mineral exploration as a complex global challenge with...

Mineral Exploration as a Complex Global Challenge

with Numerical Modelling Solutions

Jean-Marc LulinAzimut Exploration Inc.

7th International Megaprojects Workshop: Theory meets Practice

Artificial Intelligence and Megaprojects

June 13, 2019

Global Mineral

Exploration

1) A complex challenge with low success rates

2) Advanced data processing as a potential breakthrough

3) Successes and pitfalls: Simplexity versus complexity

Azimut in Quebec, Canada

• Core business since 2003: Big Data analytics applied to mineral exploration alongside partnership development

• 31 partnership agreements, including Rio Tinto (3), Goldcorp (2), IAMGOLD (2), Hecla Mining (2) and SOQUEM (2) for a total of~$140 million in work commitments

• Discovery of 400+ mineral prospects as a direct result of Azimut’sproprietary targeting methodology (AZtechMineTM)

• Holder of the largest exploration portfolio in the province

• Quebec: One of the top mining jurisdictions in the world

What is Mineral Exploration Today?

• Global activity

• US$10.1 billion budget in 2018 (non-ferrous exploration)

• 3,300 companies: major, intermediate, junior, parastatal Source: S&P Global

A Complex Global Challenge

1) New mineral deposits must be discovered to sustain a modern world

2) Discovery chances are decreasing in mature mining districts

3) Significant potential remains in known districts but at greater depths, and in

remote and/or politically risky regions of the world

A multiparameter task involving many interrelated factors

that are not easy to predict

➢ Mineral deposits: Small objects unevenly distributed within the Earth’s crust:

Size, grade, 3D location

➢ Mid- to long-term metal prices

➢ Technology-driven needs

➢ Political, legal, fiscal, societal, environmental factors in

multiple jurisdictions

A Complex Global Challenge

1) Universal paradigm: Use of geoscientific databases in exploration, one of the main drivers for discoveries

2) Explosive growth of data production and availability, but declining discovery rates

…A paradox!

Two possible combined explanations:

➢ Increasing global maturity

➢ Too much data producing too many targets, without theright selection tools: Quantity is the enemy of quality

Mineral Exploration: A Global Challenge

Exploration success rates

1,000

1,000100

100

10

10

1

1

Miracle method 1 target:1 discovery

Oil industry 1:10

Mining industry

1:1,000

Number of

targets

Number of

discoveries

Poor use of too much data

= too many targets

= poor success rates

_________________________

Advanced data processing

= fewer but higher quality targets

= dramatically improved success rates

All rights reserved

Advanced Data Processing: A New Exploration Paradigm

1) Accurate data processing can significantly improve the discovery rates

2) Selecting the right targets at the initial exploration stage reduces the technical and financial risks

Example of Quebec

➢ One of the best geoscientific digital databases worldwide, covering almost the entire province – coherent, reliable, accessible

➢ Azimut conducted systematic predictive modelling for selected mineral deposits using proprietary big data expert system AZtechMineTM

Advanced Data Processing applied to Mineral Exploration

Type of Processing

Innovative statistical approach linking regional-scale parameters to a database of mineral prospects and deposits 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 parameters per pixel; 500 GB database

- Purely data-driven methodology

- Relies exclusively on measured numerical data over regular grids

- No patchy or local data

- No interpreted data

- No parameter weighting

- Automated procedures, but processing steps entirely controlled


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 comparable footprints that may represent valuable new targets

3) To be relevant, predictive modelling must retain the smallest surface area while capturing the largest number of deposits to be characterized


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

Geoscientific Data

- Multi-element geochemistry: Lake-bottom sediments, stream, till, soil, rocks- Geophysics: Magnetism, gravity, electromagnetism, radioactivity- Geology- Drilling- Remote sensing- Digital topography

Rock sampling &geological observations

1,031,800 points

Rock samples(299,773 incl. 23,257 from AZM)

Geological observations(731,991)

