive systems, alterationmineralogy determined routinely during mapping helps to define

vertical and horizontal zoning and related ore environments. Withineach environment, alteration mineralogy can define local zoningproviding vectors to mineralization. SWIR spectroscopy is moshelpful where alteration mineralogy is not easily identified in handspecimen because of grain size or weathering. Even where fieldmapping of alteration minerals is effective, SWIR spectroscopyallows recognition of subtle mineralogic and compositiona

variations; these can be important in locating ore.

REFLECTANCE SPECTROSCOPY

Reflectance spectroscopy is an analytical technique used bychemists and mineralogists since the early 1900s, with infrared dataon minerals published between 1905 and 1910 by W.W. Coblentz othe U.S. Bureau of Standards. Commercially available infraredspectrophotometers in the mid-1940s led to increased use of thetechnique for mineralogy. Early reviews of mineral spectra werepublished by Lyon (1962) and Moenke (1962). Farmer (1974

published a comprehensive book on theoretical and practicaaspects and Marel and Beutelspacher (1976) compiled clay mineralsKodama (1985) published spectra of minerals typically found insoils, including numerous hydroxides, oxides, phyllosilicatescarbonates, and sulfates.

S W I R S P E C T R O S C O P Y , C O N T .. . . from 1

Table 1. Examples of the Use of SWIR in Exploration

Mineral Identification Alteration Interpretation Exploration Application

Alunite Advanced argillic — High-sulfidation environment, and zoning around high sulfidation

— Steam-heated zones in low sulfidation

Dickite Advanced argillic — Zoning around high sulfidation

— Sediment-hosted Au, with mineralization

Kaolinite Advanced argillic and — High-sulfidation

weathered rock — Sediment-hosted Au, zoning

Dickite, pyrophyllite, diaspore Advanced argillic — Depth estimation

Chlorite Propylitic, chloritic — VMS zoning

— Uranium zoning

Illite/smectite Argillic — High and low sulfidation, zoning

— Uranium, zoning

Carbonate Carbonate — Mesothermal, zoning

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OCTOBER ’99 • Nº 39 S E G N E W S L E T T E R 17

Mineral spectra in the SWIR were first compiled by Hunt andSalisbury (1970, 1971), and Hunt et al. (1971a, b, c). Hunt’s databaseprovided a basic reference for infrared active minerals in the SWIR region that is still in active use. The work was expanded on by Clark et al. (1990). Hauff (1993) published a commercial referencelibrary. Workers at the Jet Propulsion Laboratory also added to thereferences available (Grove et al., 1992).

Field Portable Spectrometers

Geologists in the remote-sensing community drove the initialdevelopment of field portable SWIR spectrometers that wereparticularly useful to the mineral exploration industry. The ability toeasily produce laboratory-quality data in field situations enhanced

the ability to field check Landsat Thematic Mapper imagery. Severa

field spectrometers are available. These include the Geophysica

Environmental Research, Inc. (GER-IRIS), Analytical Spectral Devices

(ASD-FieldSpec) and Integrated Spectronics Pty. Ltd. (PIMA). The

GER and ASD instruments provide data in the visible, near, and

shortwave infrared wavelengths. The instruments are field portable

but require use of solar illumination. Early published papers includedocumentation of the GER instrument (Marsh and McKeon, 1983)

The instrument was used in field checking of airborne spectroradio

meter data in the Oatman district (epithermal veins), Arizona. Hun

and Ashley (1979) and Crowley (1984) linked

SWIR spectroscopy to alteration mapping.to page 18 . . .

Table 2. Summary of Infrared-Active Minerals, with Distinctive Spectra in the SWIR

Environment of formation Standard terminology SWIR active mineral assemblage (key minerals are in bold)

Intrusion-related Potassic (biotite-rich), K silicate, Biotite (phlogopite), actinolite, sericite, chlorite, epidote,

biotitic muscovite, anhydrite

Sodic, sodic-calcic Actinolite, clinopyroxene (diopside), chlorite, epidote, scapolite

Phyllic, sericitic Sericite (muscovite-illite), chlorite, anhydrite

Intermediate argillic, sericite- Sericite (illite-smectite), chlorite, kaolinite (dickite),

chlorite-clay (SCC), argillic montmorillonite, calcite, epidote

Advanced argillic Pyrophyllite, sericite, diaspore, alunite, topaz, tourmaline,

dumortierite, zunyite

Greisen Topaz, muscovite, tourmaline

Skarn Clinopyroxene, wollastonite, actinolite-tremolite, vesuvianite,

epidote, serpentinite-talc, calcite, chlorite, illite-smectite, nontronite

Propylitic Chlorite, epidote, calcite, actinolite, sericite, clay

High-sulfidation epithermal Advanced argillic—acid sulphate Kaolinite, dickite, alunite, diaspore, pyrophyllite, zunyite

