Azimut Exploration, Inc. · TSXV:AZM
Azimut Exploration — PDAC 2019
26 slides from Azimut Exploration's conference presentation, Mar 5, 2019
Slide 1: Predictive Modelling that Works
Predictive Modelling that Works Big Data Analytics applied to Country-Scale Exploration Jean-Marc Lulin, François Bissonnette and Marc Philippin Azimut Exploration Inc. PDAC, Toronto, March 5, 2019
Slide 2: Predictive Modelling that Works
Predictive Modelling 1) A Few Fundamental that Works Issues with Targeting 2) Processing Technology: AZtechMineTM 3) Practical Achievements
Slide 3: Azimut in Quebec, Canada
Azimut in Quebec, Canada • Core business since 2003: Big Data analytics applied to mineral exploration, alongside partnership development • 31 partnership agreements since 2003, including Rio Tinto (3), Goldcorp (2), IAMGOLD (2), Hecla Mining (2) and SOQUEM (2) • Discovery of 400+ mineral prospects
Slide 4: Key Facts
Key Facts TSXV: AZM • Company founded in 1986 • 53M shares outstanding (59.3M fd) – Never consolidated • Tightly held share structure: 65% of the shares owned by top shareholders: − 5% insiders − 29% institutional funds in Quebec − 31% several large corporate and private investors • $1.7M working ca
Slide 5: Property portfolio in Quebec, Canada
Property portfolio in Quebec, Canada Qassitugq (PGE, Cu, Au) Nantais (Au) Rex - Duquet (Cu, Au, Ag W, REE) Rex South (Cu, Au, Ag, W, REE) NCG (Cu, Au, Ag, W, REE) North Rae (U) Pontois (Au) Kaanaayaa (Au) Pikwa (Au) Dalmas (Au) Kukamas East (Au) Opinaca A (Au) Opinaca D (Au) Galinée (Au) Munischiwan
Slide 6: A Few Issues with Targeting
A Few Issues with Targeting ➢ Explosive growth of data production and its availability ➢ Declining discovery rates ➢ Universal exploration paradigm: Geoscientific data are one of the main drivers for discoveries but… ➢ Discovery rates do not correlate with this exponential data production Two possib
Slide 7: A Few Issues with Targeting
A Few Issues with Targeting Exploration success rates Poor use of too much data = too many targets Number of = poor success rates _________________________ discoveries Miracle method 1 target:1 discovery Advanced data processing 1,000 = fewer quality targets 100 Oil industry 1:10 = dramatically impr
Slide 8: Azimut’s Predictive Modelling
Azimut’s Predictive Modelling Supporting Concepts 1) Link regional geoscientific data to the mineral database to characterize the statistical footprint of specific deposit types / commodities 2) Convert the footprints of known deposits into discovery- probability maps that include unexplored but com
Slide 9: Azimut’s Predictive Modelling
Azimut’s Predictive Modelling Scale of Analysis Regional to country-scale analysis provides the best leverage to recognize the strongest and largest mineralized systems A large geostatistical database allows the right footprints to be discriminated against marginal or second-order targets Types of G
Slide 10: Sampling Points
Geochemistry Sampling Points Surficial Sediments Lake-bottom sediments (150,635) Government Surveys + Azimut samples (17,165) Stream sediments (230,224) 500,000 samples Soils (75,845) over 1.5 million km2 Tills (44,877) 500 km Data: MERN, Azimut Processing: Azimut
Slide 11: Azimut’s Predictive Modelling
Azimut’s Predictive Modelling Type of Processing Innovative statistical approach linking regional-scale parameters to the mineral prospects & deposits database to extract a reliable footprint for selected deposit types Quebec-scale processing: 87.5 million pixels; cell size: 200 x 200 m; up to 70 pa
Slide 12: Azimut’s Predictive Modelling
Azimut’s Predictive Modelling Target Ranking: Five Main Criteria 1) Footprint components 2) Strength, anomalous contrast 3) Size 4) Shape 5) Exploration history
Slide 13: Azimut’s Mineral Potential Modelling by Target Type (2003 to 2018)