Data: MERN, Azimut

Processing: Azimut

Geochemistry Surficial SedimentsGovernment Surveys

500,000 samplesover 1.5 million km2

Sampling Points

Data: MERN, Azimut

Processing: Azimut

Lake-bottom sediments (150,635)

+ Azimut samples (17,165)

Stream sediments (230,224)

Soils (75,845)

Tills (44,877)

500 km

Percentile

Data: MERN, Azimut

Processing: Azimut

Copper Content in

Lake-Bottom Sediments

Copper

1

50

100

Percentile

Data: MERN

Processing: Azimut

Geophysical Data

Magnetism

(total field)

Diamond Drill Holes

147,506 holes for a total of

24,280 km of core

(60% of Earth’s circumference)

Drilling Data

Data: MERN, Azimut

Processing: Azimut

500 km

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 predictionand field validation

26 properties 10,157 claims 4,843 km2

PROPERTY PORTFOLIO IN QUEBEC

Major Results obtained by

Azimut and Partners since 2003

500 km

Copper

Gold

Uranium

PolymetallicUngava Bay uranium

province: Rössing-type

Rex Trend polymetallic

province: Au-Ag-Te-Bi-Cu-W-

Sn, IOCG & Intrusion-related

systems

Intrusion-related gold

mineralization in the

Eleonore mining camp

Prospects

Nantais Belt: Au, Ag, Cu, Zn

Surface area: 1,667,000 km2


Quebec-Scale Examples

Analyzed surf. area Results

Gold 1,169,473 km2 42.4% Au deposits captured within0.46% of the surface area

New targets also located within the 0.46%

Gold Potential Predictive Modelling

over the James Bay region: 224,430 km2

Dates: 2003, 2005, 2009, 2015 and 2016

Parameters: Lake-bottom sediment geochemistry

(30,060 samples), magnetism & gravity data

Azimut’s properties

Eleonore Mine

FootprintSlide 22

Munischiwan

FootprintSlide 23

500 km

Analyzed surface area

21

James Bay Predictive Modelling (2016)

Eleonore Mine Footprint

8 million oz of gold (250 t Au)

22

Munischiwan Property (Azimut – SOQUEM)

James-Bay Predictive Modelling (2016)

Discovery of the InSight Prospect (2018)

InSight Prospect

23


➢ Implementation of AZtechMineTM: Big data expert system applied to initial exploration targeting

➢ Extensive track record of field validation: Discovery of 400+ prospects, including district-scale gold, copper and uranium mineralized systems

➢ Well beyond the experimental stage!

➢ Designed by its users with fully understandable processing steps and results

➢ Geographically transferable: Can be applied whereverthe right database exists

Summary

1) Mineral exploration: Global challenge with partial solutions (regional to country-scale) to improve success rates

2) Large geoscientific databases: Complex and spatially clustered

3) Too many possible targets: Adequate predictive modelling must only retain the very best

Two main ways to proceed:

➢ Expert systems

➢ AI (machine learning, neural networks)

Simplexity versus Complexity

➢ Expert systems

- An expert defines the rules

- Reasoning and processing chains are entirely controlled

- Can be adjusted

- Final human interpretation and ranking

▪ White box

➢ AI (machine learning, neural networks)

- A computer creates the rules

- Gap between software engineer and end-user

- Internal processing steps not mastered, not known by the user

- Not always clear how the results have been produced

- Final human interpretation and ranking

▪ Black box

Simplexity versus Complexity

Common pitfalls in predictive modelling

➢ We learn something that we already know…No risk, but no upside

➢ We learn something that we cannot explain…Potential upside, but high risk

➢ Overfitting models with poor predictive capabilities

Expert systems appear more adapted to the exploration challenge with minimal conceptual bias

Overcome the complexity of large databases and the natural variability of mineral deposits by finding simpler generic solutions.

Simplexity: Berthoz, 2009

Munischiwan

Principaux projets

➢Elmer

➢Munischiwan

➢Pikwa

➢Eléonore Sud

Thank you !

Merci !ᓇᑯᕐᒦᒃ

ᑭᓇᓈᐢᑯᒥᑎᐣ

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