Argillic, intermediate argillic Kaolinite, dickite, montmorillonite , illite-smectite

Propylitic Calcite, chlorite, epidote, sericite, clay

Low-sulfidation epithermal “ Adularia” — sericite, sericitic, Sericite, illite-smectite , kaolinite, chalcedony, opal,

argillic montmorillonite, calcite, dolomite

Advanced argillic— Kaolinite, alunite, cristobalite

acid-sulphate (steam-heated) (opal, chalcedony), jarosite

Propylitic, zeolitic Calcite, epidote, wairakite, chlorite,

illite-smectite, montmorillonite

Mesothermal Carbonate Calcite, ankerite, dolomite, muscovite (Cr-/V-rich), chlorite

Chloritic Chlorite, muscovite, actinolite

Biotitic Biotite, chlorite

Sediment-hosted gold Argillic Kaolinite, dickite, illite Volcanogenic massive sulfide Sericitic Sericite, chlorite, chloritoid

Chloritic Chlorite, sericite, biotite

Carbonate Dolomite, siderite, ankerite, calcite, sericite, chlorite

Sediment-hosted massive sulfide Tourmalinite Tourmaline, muscovite

Carbonate Ankerite, siderite, calcite, muscovite

Sericitic Sericite, chlorite

Albitic Chlorite, muscovite, biotite

Minerals are grouped by assemblages of alteration minerals, and keyed to commonly used terminology; Complete assemblages are in

Thompson and Thompson (1996)

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The most widely used portable instrument in exploration is thePIMA, which collects data only in the SWIR region. The PIMA-II is acommercial field instrument manufactured by Integrated SpectronicsPty. Ltd. in Australia. The instrument has an internal light source,

allowing collection of laboratory-quality data in the field by directillumination of the rock sample. In addition, internal calibrationresults in reliable spectra not subject to variability due to theconditions under which they were measured. The instrument iscapable of measuring a variety of sample types, including rocks,chips, core, powders, and liquids. An analysis typically takes lessthan 30 seconds. PIMA dominates current usage in the industry forthe purpose of alteration mapping. Several PIMA-II instruments

were used in the collection of the data discussed in this paper. All inst ruments require training for effective use, both in the

interpretation of results and in instrument operation. Lack of trainingcan result in broken and malfunctioning instruments or worse,misinterpretation of data. The limitations of the technique must be

understood in order to utilize fully a powerful tool. Integration of spectral data with geologic, geochemical, and geophysicalinformation is also critical.

Field use of short-wave infrared spectrometers has increaseddramatically in the last fi ve years. The increased application of thetool is the result of several developments during the twentiethcentury. The milestones in the development of infraredspectroscopy for minerals include:

• Early documentation (1905 – 1910);• Laboratory use, expansion of mineral reference databases

(1940 – 1985);• Development of field portable instruments (1978 – 1991);

• Real time processing of data (late 1980s);• Commercial availability of portable instrument with internal light

source, PIMA (1991);• Continued expansion of mineral reference data sets (1990s);• Dissemination of case histories and examples of the application

of SWIR spectroscopy to mineral exploration (1990s);• Use of PCs in the field, allowing rapid data interpretation

(1995 – present);• Heightened interest due to use of airborne hyperspectral scanners

and increasingly sophisticated data processing software(1998 – present).

SWIR spectrometers also are now employed in numerous othercapacities beyond exploration. In particular, they are useful in

mineral processing control procedures and evaluation of leach pilesand tailings dumps. Continued development of applications willlead to uses in other environmental applications and thegeotechnical fields.

SWIR Spectroscopy

Remote-sensing geologists use a variety of bands within theelectromagnetic spectrum, including the visible-near infrared (VNIR),short-wave infrared (SWIR), and mid-infrared (MIR). Field portableinstruments detect in the SWIR region, which is sensitive tomolecular changes, and also in the VNIR, where color variations andchanges in elemental oxidation states (e.g., iron and chromium) areobserved. VNIR, however, does not relate directly to composition.

