isatis - geovariances.com · boundary models. page 18 m i n i n g associates masbate geology...

M I N I N G ASSOCIATESM I N I N G ASSOCIATES

ISATIS ISATIS

Narrow Vein Modeling and Narrow Vein Modeling and Resource EstimationResource Estimation

Mark SweeneyMark Sweeney


Mark SweeneyMark Sweeney

►► Current PositionCurrent Position Resource Geology Resource Geology –– GeostatisticsGeostatisticsMining Associates Pty LtdMining Associates Pty Ltd BrisbaneBrisbane

►► Career History:Career History: Strategy Optimisation SystemsStrategy Optimisation Systems BrisbaneBrisbane

Snowden Mining ConsultantsSnowden Mining Consultants PerthPerthRioRio TintoTinto -- Technical ServicesTechnical Services MelbourneMelbourne

RossingRossing Uranium Uranium NamibiaNamibiaWitwatersrandWitwatersrand gold fieldsgold fields RSARSA


Masbate Gold MineMasbate Gold Mine

Thank youThank you’’ss

►►Thistle Mining Thistle Mining -- Masbate Gold MinesMasbate Gold Mines

►►Johan Johan Raadsma Raadsma -- Technical ManagerTechnical Manager►►Geoff BoswellGeoff Boswell -- Manager GeologyManager Geology


Masbate Masbate -- PhilippinesPhilippines


Masbate Gold DepositMasbate Gold Deposit

4 km4 km

2 km2 km


Isatis Isatis –– Masbate Success StoryMasbate Success Story

Presentation falls into two sections:Presentation falls into two sections:

►►Geological ModelingGeological Modeling -- SURPAC

►►Resource EstimationResource Estimation -- ISATIS


Isatis Isatis –– Masbate Success StoryMasbate Success Story

Geological ModelingGeological Modeling


DomainingDomaining

GEOSTATISTICS NOT WORKING CORRECTLYGEOSTATISTICS NOT WORKING CORRECTLY(....are you ignoring the geology)(....are you ignoring the geology)

fudge factorsfudge factors►►---- generally known as Mine Call Factors generally known as Mine Call Factors

(MCF(MCF’’s) or Survey Loss Factors (SLFs) or Survey Loss Factors (SLF’’s)s)►►---- have been used for over 100 years c.f. have been used for over 100 years c.f.

the Witwatersrand gold mines the Witwatersrand gold mines


DomainingDomaining

Back to basics:Back to basics:

►►…………FIRST DO THE GEOLOGYFIRST DO THE GEOLOGY !!!!!!!!!!!!!!!!


Masbate Masbate -- HistoryHistory►► Gold province.Gold province.

►► History of large History of large scale bulk mining.scale bulk mining.

►► Small scale mining Small scale mining by locals.by locals.

►► Targeting high Targeting high gradesgrades


DomainingDomainingAnalogy: Analogy: -- measuring the height of grassmeasuring the height of grass

Unconstrained Geology Unconstrained Geology

Grass Thickness


DomainingDomainingUnconstrained Geology Unconstrained Geology –– apply a cutoff grade apply a cutoff grade

(COG) e.g. 0.7(COG) e.g. 0.7

COG Over-estimation of grade

Under- estimation of grade

0.7


DomainingDomainingHard Boundaries Hard Boundaries -- domainingdomaining reduces stationarity issuesreduces stationarity issues

-- globally correct T, G, Mglobally correct T, G, M-- locally correct T, G, Mlocally correct T, G, MGrass Thickness

(COG)

geology boundaries

0.7


DomainingDomaining

Vein Material:Vein Material:

►►Vein grades 4 times stockwork gradesVein grades 4 times stockwork grades►►All grades within veins are above economic All grades within veins are above economic

cutoff grade (<1 g/t Au)cutoff grade (<1 g/t Au)►►OrdinaryOrdinary KrigingKriging applied to determine applied to determine

panel grades within vein boundaries.panel grades within vein boundaries.


