Putting the GEO back in front of GEOmetallurgy A Cradle to Grave unifying link across the exploration and mining value chain

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Ilene Johns
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Potma Scott Halley, Andrew Scogings, Serik Urbisinov, Carl Brauhart,

1 Putting the GEO back in front of GEOmetallurgy A Cradle to Grave unifying link across the exploration and mining value chain Warren Potma Scott Halley, Andrew Scogings, Serik Urbisinov, Carl Brauhart, Nikita Sergeev, Angela Escolme Thanks, also, to Hot Chili Ltd. 22/02/2018

clients in the global minerals industry.

2 About CSA Global CSA Global is a leading mining, geological, technology and management consulting company which provides high quality solutions to our clients in the global minerals industry. Our staff include geologists, mining engineers, project managers, data management professionals and technical personnel.

3 Integrated GEOmetallurgy & 3D Mineral System Modelling Case for early implementation Inputs Open-minded working hypotheses Informed Observational Geology 4 Acid ICP-MS Geochemistry Alt n-pathfinder-litho-geochem Applications Ground Selection Quantify strategic decisions Exploration Greenfields brownfields mine - shoot Impact Cradle to Grave Unifying link across Mining Value Chain Calculated Mineralogy Mineralogy: Spectral, Petro & XRD Geophysics/Petrophysics Appropriate Quantitative Mapping & DH logging data capture Structure, MagSus, Density, pxrf, Acid Fiz, Hardness, quality photography... High quality data management Live (implicit) 3D models Simplified fit-for-purpose 3D models Lithology Mineralogy Payable/Deleterious Elements - Weathering Rock Mass 3D Modelling Litho-structural Alteration - Resources Metallurgy Quantitative 3D Mineralogy Truly Representative Met. Composites Mine Design Rock Mass Classification Geotech Blasting - Scheduling Process Design Optimisation Scheduling - Blending Environmental Acid Mine Drainage Mine Closure Philosophy: to provide critical knowledge EARLY in project lifecycle for maximum value Costs savings at every stage from improved decision making Early warning of fatal flaws and opportunities W. Potma: RIU Explorers Conference 22/2/2018

GEOmet Value Proposition Can you afford NOT to do it?

stage: <10% of total project cost Hangingwall stratigraphy Projected position of massive sulphide Blind VMS

Cu is in chalcopyrite; restricted to sparse veinlets. 1km VMS horizon Sb is in the lattice of the pyrite.

4 GEOmet Value Proposition Can you afford NOT to do it? (from the earliest stage) 4A ICP-MS Geochem, Spectral, quantitative logging: Surface sampling & mapping stage: <10% of total project cost Hangingwall stratigraphy Projected position of massive sulphide Blind VMS example: Value of low level multi-element pathfinder geochem. Footwall stratigraphy VMS stringer zone. Cu is in chalcopyrite; restricted to sparse veinlets. 1km VMS horizon Sb is in the lattice of the pyrite. Maps the footprint of the stringer zone. If assays were limited to Cu, Pb, Zn this target would have been missed. (Halley 2016) W. Potma: RIU Explorers Conference 22/2/2018

Surface sampling & mapping stage: <10% of total project costs Exploration

detection limit 0.01ppm Bi_ppm ICP-MS 0.01ppm Det.

Halo Blind Porphyry Cu deposits 300m below surface Outcropping Porphyry Cu

5 GEOmet Value Proposition Can you afford NOT to do it? (from the earliest stage) 4A ICP-MS Geochem, Spectral, quantitative logging: Surface sampling & mapping stage: <10% of total project costs Exploration drilling stage: <2% of total project costs Bismuth assays by ICP-MS, detection limit 0.01ppm Bi_ppm ICP-MS 0.01ppm Det. Limit Phyllic Alteration Halo SWIR Spectral Mineralogy Phyllic Alteration Halo Blind Porphyry Cu deposits 300m below surface Outcropping Porphyry Cu deposit Bi_ppm ICP-AES 5ppm Det. Limit Blind Porphyry Cu 1km Outcropping Porphyry Cu Bismuth assays by ICP-AES, detection limit 5ppm (Halley 2016)