Short-wave infrared spectroscopy detects the energy generatedby vibrations within molecular bonds. These bonds have bendingand stretching modes within the 1,300- to 2,500-nm region of theelectromagnetic spectrum. The observed absorption features aremanifestations of first and second overtones and combination tonesof fundamental modes that occur in the mid-infrared region. SWIR iparticularly sensitive to certain molecules and radicals, includingOH, H2O, NH4, CO3, and cation-OH bonds such as Al-OH, Mg-OHand Fe-OH. The positions of the features in the spectrum and theicharacteristic shapes are a function of the molecular bonds presentin the mineral. Variations in chemical composition may be detectedas the wavelength positions of features shift consistently withelemental substitution. SWIR spectroscopy is partly sensitive tocrystallinity variations, but may not detect primary changes in thelattice structure. A typical spectrum consists of several absorptionfeatures. Figure 1 illustrates the various aspects of an absorptionfeature, including wavelength position, depth and width (full-heighthalf-width maximum). The outline of the hull or continuum is alsoshown.

Figure 1. Detail of feature in kaolinite SWIR spectrum collected with PIMA-I

spectrometer. The hull, feature depths, position and the full width half-maximum

(FWHM) are shown.


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Minerals can be distin-guished not only on thebasis of distinctive featuresand wavelength positions,but also by the character of the profile (without hullsubtraction). Examples of common alteration mineralsa r e s h o w n i n F i g u r e 2 .Mineral identification isbased on wavelength posi-tions, intensity and shape of absorption troughs, and theoverall shape of the entirespectrum.

The short-wave infrared wa vel en gt h re gi on is no tsuitable for most anhydroussilicates. In addition, it isdifficult to identify minerals

present in amounts less than5%, unless the sample is asimple mixture with quartzand the mineral is highly reflective. Infrared reflec-tivity varies between mineralspecies . In mixtures of infrared-active minerals, thedominant and typically mostreflective mineral is easily identified; however, as ageneral rule, 10% or more of a mineral must be present

for positive identification. Where low reflectance min-erals are present, recognitionmay require 20% or more of the mineral in the sample(e.g., carbonate, chlorite).

Mineral chemistry : Vari-ations in mineral chemistry are typically detected by shifts in wavelength posi-tions or changes to the hullshape. The presence of ironin most minerals results in a

strong positive slope from1,300 to 1,900 nm. A com-parison of the spectra from Fe-rich and Mg-rich clinochlore is shownin Figure 3. Chemical variation in the carbonate group of minerals isgauged by a shift in the position of the major feature as a functionof the cation present. The dominant feature varies widely, includingmagnesite (Mg) at 2,300 nm, dolomite (Mg, Ca) at 2,320 nm, calcite(Ca) at 2,330 nm and rhodochrosite (Mn) at 2,360 nm. Variations inalunite-group mineral chemistry are manifested by shifts in the1,480-nm position, with values ranging from ~1,461 (NH4), to ~1,478nm (pure K) to ~1,496 (Na) to 1,510 nm (Ca). Examples of all fourspectra are shown in Figure 4. Depending on the quality of reference spectra, petrographic, SEM or electron microprobe data

may be required in order to assign an observed variation to achange in chemical composition. Mineral composition variations arebest evaluated from monomineralic samples; however, it may bepossible to define variations in some mineral mixtures.

DATA COLLECTION AND ANALYSIS

Data Collection

Understanding the variables that affect spectra is critical to theinterpretation of spectral data sets. These variables include grainsize, transparency, sulfide content, overallreflectivity, water content, heavy element

to page 20 . . .

Figure 3. Example of Fe- and Mg-bearing clinochlore.

Note the steep slope on the Fe-rich sample from 1,300

nm to 1,900 nm.

Figure 2. Stack plot with examples of spectra

characteristic of individual minerals. Reflectance values

are offset for clarity. Examples are from the SPECMIN™

database.

Figure 4. Examples of K-, Na-, Ca-, and NH4-bearing alunite group

minerals. An inset shows the positions of distinctive features for K and

Ca in the Ca-dominant sample. Elements present in this sample were

confirmed by EDS analysis (scanning electron microscope). Examples

are from the SPECMIN™ database.

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content, contaminants (e.g., oil, organic material), orientation of minerals (e.g., micas), and mineral mixtures. Good data collectionprocedures that minimize the effect of these variables should beadopted. A minimum of 2 spectra per sample is necessary, both forreproducibility of data and to test for heterogeneity of the samples.By analyzing the groundmass, veins, phenocrysts, vug infill, fracturecoatings, and weathered surfaces, the data gatherer can identify several minerals in one sample. Clear descriptions of basicobservations — e.g., color, texture, veining or fractures, veinenvelopes, and weathering state — are important for high-quality spectral interpretation.