Surpac Surpac -- Geological ModelingGeological Modeling

Stockwork Material:Stockwork Material:

►►Lower grades around economic cutLower grades around economic cut--offoff►►Sporadic high grade intersections.Sporadic high grade intersections.►►Blocks too large to capture localized higher Blocks too large to capture localized higher

grades.grades.


Isatis Isatis –– Automated DomainingAutomated DomainingAutomated Vein Automated Vein

Modeling:Modeling:

►► Vein too complexVein too complex

►► Compliance issues Compliance issues JORC etc!JORC etc!


Isatis Isatis –– Data AnalysisData AnalysisWhat do we know?:What do we know?:

►► Veins well definedVeins well defined►► 2 m to 5 m width2 m to 5 m width►► 4:1 grade contrast4:1 grade contrast

►► Require hard Require hard boundary modelsboundary models


MasbateMasbateGeology counts!!Geology counts!! HIGH GRADE VEIN

STOCKWORK

‘PATCHY’ ORE GRADES


Geological Modeling Geological Modeling -- SurpacSurpac

►► Wireframes generated Wireframes generated in Surpac.in Surpac.

►► Composites and block Composites and block model exported to model exported to ISATIS.ISATIS.


Isatis Isatis –– Geostatistical SoftwareGeostatistical Software

IsatisIsatis::►►Industry standard forIndustry standard for GeostatisticalGeostatistical

SoftwareSoftware►►Complete suite ofComplete suite of geostatisticalgeostatistical tools.tools.►►Enhanced statistical functions.Enhanced statistical functions.►►Seamless integration most major geological Seamless integration most major geological

modelling system.modelling system.


Isatis Isatis -- Data ImportData Import

►►Composites imported directly into IsatisComposites imported directly into Isatis►►Multi format import types:Multi format import types:

►►ASCIIASCII►►ExcelExcel►►VulcanVulcan►►DatamineDatamine►►SurpacSurpac►►GemcomGemcom


Isatis Isatis –– Data AnalysisData Analysis

►► Identify high grade Identify high grade trendstrends

►► Identify outlier gradesIdentify outlier grades

►► StationarityStationarity issuesissues

……..........understand your dataunderstand your data !!!!!!!!!!!!!!!!


Isatis Isatis –– Data AnalysisData Analysis

-3 -2 -1 0 1 2 3

Gau_gold

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

Frequencies


Structural ComplexityStructural Complexity

►► Need to model vein Need to model vein material separately.material separately.

►► Stockwork material too Stockwork material too low grade to be low grade to be included with the vein included with the vein ore.ore.


Isatis Isatis –– Composite SelectionComposite Selection

GraphicalGraphical

LogicalLogical


Grade Cutting Grade Cutting –– Spatial DistributionSpatial Distribution

0 5 10 15

gold

0.0

0.1

0.2

0.3

0.4

Freq

uenc

ies

Mask higher grade Mask higher grade samplessamples

Review spatial Review spatial distribution of distribution of high gradeshigh grades


Grade Cutting Grade Cutting –– Statistics & QuartilesStatistics & Quartiles

Basic StatisticsBasic Statistics

ReportingReporting


Isatis Isatis -- Grade CuttingGrade Cutting

Define variablesDefine variables

Grade cut formulaGrade cut formula


Isatis Isatis -- DeclusteringDeclustering►► Remove effects of clustered data Remove effects of clustered data –– effective disteffective distnn..►► Gives effective weighting to clustered higher gradesGives effective weighting to clustered higher grades►► Give initial indication of global krige gradesGive initial indication of global krige grades


Isatis Isatis -- DeclusteringDeclusteringCell Declustering Analysis

raw mean

-10%

1

2

3

4

5

6

7

Raw

2.5x2

.5 5x5

10x1

020

x20

25x2

550

x50

Declustering size

4m vertical4m vertical

2m vertical


Isatis Isatis -- VariographyVariography

N0

D90

N90

0

0

50

50

100

100

150

150

200

200

Distance (m)

Distance (m)