6 GEOmet Value Proposition Can you afford NOT to do it? (from the earliest stage) 4A ICP-MS Geochem, Spectral, quantitative logging: Surface sampling & mapping stage: <10% of total project costs Exploration drilling stage: <5% of cumulative project costs Resource Definition stage: <<0.5% of cumulative project costs Optimised drill design & sampling protocols - reduced costs Better 3D geological and mineralisation models increased confidence Better discovery/resource extension outcomes more ore Improved MRe confidence & Resource classification outcomes GEOmet model available pre-scoping Study time saving Early warning of any fatal flaws (and opportunities) W. Potma: RIU Explorers Conference 22/2/2018

7 GEOmet Value Proposition Can you afford NOT to do it? (from the earliest stage) Productora Chilean Coastal Range Cu-Mo-Au example: Integrated GEOmet approach delivered 3D mineral system model which: identified problems with existing geological model changed exploration focus, and targeting rationale led to 3 new discoveries & Resource increases from ~100Mt to ~260Mt, 90 Mt maiden Reserve Ultimately led to maiden porphyry copper discovery (Alyce PCD) 500m west of Productora breccia in 2014 & identification of 7km long porphyry geochem trend Directly impacted PFS outcomes W. Potma: RIU Explorers Conference 22/2/2018

8 East-dipping Habanero discovery Alt n model indicated there should be more Cu, and it looks like its East-dipping?! Habanero Discovery Productora Central Pit Area Section mN Preliminary Central Pit Design 72m@ 0.7% Cu, 36ppm Mo Early % Cu mineralisation +0.1% Cu mineralisation OPEN W. Potma: RIU Explorers Conference 22/2/2018

East-dipping Habanero discovery 181m @ 1.0% Cu, 0.2g/t Au, 173ppm Mo!

1g/t Au, 207ppm Mo Preliminary Central Pit Design 100m@ 1.0% Cu, 0.

9 East-dipping Habanero discovery 1.0% Cu, 0.2g/t Au, 173ppm Mo! Habanero Discovery Productora Central Pit Area Section mN 49m@ 1.0% Cu, 0.1g/t Au, 207ppm Mo Preliminary Central Pit Design 100m@ 1.0% Cu, 0.3g/t Au, 108ppm Mo 181m@ 1.0% Cu, 0.3g/t Au, 173ppm Mo (open to end of hole) including: 71m@ 1.6% Cu, 0.4g/t Au, 229ppm Mo Oct % Cu mineralisation +0.1% Cu mineralisation OPEN W. Potma: RIU Explorers Conference 22/2/2018

10 Metallurgist: Feasibility Study Time.. I want a representative bulk sample.. please BOCO TOFR Potassic Tourmaline Breccia Cu-Au-Mo Ore: Majority of ROM ore Variable hardness, density & abrasiveness K-spar cement dominant - dense and hard Tourmaline breccia - soft, friable, abrasive Highly variable weathering ox-trans-fresh? Strong localised structural/breccia controls (GC complexity)

11 GEOmet domain considerations BOCO TOFR Habanero Ore Zone: Anomalously E-dipping (Mining) Very high Cu grade (Mill feed variability) Locally high pyrite content (AMD) Relatively soft (Low BWI) Sericite + Clays (Float sliming?) Clay-rich shears & foliation (Geotech)

12 GEOmet domain considerations BOCO TOFR Advanced Argillic Domain: Silica-Clay (+/- sericite/pyrite) Dominantly waste Pit high-wall Sharp E-dipping contact with HG ore (Mining) Deep weathering highwall geotech Localised clay-rich shears/foliation (Geotech) Very hard silicification (Blasting/Drilling) Abundant cavities in oxide zone (Water!)

13 GEOmet Value Proposition Can you afford NOT to do it? (from the earliest stage) 4A ICP-MS Geochem, Spectral, quantitative logging: Surface sampling & mapping stage: <10% of total project costs Exploration drilling stage: <5% of cumulative project costs Resource Definition stage: <<0.5% of cumulative project costs Feasibility Study stage:<<0.01% of cumulative project costs with massive advantages: Reduced Risk: Infinitely more representative, accurate & precise Reduced Cost: well characterised remnant ½ core available for Met test work Minimised risk of rework or surprises Reduced Time: All the GEOmet characterisation & modelling is complete before Feasibility, thus well informed Met test work can commence immediately W. Potma: RIU Explorers Conference 22/2/2018