Sample types and sample processing may also affect the spectra.Hand specimens, powders, rock chips, liquids, and reject samplescan all be analyzed, with some minor variations observed in thespectra. Samples that have been pulverized (e.g., analytical pulps)commonly yield extremely degraded patterns, where many of thespectra are similar in appearance. Pulps are typically made with ringmills that generate heat during the crushing process. Clay analysis,

in particular, will be inaccurate if the structure of the mineralchanges with heating. Standard XRD analysis for clays is also done

without the use of r ing mills for the same reason.Instrument stability must be considered in evaluating spectra,

particularly in variable field conditions. Without calibration, wave length positions will shift as the instrument heats up or ismoved. Analysis of a standard kaolinite with the PIMA-II shows asystematic wavelength shift of 2 nm downward as the instrumentheats up from 22° to 44°C. Although small (within the spectralresolution of the PIMA spectrometer), this shift highlights the needfor consistent recalibration of the instrument. Good laboratory practice also includes use of standards and the saving of calibrationfiles as references for future checks on instrument drift. Some

wo rke rs ha ve rep ort ed 2 nm va ri at io ns in as few as 10measurements.

SWIR spectroscopy is a useful tool for identifying minerals inindividual samples; however, its greatest power comes fromconsistent collection of data in a systematic manner. Sampleintervals may need to be as small as 1 to 2 m to evaluate gradientsin alteration mineralogy and define boundaries when used for drillcore logging or detailed traverses. Once the basic variations aredescribed, spacing can be widened, depending on the area to becovered and the goal of the survey. For example, core logging may be done on 5- to 10-m spacing, whereas mapping may be widenedto 50 or even 100 m. Closely spaced sampling typically producesthe most useful information. For mapping, the sample locations may

be laid out on a grid pattern and include soils or may be tied tooutcrop patterns. Data processed and evaluated concurrent withmapping can have a direct impact on an exploration program.

Data Analysis and Processing

Mineral identification is based on the use of reference data sets, which are empirical records of each mineral’s characteristic spectra. Visual observation of a group of mineral spectra will quickly show variations based on numerous factors, including mineral chemistry,temperature, and mode of formation (reflected in crystallinity), andother subtle changes. The user’s greatest asset, then, is a well-defined and large reference collection or spectral database createdfrom samples representative of a wide variety of depositenvironments and occurrences. Experienced users are ultimately

able to identify many spectra characteristic of minerals by eye, asdata collection is carried out. Even novice users can quickly learnthe basic minerals important in their study area. Only a few mineraltypically are required to characterize an area, which also realisticallyallows for the tracking of variability within a single mineral speciesReference databases are extremely useful in refining the ability odata processing software to provide automatic identification.

Automatic identification may be helpful when working with largedata sets on well-defined areas. In order to achieve high-qualityresults, variations at the deposit scale must be observed andrecorded by the user, using reference data sets created for thatdeposit. Deposit or region specific data sets appear to be critical inobtaining reliable results from attempts at unmixing (identifyingmineral mixtures) using algorithms. Identification of complexmixtures will require geologic context, user experience, andestablishment of reference samples with additional information, e.g.petrography, XRD, and SEM analysis, but may be difficult withcurrently available algorithms.

Data processing software allows the subtraction of the hull (seeFig. 1), typically followed by extraction of feature positions

intensities, and widths. There are various methods to extracdiagnostic derivative spectra from those that are measured. Twocommon methods are the hull quotient, which is a “rubber bandmethod of removing the effects of variable background slopes, andthe first derivative that removes the effects of background byemphasizing changes in response. A variety of software packageare available commercially, with the most flexible being those thaallow the importing of data from a variety of sources (spectrometersor scanner data). Care must be taken when extracting data to ensurethat the data is treated in the same manner, e.g., feature positions albased on the hull quotient, or on the first derivative.

In some cases, use of a single feature position, depth, or ratio odepths of two features may provide broad outlines of alteration

zones. This style of data extraction with a computer program can bedone extremely quickly; however, the data must be carefullyevaluated by an experienced user to confirm that the comparison isof similar material, with comparable mineral assemblages andfeatures. Since wavelength positions for various minerals overlapfaulty results which are based on a single feature may be producedFor example, the Al-OH feature at ± 2,200 nm may represent alunitepyrophyllite, kaolinite, dickite, illite, mixed-layer illite/smectitesmectite, or muscovite, which all obviously have differenimplications in terms of deposit modeling. Contouring of such datamust also be carefully carried out. Individual analyses may falsely

weight the data, resulting in spurious features.Mineral percentages: A common objective of SWIR analysis is to

determine not only the minerals present in a sample but also theirrelative abundance. Many software programs attempt to providemineral unmixing as part of the package. This task is challengingdue to the lack of knowledge regarding the absorption coef ficientfor the molecular bonds detectable in the SWIR range of theelectromagnetic spectrum. The spectral data indicate that mineralapparently are not present in linear mixed configurations, but ratheas a function of these unknown absorption coefficients. Theintensities of the absorption features, therefore, cannot be used as a1:1 correlation to relate directly with the amount of mineral presentFor instance, an iron chlorite will absorb more energy at itsdiagnostic wavelengths and reflect less back to the detector than analuminum-bearing mineral, a muscovite, which more accurately


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reflects the absorbed energies. In addition, there is a matrix effectproblem when non-infrared active minerals are also present andabsorbing, but not reflecting the excitation energy.

In cases where the absorption coef ficients are likely to be similarfor the minerals in question, the resulting spectrum can be treatedessentially as a linear mixture. The key to accurate results lies inbuilding calibration files from the sample suite under investigation.Monomineralic end members must be chosen for the models tosucceed. The accuracy of the mixing algorithm may be as good as4%; however, this will vary depending on the software (algorithm),the materials in the mixture, and their relative abundance.

Instrumentation is also a limiting factor in producing accurateresults. PIMA-II has approximately a 5- to 6-nm resolution andsamples are collected in 2-nm steps. This over-sampling is done toimprove the reproducibility of the method; however, it does notnecessarily improve the accuracy and leads to an artificial perceptionof a 2-nm resolution. Therefore, the limit of the method forresolving the wavelength positions remains between 5 and 6 nm.

CASE STUDIES

The following case studies illustrate the use of SWIR inexploration. All of the analyses were collected with PIMA-IIspectrometers. These examples emphasize the use of the techniqueas a mapping tool, for integration with other data types. Theexamples include alteration maps, detailed drill logs, and integrationof geochemistry, petrography and spectral data. The mineralidentifications and variations in feature positions are shown todefine alteration zones and provide vectors toward mineralization.Blind application of digital data, however, may lead to false resultsbased on inadequate sampling or misinterpretation of spectral

variations.

High-Sulfidation Epithermal Gold Deposits

The general characteristics of high-sulfidation deposits are wellknown and are summarized in Arribas (1995), who includesnumerous examples of deposits around the world. These depositsare known for their extremely fine grained alteration minerals andtypically homogeneous appearance. Minerals that are infrared activeand form in these environments are shown in Table 2. Alterationmapped in the field typically relies first on varying degrees of silicicalteration ranging from leached, vuggy quartz to zones of replacement quartz. Beyond the quartz-dominated areas, however,the alteration assemblages are commonly mapped simply asadvanced argillic and argillic alteration during exploration programs.Identification of individual minerals, some of which are critical tozoning patterns, is extremely difficult. Use of SWIR spectroscopy

allows the major alteration minerals to be easily and rapidly identified.

Virgen : A comprehensive alteration study of the Virgen property was comple ted for Gitennes Exp loration Inc ., Vancouver. The Virgen property is a gold prospect located 180 km east of Trujillo,Peru. Cretaceous sedimentary rocks and Tertiary andesites hostmineralization. The aim of the work was to determine alterationzone patterns with respect to mineralization using the PIMA-IIspectrometer. Data were collected from available drill holes, handsamples, and road cuts. A total of 22 drill holes were analyzed andover 900 spectra were obtained across the property. Alterationminerals observed using spectral analysis include alunite, dickite,pyrophyllite, diaspore, kaolinite, smectite, illite, and quartz.

Alteration was def ined based on mineral assemblages, and thealteration patterns determined by spectral analysis were used to helppredict zones of mineralization. It was found that dickite increasedin areas associated with gold mineralization, thus providing amineralogical pointer to areas favorable for gold. Figure 5 is a drillog from the Alumbre zone, showing the relationship of thealteration to mineralization. Spectral analyses were carried out every2 to 3 m down hole. This hole was subsequently deepened andadditional mineralization associated withdickite, alunite, and vuggy quartz was found.

Figure 5. Drill log from the Alumbre zone, Virgen deposit, Peru, showing variations in

alteration mineral assemblage, lithology, and mineralization. Dickite in the advanced

argillic alteration was used as an indicator for mineralization.

to page 22 . . .