0.00 0.00

0.25 0.25

0.50 0.50

0.75 0.75

1.00 1.00

1.25 1.25

Variogram : gau_au

Variogram : gau_au

N120

N293 D19

N113 D-19

N284 D37

N104 D-37

N269 D54

N89 D-54

N237 D68

N57 D-68

N183 D68

N3 D-68

N151 D54

N331 D-54

N136 D37

N316 D-37

N127 D19

N307 D-19

U

V

N120

N281 D-7

N101 D7

N262 D-13

N82 D13

N242 D-17

N62 D17

N221 D-20

N41 D20

N199 D-20

N19 D20

N178 D-17

N358 D17

N158 D-13

N338 D13

N139 D-7

N319 D7

U

W

N210 D70

D-90

D90

N210 D-70

N30 D70

N210 D-50

N30 D50

N210 D-30

N30 D30

N210 D-10

N30 D10

N210 D10

N30 D-10

N210 D30

N30 D-30

N210 D50

N30 D-50

V

W

0.00

0.00

0.25

0.25

0.50

0.50

0.75

0.75

1.00

1.00

1.25

1.25

Distance (m)

Distance (m)

0.0 0.0

0.1 0.1

0.2 0.2

0.3 0.3

Variogram : au:alt cut=3.0

Variogram : au:alt cut=3.0

29500

29500

29600

29600

X (m)

X (m)

26400 26400

26500 26500

26600 26600

26700 26700

Y (m

) Y (m)


Isatis Isatis –– Data TransformationData Transformation

0 5 10

gold

0.0

0.1

0.2

0.3

Freq

uenc

ies

-4

-4

-3

-3

-2

-2

-1

-1

0

0

1

1

2

2

3

3

4

4

gau_gold

gau_gold

0.00 0.00

0.05 0.05

0.10 0.10

0.15 0.15

0.20 0.20

Freq

uenc

ies Frequencies


Isatis Isatis –– Gaussian TransformsGaussian Transforms


Isatis Isatis -- Gaussian VariographyGaussian Variography

N315

D90

N45

0

0

100

100

200

200

300

300

400

400

Distance (m)

Distance (m)

0.00 0.00

0.25 0.25

0.50 0.50

0.75 0.75

1.00 1.00

1.25 1.25

Variogram : gau au vn=1 Variogram : gau au vn=1

N0

N90

D-90

0

0

100

100

200

200

300

300

400

400

Distance (m)

Distance (m)

0 0

100 100

200 200

300 300

Variogram : au

Variogram : au

Raw VariableRaw Variable Transformed VariableTransformed Variable


Isatis Isatis -- Kriging ParametersKriging Parameters►► Block size criticalBlock size critical►► Graphically observe Graphically observe

block sizesblock sizes►► Block size and data Block size and data

densitydensity


Isatis Isatis -- Kriging ParametersKriging Parameters

►►Graphical interfaceGraphical interface

►►Observe kriging Observe kriging weightsweights

►►Improve kriging Improve kriging strategiesstrategies


Quadrant SearchQuadrant Search►► Quickly identify Quickly identify

improvements to krigingimprovements to kriging

►► Remove bias due to Remove bias due to clustering of dataclustering of data

►► Improve outcomes with Improve outcomes with mixed data sets.mixed data sets.


Isatis Isatis –– Uniform ConditioningUniform Conditioning

Objectives:Objectives:

Estimate the distribution of smaller SMU blocks Estimate the distribution of smaller SMU blocks within a larger kriged panel. All that is needed is:within a larger kriged panel. All that is needed is:

•• kriged panel grades, andkriged panel grades, and

•• semivariogramsemivariogram


Isatis Isatis –– Uniform ConditioningUniform Conditioning

……………….and RUN!!!!.and RUN!!!!””