Azimut’s Mineral Potential Northern Quebec Gold Modelling by Target Type IOCG (from 2003 to 2018) Ni-Cu-Co Uranium Quebec-scale processing: Rare earths 87.5 million pixels Cell size: 200 m by 200 m James Bay Gold Up to 70 parameters per Ni-Cu-Co Northern Quebec pixel Uranium 500 GB database Grenvill
Slide 14: Major Results obtained by Azimut and Partners since 2003
Prospects Major Results obtained by Azimut and Partners since 2003 Gold Copper Nantais Belt: Au, Ag, Cu, Zn Uranium Ungava Bay uranium Polymetallic province: Rössing-type Rex Trend polymetallic province: Au-Ag-Te-Bi-Cu-W- Sn, IOCG & Intrusion-related systems Intrusion-related gold mineralization in
Slide 15: Predictive Modelling that Works
Predictive Modelling that Works Data processing (for a defined region) leads to: ➢ Footprints of already known mineral deposits and prospects ➢ Comparable footprints of unexplored/underexplored sectors = new potential targets Field work leads to: ➢ Discoveries Strong correlation rates between predic
Slide 16: Predictive Modelling that Works: Quebec-Scale Examples
Predictive Modelling that Works Quebec-Scale Examples Analyzed surf. area Results Gold 1,169,473 km2 42.4% Au deposits captured within 0.46% of the surface area New targets also located within the 0.46% Uranium 1,245,759 km2 58.8% U deposits captured within 2.7% of the surface area New targets also
Slide 17: Gold Potential Predictive Modelling
Gold Potential Predictive Modelling over the James Bay region: 224,430 km2 Dates: 2003, 2005, 2009, 2015 and 2016 Parameters: Lake-bottom sediment geochemistry Eleonore South (30,060 samples), magnetism & gravity data Eleonore Mine footprint Munischiwan Analyzed surface area Azimut’s properties 500
Slide 18: Eleonore South Property JV (AZM– G – ER)
Eleonore South Property JV (AZM– G – ER) James-Bay Predictive Modelling (2003) Discoveries: JT (2006), Moni & Contact trends (2016-2018)
Slide 19: James Bay Predictive Modelling (2016) Eleonore Mine Footprint
James Bay Predictive Modelling (2016) Eleonore Mine Footprint
Slide 20: Munischiwan Property (Azimut – SOQUEM)
Munischiwan Property (Azimut – SOQUEM) InSight Prospect James-Bay Predictive Modelling (2016) Discovery of the InSight Prospect (2018)
Slide 21: Quebec-Scale Gold-Copper Potential
Quebec-Scale Gold-Copper Potential Predictive Modelling over 1,169,473 km2 Dates: 2009, 2015 Parameters: Lake-bottom geochemistry (111,018 samples), magnetism & gravity data Rex South Analyzed surface area Azimut’s properties 500 km
Slide 22: Rex South
Rex South Cu, Au, Ag, Te, Bi, W, Sn ➢ 18 mineralized zones ➢ 60 km cumulative length of target zones Copperton Zone Copperton Zone Very high High 3.5 km x 20 to 100 m; up to 9.56g/t Au, Low 82.7 g/t Ag, 9.28% Cu (grabs)
Slide 23: Uranium Mineralization
Uranium Mineralization (>0.05% U3O8) in Nunavik Pre-2006 North Rae Ungava Bay 2006-2009 (Azimut) 25 km South Rae Uran. potential Surf. area Very high 0.05% 1.05% Uranium Potential Predictive Modelling 1.66% over 1,245,759 km2 High Dates: 2005, 2015 Parameters: Lake-bottom sediment geochemistry (125,
Slide 24: All active properties
Practical Achievements from 2003 to 2018 (gold and copper-gold properties) Eleonore South Au AZM-Goldcorp-Eastmain Wabamisk Au AZM-Goldcorp Opinaca B Au AZM-Hecla-Everton Opinaca D Au AZM Munischiwan Au-Ag-Cu AZM-SOQUEM Pikwa Au-Co-Cu AZM-SOQUEM Pontois Au AZM-SOQUEM Galinée Au AZM-SOQUEM Rex Cu-Au-
Slide 25: Conclusion
Conclusion • Azimut Exploration: Pioneer in Big Data analytics applied to exploration with extensive track-record of field validation at the scale of Quebec – Not at the experimental stage! • Led to the discovery of 400+ prospects, including district- scale gold, copper and uranium mineralized syste
Slide 26: Thank You!
Munischiwan Thank You ! Principaux projets ➢ Elmer ➢ Munischiwan ➢ Pikwa ➢ Eléonore Sud