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Pamel : An alteration study of the Pamel prospect in the WesternCordillera of Peru was conducted for Candente Resource Corp. Theproperty is at an early stage of exploration. Geologists employed atthe property selected hand and reject samples from the geochemical

sampling program and submitted these to Vancouver for analysis with a PIMA-II spectrometer. The results from approximately 128samples were integrated with geologic information and outcroppatterns. Based on three days work, combined with the previousgeologic data, an alteration map was created for the property. Theresults clearly showed distinct alteration zones and helped delineatezones of interest (Figure 6). The alteration varies, from silicificationto alunite-dickite, to alunite-kaolinite, to kaolinite dominant, andoutward to sericite, illite, and chlorite. Small amounts of diaspore,topaz, and tourmaline were also noted locally. A detailed study wasconducted in the western portion of the map area. Samples from the

zone contain two to three minerals in a single spectrum. Exampleof these spectra are shown in Figure 7. The alteration and style omineralization is consistent with a high-sulfidation epithermal tomagmatic-hydrothermal environment.

Figure 7. Examples of spectra for mineral mixtures, from the Pamel prospect

Candente Resource Corp.

Low-Sul fi dation Epithermal Gold Deposits

Alteration in low-sulfidation deposits is characterized by adulariaand calcite-replacement textures within quartz veins that gradeoutward in the host rock to illite, illite-smectite, and illite/smectite-chlorite zones. Calcite may also occur within the alterationenvelope. The variation in clay al teration outward frommineralization is typically dif ficult to define in the field, but may bedetected with SWIR. Zoning patterns are well described bynumerous authors, including White and Hedenquist (1990) andSillitoe (1993). The widths of alteration envelopes vary fromcentimeters to meters. Zones of steam-heated, advanced argillicalteration may also cap or develop laterally from low-sulfidation


1000metres

Figure 6. Alteration map based on outcrop patterns for the Pamel prospect,

Candente Resource Corp.

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mineralization. Differentiation of these zones from high-sulfidationsystems is critical to exploration in this environment. Table 2 listsminerals found in the low-sulfidation and steam-heatedenvironment.

Patagonia, southern Argentina: SWIR spectroscopy was carriedout systematically on RC (reverse circulation) chips from a property in Patagonia, southern Argentina. The area contains zones of high-grade mineralization associated with illite-dominant alteration. Theuse of spectral analysis allowed mapping of alteration patterns.Figure 8 is a drill log showing the distribution of illite, illite-gypsum,illite-smectite, and illite-chlorite. The illite-gypsum zones clearly flank the mineralization.

Figure 8. Drill log shows the distribution of alteration with respect to gold assays.

Example of low-sulfidation system, Patagonia, southern Argentina.

Volcanogenic Massive Sul fi de Deposits

Alteration mapping is an important aspect of VMS exploration inareas where metal distribution may provide limited informationTypically, distribution of Fe and Mg chlorite and sericite (muscovitezones are used as a vector toward ore lenses. Clays may also be animportant part of some systems. The style of alteration varies

depending on the setting of the deposit; Franklin (1993) givesseveral models. Lithogeochemistry is commonly employed todifferentiate alteration types, but use of SWIR spectroscopy may alsoprovide valuable, direct information regarding the alterationmineralogy.

Kidd Creek: Distribution of chlorite and sericite is outlined byKoopman et al. (1999) for the Kidd Creek deposit, western AbitibSubprovince, Canada. Variations in proportions of the two mineralappear to reflect proximity to ore and help to outline mineralizedzones. The initial work was based on field observations, X-raydiffraction and petrographic techniques. Seventy-six of the XRDsamples were analyzed with a PIMA-II spectrometer. A variety oprocessing techniques were applied to the data set, including

automatic mineral unmixing using commercially available softwarecomparison of ratios of feature depths in the Al-OH region to the FeOH (as described by Huston, 1999); and simple comparison of thedata to a set of artificially (linear) mixed spectra. The linear mixedspectra used site-specific end members, and 10% increments (Figure9, page 24). Results from this comparison were the most consistenand correlate well with the estimated mineral abundance based onpeak intensities from the XRD analysis (Table 3, page 26). Theattempts at semiquantification using the other techniques, howeverdid not produce reliable results. The effect of different reflectivityand of mixed mineral assemblages may hinder the use of theautomatic techniques in determining mineral percentages in thisenvironment until better data processing is possible.

Intrusion-Related Deposits

Many deposit types occur within the intrusion-relatedenvironment. Mapping of alteration has application to both broadand local zoning. As shown in Table 2, a wide variety of alterationminerals may be present in this environment and can be used tofocus exploration on a specific target type.