14 GEOmet of a complex skarn Client Requirements: Deliver the least complex 3D geometallurgical domain model possible for a complex Sn-fluorite-Cu-W Skarn deposit that: is representative of the critical domains affecting processing, including: lithological, alteration and ore mineralogy; oxidation and weatheringrelated mineralogy; hardness; abrasiveness; grainsize and textural characteristics has quantitative constraints sufficient to underpin confidence in an Indicated classification for the fluorite MRe adequately informs metallurgical sampling strategy A reliable, quantitative, 3D GEOmet model is required to underpin confidence in the Feasibility Study for this perceived marginal Resource W. Potma: RIU Explorers Conference 22/2/2018

GEOmet Protolith vs Logging CLY NW SAN WCR-CLY LOAM WCR_GRN SE WCR-RUBB MTS_AND ORIG-TIRR Clients Geological Model: based on detailed logging MTS_GL_3_1 MTS_MG GRN 150m Simplified Protolith Model:

15 GEOmet Protolith vs Logging CLY NW SAN WCR-CLY LOAM WCR_GRN SE WCR-RUBB MTS_AND ORIG-TIRR Clients Geological Model: based on detailed logging MTS_GL_3_1 MTS_MG GRN 150m Simplified Protolith Model: based on quantitative lithogeochemistry focused on mineralogy that might impact processing Calcareous- Sediments Mafic Dykes Granite Mg Dolomitic- Sediments 150m W. Potma: RIU Explorers Conference 22/2/2018

16 Gangue Mineralogy Classification (alteration mineralogy) K-spar or Biotite Point Density Least Altered Granite Muscovite Least Altered Sediments & Mafics/Andesites Kaolinite W. Potma: RIU Explorers Conference 22/2/

Mineralogy_SH Relationship between Gangue

mineralisation is spatially correlated with

Skarn +/- Muscovite Lower grades occur with

Mineralogy requires further simplification

17 Mineralogy_SH Relationship between Gangue Mineralogy & Sn grade Highest-grade Sn mineralisation is spatially correlated with Quartz- Topaz-Kspar (and/or Biotite)- Mt Skarn +/- Muscovite Lower grades occur with Sericite- Chlorite-Magnetite Skarn Gangue Mineralogy requires further simplification for GEOmet W. Potma: RIU Explorers Conference 22/2/2018 Sn% 17

ICP- MS, XRD, SWIR, petrography and logging data), and is simplified to

18 Simplified 3D GEOmet Domain Classification The simplified GEOmet Domain Classification is based on gangue mineralogy characterisation (using ICP- MS, XRD, SWIR, petrography and logging data), and is simplified to focus on critical attributes that potentially impact processing and mining. 18

Simplified 3D GEOmet Domain Model Note the steeply plunging shoot geometry of the Qtz-Topaz Domain (right) Enveloped by the magnetite-rich Fe-Skarn Domain

19 Simplified 3D GEOmet Domain Model Note the steeply plunging shoot geometry of the Qtz-Topaz Domain (right) Enveloped by the magnetite-rich Fe-Skarn Domain which also dominates the northeastern end of the pit (below) The remaining volume comprises Calc- Silicate Domain rocks (not represented) GEOmet Domains 19

20 Client s detailed Geol. Model vs CSA GEOmet Domains NW CLY SAN WCR-CLY LOAM WCR_GRN SE WCR-RUBB MTS_AND Client s Geological Model: based on detailed logging ORIG-TIRR MTS_GL_3_1 MTS_MG GRN 150m Fe-Skarn Calc-Silicates Qtz-Topaz Simplified GEOmet Domains that impact processing & mining: based on quantitative gangue mineralogy Granite 150m W. Potma: RIU Explorers Conference 22/2/

21 Client s Geol. Model vs Quantitative Weathering Domains NW CLY SAN WCR-CLY LOAM WCR_GRN SE WCR-RUBB MTS_AND ORIG-TIRR MTS_GL_3_1 MTS_MG GRN Clients Geological Model 150m Intense Supergene Clay Domain Transitional-Oxide Domain Sulphide Domain Quantitative Weathering Domain Model 150m W. Potma: RIU Explorers Conference 22/2/