ISATIS inputs for UC:ISATIS inputs for UC:

►► Require Kriged ModelRequire Kriged Model►► Panel Panel AnamorphosisAnamorphosis (SV)(SV)►► Block Block AnamorphosisAnamorphosis (SV)(SV)►► CutCut--off Grades requiredoff Grades required


Isatis Isatis -- NonNon--Linear ResultsLinear Results

UC RESULTS - SW Grade COG Grade

Tonnes COG

Tonnes (No. blocks)

Tonnes (% of tot. blks)

M{0.0} 0.58 T{0.0} 6,820 M{0.5} 0.83 T{0.5} 3,496 51% M{0.6} 0.93 T{0.6} 2,631 39% M{0.7} 1.03 T{0.7} 1,934 28% M{0.8} 1.14 T{0.8} 1,406 21% M{0.9} 1.25 T{0.9} 1,019 15% M{1.0} 1.36 T{1.0} 741 11% M{1.2} 1.58 T{1.2} 401 6% M{1.5} 1.91 T{1.5} 170 2% M{1.7} 2.12 T{1.7} 100 1% M{2.0} 2.43 T{2.0} 46 1%


Isatis Isatis -- Global Change of SupportGlobal Change of Support

DiscreteDiscrete GaussianGaussian Global Change of Support (GCOS)Global Change of Support (GCOS)

Isatis FlavourIsatis Flavour



Objectives of DiscreteObjectives of Discrete GaussianGaussian global change of global change of support (GCOS):support (GCOS):

►► Rigorous change of support for any SMU size.Rigorous change of support for any SMU size.►► GCOS results used to verify UC postGCOS results used to verify UC post--processing results.processing results.



GCOS:GCOS:

►► SemivariogramSemivariogram►► SMU sizesSMU sizes

►► Rigorous change Rigorous change of supportof support


GCOS GCOS -- UC CheckUC Check

2.0 1.9 1.8 1.7 1.6 1.5

1.4 1.3

1.2 1.1

1.0 0.9

0.8 0.7

0.6 0.5

0.4 0.3

0.2 0.1 -

2

1.7

1.5

1.2

10.9

0.80.7

0.60.5

-

0.0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80 90 100

Tonnes above Cutoff (%)

Gra

de a

bove

Cut

off (

g/tA

u)

GCOS: ( SMU size )

UC Estimate ( SMU size ) GT curve


Isatis Isatis -- UC Check using GCOSUC Check using GCOS

-3%2623-5%0.880.84Montana

+1%2829-17%0.990.82Panique

+6%3238-17%0.990.82Bin Star / Doris

+2%3436-9%0.980.90Main Vein / Libra

+2%2729-6%0.970.91Colorado

+2%2628-6%1.101.03Holy Moses / Basalt

TonnesDifference

(%)

GCOSTonnes

(% t)

UCTonnes

(% t)

GradeDifference

(%)

GCOSGrade(g/t Au)

UCGrade(g/t Au)

Tonnage AnalysisGrade Analysis

Deposit


Masbate StudyMasbate StudyLessons learned:Lessons learned:

►► domaining required in areas of high grade contrastsdomaining required in areas of high grade contrasts

►► domaining reduces local misclassification of ore and wastedomaining reduces local misclassification of ore and waste

►► domaining significantly alters grade tonnage curve domaining significantly alters grade tonnage curve

►► domaining results in higher grades and lower tonnesdomaining results in higher grades and lower tonnes

►► domaining can improve project economics e.g. NPVdomaining can improve project economics e.g. NPV


Masbate StudyMasbate Study

Project Enhancements:Project Enhancements:

►►Improved mining selectivity.Improved mining selectivity.►►Improved head grades, increased ore Improved head grades, increased ore

tonnes.tonnes.►►Increased value of resource.Increased value of resource.


Masbate StudyMasbate Study

IsatisIsatis Contribution:Contribution:

►►ImprovedImproved krigingkriging parametersparameters►►Application of nonApplication of non--linear techniqueslinear techniques►►Identified additional value in the stockworkIdentified additional value in the stockwork►►GCOS technique increased confidence in GCOS technique increased confidence in

nonnon--linear results.linear results.


IsatisIsatis

►►Easy to useEasy to use►►Integrates with all major geological softwareIntegrates with all major geological software►►Fast processing of dataFast processing of data►►GeostatisticalGeostatistical tools improve estimatestools improve estimates►►Increases confidence in resultsIncreases confidence in results

isatis - geovariances.com · boundary models. page 18 m i n i n g associates masbate geology...

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