Red Mountain, British Columbia : A detai led study of therelationship of alteration and mineralization was completed at theRed Mountain project in northern British Columbia and is describedby Rhys et al., 1995. Gold mineralization in the area is spatiallyrelated to a porphyry Cu-Mo stockwork.

Hydrothermal alteration is pervasive throughout the pre-Tertiary

rocks on Red Mountain, including all phases of the main intrusionsSeveral shallow-dipping alteration zones are stacked above a quartzstockwork/molybdenum zone with pervasive sericite alterationThese zones include: (1) sericite-quartz-pyrite alteration (pyritedominant), (2) chlorite-K feldspar-sericite-titanite alteration withdisseminated and vein pyrrhotite, (3) brown to black tourmaline

veins, and (4) K feldspar-titanite-actinolite alteration. Anomalousgold values are associated with the transition from pyrite topyrrhotite, and sericite to K feldspar alteration. High-grade zones arefocused below areas of abundant tourmaline, in pervasive sericitealteration. Details of the alteration zones are given in Table 4 (page26), along with a summary of major elementgeochemistry. The deposit is structural ly

to page 24 . . .

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disrupted by several faults. The distinct alteration zones provide a

way to reconstruct relative locations within the system where fault

boundaries are crossed in drilling.

Data gathered by the PIMA clearly outlines the major alteration

zones, based on the presence of key minerals for each assemblage.

The results of SWIR analysis on drill core samples are compared to

geochemistry and petrography in Table 4, and representative spectra

from the deposit are included in Figure 10. The complete log for a

drill hole from the Marc zone is shown in Figure 11. Integration

with the previous petrographic , geologic, and geochemical data

provides a framework for application of SWIR spectroscopy to thedeposit area and exploration within the district.

CONCLUSIONS

SWIR spectroscopy is a tool to assist field mapping in mineraexploration. The ability to rapidly differentiate fine-grained

alteration minerals in the field allows for an enhanced understandingof the property under investigation, and the results can be applied

immediately to the exploration program. Further refinement of thealteration assemblages, including the use of other analytica

techniques, will yield data important for the development of deposimodels and regional exploration programs. Several components arecritical to a successful survey. These are:


Figure 9. Artificial linear mineral mixtures determined from actual chlorite and muscovite end members for the Kidd Creek footwall rhyolite. The percentage of chlorite

increases down the plot. Examples of the comparisons of real spectra with selected mineral mixtures are shown on the right. Mineral mixtures were generated wit

SPECWIN©, Spectral International Inc.

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• A systematic, well-planned sampling program, with consistentsample spacing depending on the purpose of the survey;

• Alteration mapping concur rent with a mapping or a dri llingprogram, to allow rapid incorporation of information andeffective application of data;

• SWIR data collection by a trained operator with a geologicbackground;

• Use of mineral-dominant assemblages for preliminary mapping;• Subsequent data processing of wavelength positions or other

spectral characteristics, to further evaluate alteration;• Selected petrography and XRD for complete alteration

assemblages that can be related to the spectral data.

Wit h the increased use of SWIR spectrometers in the field,distribution patterns over large areas can be delineated. The use of field spectrometers has provided a vast new database that not only is aiding exploration, but also will contribute ultimately to theunderstanding of these systems.

······················· ACKNOWLEDGMENTS ······················

We would like to thank the many geologists and consultants inexploration for their contributions to the use of SWIR spectroscopyThe successful application of the technique would not have beenpossible without them. Data in this paper was published with thepermission of Jerry Blackwell (Gitennes Exploration Inc.), Joey

Freeze (Candente Resource Corp.), Mark Hannington (GeologicaSurvey of Canada) and Jacques Houle (Royal Oak Mines) who areall thanked for their support. The paper benefited from reviews byNoel White and Charles Tarnocai. In particular,Noel White is thanked for his enthusiasm, well-timed reminders, and critical review.


to page 26 . . .

Figure 10. Representative spectra used to determine alteration zones at Red

Mountain, British Columbia.

Figure 11. Drill log from the Marc zone, Red Mountain, British Columbia. Log show

major alteration zones, lithologies, and the location of Au-Ag mineralization. The

SWIR active minerals identified in each alteration zone are: A. actinolite dominant

actinolite + chlorite (Fe > Mg) + datolite + prehnite + axinite, B. tourmaline

dominant: chlorite (Mg > Fe) + muscovite + schorl + axinite + calcite, C. Tourmaline

stockwork: schorl + Mg chlorite + muscovite, D. auriferous pyrite-pyrrhotite

stockwork: muscovite + chlorite, and E. pyrite dominant: muscovite + chlorite +

clinozoisite.

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Table 4. Distribution of Alteration Zones at Red Mountain, B.C.

* Denotes vein mineralogy

Zones are listed from the top down, with their associated geochemistry, vein mineralogy and alteration mineralogy (petrography and SWIR analysis); after Rhys et al (1995)

Geochemistry

Na2O > 3.3%,; K 2O < 0.5%;CaO > 2.8%; Sr > 400 ppm

Na2O > 3.3%; K 2O < 0.5%;

CaO > 2.8%; Sr > 400 ppm

Na2O > 3.3%; K 2O < 0.5%;CaO >2.8%; Sr > 400 ppm

Na2O < 1.5%; K 2O > 5%;high values in Au (>0.5ppm); Ag, As, Sb andlocally Cu, Zn correspondwith ore zones

Na2O < 3.5%; K 2O > 4%;

CaO < 3.3%; Sr < 100 ppmSimilar to pyrite-dominantalteration

Cu > 300 ppm; Mo > 30ppm; SiO2> 55% andsimilar values to pyritedominant alteration forNa2O, K 2O, CaO, and Sr

Thickness

>150m

100-300m

100–200m

10-50m

100-200m

<5–100m

>200m

AlterationZone

Actinolite

Tourmaline

stockwork

Pyrrhotite

AuriferousPy-Po

stockwork

Pyrite

Gypsumstockwork

Quartzstockwork,

Mo-Cu

Veins

Chlorite + pyrite +actinolite + calcite

Tourmaline + pyrite +

chlorite + pyrrhotite

Pyrrhotite + pyrite ±chalcopyrite ± chlorite ±calcite ± quartz ±sphalerite ± galena

Pyrite ± pyrrhotite ±quartz ± chlorite

Pyrite ± calcite ± quartz ±

chloriteGypsum + pyrite + calcite± quartz

Quartz + pyrite ± chlorite± epidote ± magnetite ±molybdenite ± chalcopyrite

Petrography

K feldspar + actinolite +chlorite + titanite + albite+ pyrite ±

K feldspar + chlorite +

titanite + pyrite +tourmaline + pyrrhotite

K feldspar + sericite +pyrrhotite + pyrite +chlorite ± tourmaline

Intense sericite + pyrite;mantled by disseminatedand veinlet sphalerite +pyrrhotite + pyrite

Sericite, pyrite ± calcite ±

chlorite ± tourmalineSericite + pyrite ± quartz± K feldspar

Sericite + quartz + pyrite+ chlorite + K feldspar ±epidote ± tourmaline ±magnetite ± hematite

SWIR

Actinolite + chlorite(Fe>Mg) ± axinite* ±datolite ± muscovite ±prehnite

Schorl (tourmaline) +

chlorite (Mg) ± muscovite± carbonate ± axinite*

Muscovite + chlorite (Mg)axinite ± carbonate ±prehnite

Muscovite + chlorite (Mg)

Muscovite + chlorite

Gypsum*

Chlorite (Mg), muscovite ±clinozoisite*



Table 3. Comparison of SWIR and XRD Results for Selected Samples from the Kidd Creek Footwall Felsic Units

Spectra Lithology SWIR X-ray diffraction

ah09667b Rhyolite Chlorite>>

muscovite Quartz>>

clinochlore > muscoviteah09682b Quartz porphyry Chlorite > muscovite Quartz >> clinochlore, muscovite

ai00510b Quartz porphyry tuff Chlorite >> muscovite Quartz >> albite, clinochlore, calcite > muscovite

ai00557a Felsic tuff Chlorite > muscovite Quartz >> clinochlore, albite, calcite > muscovite

ai00671b Rhyolite Chlorite > muscovite Quartz >> clinochlore > muscovite

ai00724b Rhyolite Muscovite > chlorite Quartz >> albite, clinochlore > muscovite

ai01511b Quartz porphyry tuff Chlorite > muscovite Quartz >> albite > clinochlore, muscovite

ai01532b Quartz porphyry tuff Chlorite > muscovite Quartz >> clinochlore, calcite > muscovite

ai01538a Quartz porphyry tuff Chlorite = muscovite > kaolinite? Quartz >> clinochlore > muscovite

ai01545a Rhyolite Muscovite >> chlorite > kaolinite? Quartz >> albite, muscovite > clinochlore

ai01550b Felsic tuff Muscovite = chlorite Quartz >> albite > clinochlore, muscovite

SWIR analysis identified the chlorite as Fe-bearing

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