Hunt Jp.pdf

An Exploration Targeting Toolkit

John Paul Hunt

Overview

• Targeting & Mineral Systems Science • Reducing Risk - Targeting Tools – Lithospheric Architecture Mapping – Mineral Chemistry – Rank Statistical Analysis

Acknowledgement Relevant authors are acknowledged throughout the presentation 2

An Evolving Economic Geology Perspective

Knowledge Framework for Mineral Exploration Targeting: The application of geological concepts, to collected datasets to make spatial prediction of ore occurrence

Basic geological research Mineral Systems Science

Information flow

Targeting science Mineral exploration business

Basic ore geology research Mineral Systems Science: - Define key processes - Map process to physical rock volume - Define processes that govern ore formation - Develop frameworks for evaluating relative endowment potential

Exploration technology development

(SEG WMS Exploration Manager’s Course, 2011)

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Targeting Cycle

5

Opportunity Cost

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A Mineral Systems Approach • Focus on Process not Characteristics

4 Main Components • Source • Pathway • *Barrier • Trap / Seal

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Translating Critical Processes into Mappable Criteria and Proxies

Mappable Criteria at Different Scales – Magmatic Ni

Scale - Regional characteristics

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Scale – Deposit characteristics

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Overview

• Targeting & Mineral Systems Science

• Reducing Risk - Targeting Tools – Lithospheric Architecture Mapping – Mineral Chemistry – Rank Statistical Analysis

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Continental Scale Targeting Key Concepts • SCLM geometry focuses plume melting events • Active TLFs provide focused melt transport into the upper crust • Deposit sites must be protected from major crustal thickening • Craton margins adjacent to Archean SCLM internal to (super)continents are the most favourable zones for ALL of these to occur Intracontinental Lithospheric Active Arc

Former Backarc Craton Margin

Craton Margin

Boundary

Depth Oceanic Lithosphere

(kilometres) Crust

0 100 200 300 400 500 600 700

XXX X X X X XX X X XXXX X X X X XX X X X X X X X X X XX X X X X X X XX

X XXX X XX X X

Continental Lithosphere

Altered Lithosphere Asthe nospheric Mantle

Plume impacted on base of lithosphere Begg, 2010

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Continental Scale Targeting SLCM Tectonothermal Age Map GLAMS (Global Lithospheric Architectural Mapping)

Begg etal (2010). Geosphere. v 5 no 1; p 23-50

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Continental Scale Targeting - ULDs

Raglan Voisey’s Bay

Thompson

Duluth

Sudbury

Begg, 2010

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Overview

• Targeting & Mineral Systems Science

• Reducing Risk - Targeting Tools – Lithospheric Architecture Mapping – Mineral Chemistry • Nd isotope Mapping • Zr as a Pathfinder

– Rank Statistical Analysis

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Cratonic Scale Targeting – Nd Isotopes • Nd model ages for low-Ca granites (emplaced 2.662.63 Ga) • Maps age of lithosphere from which granitic melts were derived • Ni sulphides (2.7 Ga) • Orogenic Au (2.67-2.63 Ga)

Nd model age data from Cassidy, 2006, In Blewett and Hitchman (eds), 2006

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Cratonic Scale Targeting – Nd Isotopes

EGP

SCLM blocks (ULDs)

Nd Model Ages

Zircon as Pathfinder for Porphyry Cu-Mo-Au Deposits

Lu, Loucks, Fiorentini, McCuaig, Evans, Yang, Hou, Kirkland, Parra-Avila & Kobussen (2016). Zircon Compositions as a Pathfinder for Porphyry Cu + Mo + Au Deposits. Soc Econ Geol, Spec Publ 19, pp 329-347. 19

CL images of zircons from fertile and infertile magmatic suites Fertile Suites • Bimodal Zr CL textures w/ unzoned cores & strongly oscillatory rims

Lu et al (2016)

Scale bars = 100 µm

Infertile Suites • Unimodal Zr CL textures w/ strongly oscillatory zonation or weakly zoned 20

Zircon 10,000*(Eu/Eu*)/Y vs. (Ce/Nd)/Y

Lu et al (2016) 21

Mo Content in Zircon

Cu-Mo(-Au)

Cu-Au

Lu et al (2016) 22

Overview

• Targeting & Mineral Systems Science

• Reducing Risk - Targeting Tools – Lithospheric Architecture Mapping – Mineral Chemistry – Rank Statistical Analysis

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District Scale Targeting – Rank Statistical Analysis Barberton Greenstone Belt

Sabie – Pilgrim’s Rest

Murchison

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Zipf’s Law

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Zipf’s Law – Quantifying Undiscovered Resource • Area under the curve represents opportunity

The rank ordered distribution of deposits describes the known endowment relative to the theoretical endowment • Above – over-estimated – incorrectly ranked – error • Below – poorly delineated – undiscovered resources – error

Example of rank-ordered distribution of Ni deposits of the Norseman-Wiluna Greenstone Belt in Au equivalent describing an inverse power law distribution. After Mamuse & Guj (2011)

Zipf’s Law – Size Expectation • Area under the curve represents opportunity •

Expect 10 deposits of 100,000 kg 10 deposits of 100,000 kg known

• Total expectation 3,980,000 kg Au • Actual 3,837,000 kg Au • Residual 143,000 kg Au / 3 % residual endowment Example of rank-ordered distribution of Ni deposits of the Norseman-Wiluna Greenstone Belt in Au equivalent describing an inverse power law distribution. Modified after Mamuse & Guj (2011)

Relative Prospectiveness Barberton Au (t)

Sabie Au (t)

Murchison Au (t)

670

330

125

1,155

425

150

Total Undiscovered

485

95

25

Undiscovered Major Deps

22

2

0

Proportion Undiscovered

42 %

22 %

17 %

Total Known Au

Total Zipf Expected

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• •

Which one to explore? What is the future for mature terranes?

Concluding Remarks • Targeting & Mineral Systems Science • Reducing Risk - Targeting Tools – Lithospheric Architecture Mapping – Mineral Chemistry – Rank Statistical Analysis – – – – – –

3-Part Assessment for Undiscovered Resources Mgt Fingerprinting for IOCG-Porphyry-Skarn TEDI Index for Differentiation Mapping Magnetotellurics for diamond area selection Mahalanobis Distance for Outlier Identification SOMs

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Thank You Alkaline rocks, Carbonatites and Kimberlites

Magmatic Ni Sulphide Deposits Begg etal (2010). Geosphere. v 5 no 1; p 23-50