The Integra « Gold Rush Challenge »: Impacts from hard data management through resulting exploration targets ranking

Francine Fallara, P.Geo., M.Sc. A.,

Stéphane Faure, P.Geo., Ph.D. and Guilhem Servelle, P.Geo., M.Sc.

Earth Modelling Forum 2016

Montreal, Quebec

October 3rd, 2016 Consultants – Mine - Exploration

The Integra « Gold Rush Challenge »:

Impacts from hard data management through

resulting exploration targets ranking

2The Integra « Gold Rush Challenge » Case Study

«Gold Rush Challenge» Objectives

Increase rapidly their chance in finding the next

major gold deposit discovery within the Sigma-

Lamaque gold properties in Val-d’Or, Québec by:

1. Implementing one of the largest organized

crowdsourcing analytical challenge ever

created in the mining industry

2. Opening it to worldwide individuals and

organizations

3. Marketing the challenge through financed

sponsoring


«Gold Rush Challenge» AOI - Mineralized ZonesSigma-Lamaque Mines

75 years producing > 9 Moz. Au

Sigma-Lamaque Mill and Mine

Complex are located directly east of

the city of Val-d'Or in the Province of

Quebec, Canada


InnovExplo Mandate

InnovExplo major roles, before and after the «Gold Rush Challenge»,

are presented in three main phases:

Historical Hard Data Compilation and ManagementPhase 1

Phase 2

Phase 3

Resulting Targets Validation and Classification

Resulting Targets Ranking and Querying

171 302 files of

various types

stored

on external drives

26 080 mine plan

levels and sections

(image format)

2 boxes of various

digital supports

(CD, 3.5 inches

disks and tapes)


Hard Data Integration Methodology1. Scan, compile, merge and unify: Several local grids,

scales and elevations within one chosen coordinate

system (UTM nad 83 Z18)

2. Digitalize the 2D polylines of the digital historical geo-

referenced plan levels and sections images

3. Construct the 3D Sigma-Lamaque mines

developments (pit, shafts and drifts)

4. Model the 3D Sigma-Lamaque mines stopes

5. Combine various digital databases from historical

logs (PDF) and spreadsheet files (Excel, Drill-A,

Prolog)

6. Collect all available diamond drill hole (DDH) assays

7. Compile, homogenize and simplify the DDH

lithological markers

Phase 1

InnovExplo

homogenized the

historical data

archived in the

Sigma-Lamaque

mines vaults

InnovExplo

produced a 6-

terabytes hard

drive database


Historical 3D Digital Hard Data Integration

Pits, Shafts and Developments

3D Construction3D Stopes

Underground Geology

and Veins Drift Mapping

Phase 1

Developments draped on

surfaces modelled from the 2D

polylines digitalized on the geo-

referenced plans and sections

2D polylines digitalization of the geo-referenced plan levels and sections

3D stopes surfaces from the 2D digitalized polylines

3D Surfaces from digitalized polylines:

Lamaque Mine: Main mineralized plugs

Sigma Mine: 43 production veins7The Integra « Gold Rush Challenge » Case Study


3D Mineralized Zones DDH Entry: Trace DDH Entry: Assays

3D DDH Trace: 36 830Validate various types of spreadsheet files (Excel, Drill-A, Prolog)

Build the DDH database: Collars position and deviation tests

DDH Assays: 16 055712 339 assays entries and validation

Phase 1



Data entry of simplified geological lithologies

along the DDH (110 857 entries)

Phase 1


« Top 21 » Exploration Targets Synthesis

InnovExplo Resulting Targets Validation and Classification:Phase 2

• Define ranking methods based on several key criteria for 561 selected gold exploration

targets interpreted by the « Top 21 » Gold Rush participants.

VALIDATION

Review

participants’

reports using

an unbiased

approach

CREATION

Build 2D and

3D objects for

each resulting

targets (x, y, z)

in a common

3D model

INTEGRATION

Integrate

participant’s

interpretations

(if available) in a

common 3D

model

CHARACTERIZATION

Generate a 2D and

3D potential

ranking map based

on the targets

characterization

synthesis

classifications

Step 1

Step 2

Step 3

Step 6

CLASSIFICATION

Produce an

exhaustive

exploration

targets synthesis

classification

table

Step 4

QUERYING

Recommend

the “best” of the

“best” resulting

« Top 21 »

exploration

targets

Step 5Phases

2-3

Phase

3


Exploration Targets Synthesis: Reports Reviews

Participants’ approaches can be summarized in 5 categories:

2D and pseudo-3D structural interpretations and regional corridors

2D and 3D geophysical and structural models

Pseudo-3D targets based on geological/metallogenical models

3D estimated resources zones (mine vicinities)

Data-driven and Knowledge-driven approaches

Phase 2 InnovExplo Targets Validation and Classification

• The evaluation was unbiased without knowing the authors, judging and/or discriminating any new

geoscientific interpretations and approach used for the resulting targets.

Step 1



InnovExplo Targets Validation and Classification• The evaluation was unbiased without knowing the authors, judging and/or discriminating any new


Participants’ Approaches:

1. 2D and pseudo-3D structural interpretations and regional corridors

Team 64: Riedels model: Pseudo-3D

C-Shears Triangle Deeps and South

Triangle. 2D geophysical lineaments

interpretations in terms of Riedel

(very focused on one type of

structure). They state in their report: «

Many of these features are very

subtle to identify and may take a

trained eye ore even a touch of

imagination ».

Step 1

Phase 2






2. 2D and 3D geophysical and structural models

Team 86: 3D mineralized vein clusters

containing several individual auriferous veins

including a detailed analysis and 3D modelling

of multiple feeder faults in the well-drilled #4

Plug. A 3D model of the Main Lamaque diorite

to compare it with the mineralized clusters

distribution at Sigma, Lamaque, #5 Plug and

Parallel Zone. 3D model for the folded Main

Lamaque diorite and gold shoots. Interesting

and plausible model: Dextral compressional

flower structure; Folds (synclines and

anticlines), back-thrust faults and shear zones,

tilting. Size of individual deposits correlates

with the size of the hosting intrusions. The

dextral Manitou Fault could be the main

fault/fluid conduit?Step 1

Phase 2






3. Pseudo-3D targets based on geological/metallogenical models

Team 35: Simple depth and opening and ore shoot trend testing theories.

Deep target zones contours. New model: Trans-tensional tectonic regime with

eroded Timiskaming type sedimentary basin and intrusion emplacement

(plugs) followed by compression and mineralized veins (Flower structure).

Step 1

Phase 2






4. 3D estimated resources zones (mine vicinities)

Team 84: Resource estimation exercise: Assay

data validation, creation of solids, statistics

and block modelling completed with GEMs.

Includes: 1) 102 mineralized zones (capped at

25 g/t Au) modeled relative to Sigma-Lamaque

developments; 2) Composited assays at 3 g/t

Au and dataset used as a guide to capture

broader high grade core of the granodiorite (i.e.

two main plugs visually stood out and were

modeled: Bulk1 and Bulk2); 3) Existing

potential corridors modeled to extend up

plunge to surface linking with surface deeper

mine Sigma #45 zone with surface known

deposits; 4) Future prospect: Outline sub-

horizontal high grade veins and 3D plane of

one of the main sub-horizontal high grade vein

with granodiorite outlines projected on the

plane to show areas of higher favorability of

finding new high grade material.

Step 1

Phase 2



InnovExplo Targets Validation and Classification

• The evaluation was unbiased without knowing the authors, judging and/or discriminating any new



5. Data-driven and Knowledge-driven approaches

Team 38: 3D surfaces for first order

intrusions (I2J + I1C). 3D surfaces for

faults model (1st, 2nd and 3rd order).

Predictive bloc model (50x50x50m; 2500

m deep). Virtual reality (Oculus Rift) used

to extract new data trends. Team 38

stated « This approach is very similar to

that described in Fallara et al. (2006) in

which the data is integrated into a

GOCAD® “voxet” and a decision tree is

defined to reduce the target areas to a

manageable size ». Fallara et al. (2006)

had chosen for their examples a binary

logic (Yes/No) approach to illustrate the

queries strengths of the gOcad®

software. SGS Geostat chose the Wofe

(weights of evidence) approach to define

classes and ranking scores.

Step 1

Phase 2


Exploration Targets Synthesis: Integration

Roughly 800 digital files were sent with the « Top 21 » reports

Step 2

The majority of the « Top 21 »

resulting targets did not exist

as 3D digital objects and were

manually traced by participants

on their report’s figures

Phase 2


Exploration Targets Synthesis: Build Targets

Step 3

InnovExplo 3D Target Modelling Methodology

Build 2D and 3D objects for each resulting targets (x, y, z) in a common 3D model

Phase 2


Exploration Targets Synthesis: Classification

InnovExplo produced an exhaustive characterization systematically

based on thematic attributes excerpted from the « Top 21 » reports

Step 4

InnovExplo used this characterization

as a final ranking multiplication criteria

Phase 2

1:50 000 map of the 561 targets projected at the surface

The Integra « Gold Rush Challenge » Case Study

Exploration Targets Synthesis: 2D Potential MapInnovExplo generated a 2D potential ranking map established on the

characterization of the targets based on their interpretation approach

Step 5

19

Targets characterization by their interpretation

method:

1. Knowledge-driven

2. Data-driven

3. Conceptual (areas and geological corridors)

4. Geophysical

Phase 2

The Integra « Gold Rush Challenge » Case Study

Exploration Targets Synthesis: 3D Potential Cells

Perspective view looking East

Step 5

20

InnovExplo interpolated the targets 3D potential ranking characterization

based on their interpretation approach in a voxet regions cells

Au > 10 g/t (Sigma-Lamaque Assays )

« Top 21 » Gold Rush participants’ 530 targets

centroids

Targets characterization by their interpretation method:

1. Knowledge-driven

2. Data-driven

3. Conceptual (areas and geological corridors)

4. Geophysical

Geophysical

Data-DrivenConceptual

Knowledge-Driven

Phase 2

Perspective view looking EastPerspective view looking NE

Method - M1Step 5


« Top 21 » Targets Rankings: Regional-Scale Results

InnovExplo (IE) Mean Class Ranking (Method 1 – M1):

Phase 3

Method – M1

Au > 10 g/t (Sigma-Lamaque Assays )

« Top 21 » Gold Rush

participants’ targets

centroids, scaled with the

Mean Class Ranking

Targets regions painted with

the Mean Class ranking

(ranking_mean_class_IE)


Method – M2

Step 5



Method Approach Ranking (Method 2 – M2)

Method – M2Au > 10 g/t (Sigma-Lamaque Assays )



centroids, scaled with the

Method Approach RankingTargets regions painted with

the Method Approach Ranking

(ranking_method_reg)

Phase 3


Method – M3


Step 5


Exploration Ranking (Method 3 – M3)



centroids, scaled with an

Exploration Ranking

multiplied by the

InnovExplo Factor

Targets regions painted with

the regional Exploration

Ranking (ranking_GG_reg)

Method – M3Au > 10 g/t (Sigma-Lamaque Assays )

Phase 3

Perspective view looking NE

Method – M4


Step 5


Total Number of Target Intersections Ranking (Method 4 – M4)

Maximum possible number of

intersecting targets within a

cell is 10 for both the regional-

scale and mine-scale voxets

Phase 3

Perspective view looking NE Perspective view looking NE

Method – M4



Total Number of Target Intersections Ranking (Method 4 – M4)

Step 5

Method – M4

« Top 21 » Gold Rush participants’

targets total number of intersections,

from two to 10 possible intersections,

illustrated on the plan and sections of

the regional-scale voxetTargets regions painted with

the Total Number of Target

Intersections Ranking

(target_int_Number_reg)

Phase 3


« Top 21 » Exploration Targets Synthesis: Querying

Summary of 3D Queries based on Best Rankings PercentilesPhase 3

• 3D queries were defined using simple Boolean queries (Q) to identify the best theoretical targets of

the area based on:

Step 6Examples of 3D queries (Q) for the regional-scale and mine-scale voxets

Method

M3-

M3a-

M1-

M2-

Method

M3-

M3a-

M1-

M2-

Thresholds set systematically above the 75th percentile with the resulting total

remaining cells applied to both the regional-scale and mine-scale voxets

Targets 3D Queries: Regional-Scale Results

Summary of 3D Queries (Q): Q01 through Q05

Q01 = M3 cells Q02 = M3a cells Q03 = M1 cells Q04 = M2 cells

Results

Intersections

∩

Union

U

Q01’ = ∩ (Q02 -Q03-Q04) Q02’ = ∩ (Q02-Q03) Q03’ = ∩ Q04

Q05 = U (Q01-Q02-Q03-Q04) 27The Integra « Gold Rush Challenge » Case Study

Step 6

User-defined 3D queries to extract the

best of the best « Top 21 » Gold Rush

exploration targets

Phase 3

Distance to drill holes < 100 m

Mine-Scale Sections illustrating Rankings and Attributes


Step 6

North-South 1:10 000 cross-section (looking West) spaced at 100 ± 50 m

S N

S294 350 m.E.

Attribute 1

SigmaLamaque

MainPlugs

looking West

La

ma

qu

e S

ou

th

Sig

ma

-La

ma

qu

e a

nd

/o

rF

ou

rnie

r

0 m

100 m

Targets Intersections Ranking

S N

S294 350 m.E.

Method 4

Production Veins

SigmaLamaque

MainPlugs

Sig

ma

-La

ma

qu

e a

nd

/o

rF

ou

rnie

r

La

ma

qu

e S

ou

th

Production Veins

Phase 3

The Integra « Gold Rush Challenge » Case Study 29

Worldwide Brainstorming

Session

Multi-disciplinary

Ideas

Low-risk high return investment

tag

Motivated over 1,000 hours

of brainpower data crunching

Created a mega-database

interpretation

Rendered over 3,000 pages

reports + video submissions

Generated new and

outside the box

innovative approaches

and ideas for future

exploration programs

Donated generous cash

prizes

Received in exchange

(hopefully) their next big

gold discovery

« G

old

Ru

sh

Ch

alle

ng

e »

Imp

act

for

Inte

gra

Go

ldSession: Data ManagementQ1: Why does InnovExplo see value and/or impact in data management?

1 year investment return ?? Qualified as Au Moz ++ / ++ Production Years ??The Integra « Gold Rush Challenge » Case Study

Session: Data ManagementQ2: What business value or concerns are addressed?

30

TimeImpactAu Ounces

10 Moz

+ 0 Moz

+ ?? Moz

Technologies and

techniques

implementations

Production

> 500 highly

prioritized

quality

exploration

targets

75 yrs.

+ ? yrs.?

« Gold Rush Challenge »

+75 Years1932

ExplorationProduction 2010

Closure

InnovExplo produced 6 terabytes

historical hard database compilation

Future Exploration Program

? Future Production ?

20??

InnovExplo validated, ranked and

queried Top-21 targets

1 y

ea

r sp

an

Investing 1

year focused

on adding

the historical

hard data


Integra Gold Corporate,

workers and technical

team

« Gold Rush Challenge »

Participants

InnovExplo Team

Acknowledgements

Top

21

1

SGS Geostat

2

Data Miners

Science

The Integra « Gold Rush Challenge »: Impacts from hard data management through resulting exploration targets ranking