Fluorite pct from semi-quantitative XRD Client s estimated fluorite pct (all F in fluorite) Estimating Fluorite Grade using Ca:F ratios (fluorite stoichiometry) Fluorite constitutes ~20% of Resource

22 Fluorite pct from semi-quantitative XRD Client s estimated fluorite pct (all F in fluorite) Estimating Fluorite Grade using Ca:F ratios (fluorite stoichiometry) Fluorite constitutes ~20% of Resource value, but there is a problem Fluorite (CaF 2 ) = 51.3wt% Ca, 48.7wt% F Any point above the black line exceeds the maximum possible fluorite content based on Ca content Calc. max fluorite pct from fusion XRF F & Ca values Fluorite + Carbonate/Amphiboles W. Potma: RIU Explorers Conference 22/2/2018 Calc. max fluorite pct from fusion XRF F & Ca values 22

23 Relative abundance of (Acid Insoluble) Cassiterite vs Stannite (Acid Soluble) Cassiterite (blue) dominates the proximal high grade ore zones. Relative abundance of Stannite:Cassiterite increases away from the main ore, usually coincident with very low Sn grades. 23

24 GEOmet Impact Characterise more, characterise earlier & test less High value exploration outcomes Saves time & money throughout the project life cycle Improves spatial representivity & confidence in models Provides early warning of threats and opportunities Reduces risk, minimises errors & rework Skarn GEOmet work positively impacted fluorite MRe, Met test work programmes, plant design, pit optimisation and scheduling and demonstrated that most of the preliminary metallurgical test work (predating the GEOmet project) was essentially wasted (Client s independent consultant metallurgist) W. Potma: RIU Explorers Conference 22/2/2018

25 Integration Communication - Collaboration Integrated GEOmetallurgy & 3D Mineral System Modelling Inputs Cassiterite (blue) dominates the proximal high grade ore zones. Open-minded working hypotheses Informed Observational Geology 4 Acid ICP-MS Geochemistry Alt n-pathfinder-litho-geochem Calculated Mineralogy Mineralogy: Spectral, Petro & XRD Geophysics/Petrophysics Appropriate Quantitative Mapping & DH logging data capture Structure, MagSus, Density, pxrf, Acid Fiz, Hardness, quality photography... High quality data management Live (implicit) 3D models Simplified fit-for-purpose 3D models Lithology Mineralogy Payable/Deleterious Elements - Weathering Rock Mass W. Potma: RIU Explorers Conference 22/2/2018 Relative abundance of Stannite:Cassiterite Acid Mine Drainage Mine Closure increases away from the main ore, usually coincident with very low Sn grades. Applications Ground Selection Quantify strategic decisions Exploration Greenfields brownfields mine - shoot 3D Modelling Litho-structural Alteration - Resources Metallurgy Quantitative 3D Mineralogy Truly Representative Met. Composites Mine Design Rock Mass Classification Geotech Blasting - Scheduling Process Design Optimisation Scheduling - Blending Environmental 25 Impact Cradle to Grave Unifying link across Mining Value Chain Philosophy: to provide critical knowledge EARLY in project lifecycle for maximum value Costs savings at every stage from improved decision making Early warning of fatal flaws and opportunities

Potma: RIU Explorers Conference 22/2/2018 CSA Global GEOmet Team: Warren Potma Scott Halley Andrew

26 Putting the GEO back in front of GEOmetallurgy: Can you afford NOT to do it? For more information please contact: Follow Us On: W. Potma: RIU Explorers Conference 22/2/2018 CSA Global GEOmet Team: Warren Potma Scott Halley Andrew Scogings Serik Urbisinov Carl Brauhart Nikita Sergeev Travis Murphy Fede Cernuschi Jamie Robinson + many additional specific commodity experts

DISCOVERY OF MINERALISED PORPHYRY & MAGNETITE-COPPER-GOLD AT KAMARANGAN

DISCOVERY OF MINERALISED PORPHYRY & MAGNETITE-COPPER-GOLD AT KAMARANGAN MEDUSA MINING LIMITED ABN: 60 099 377 849 Unit 7, 11 Preston Street Como WA 6152 PO Box 860 Canning Bridge WA 6153 Telephone: +618-9367 0601 Facsimile: +618-9367 0602 Email: admin@medusamining.com.au Internet: