Understanding and incorporating 2D data, whether from surface field work or underground mine mapping, should always be the starting point of an integrated and coherent 3D geologic model, especially for areas with great geometric contrasts. Without this valuable data, 3D modelling is essentially performed with blinders on, and its absence results in a model that is too theory-driven, and lacks input from geologists and “real” field data. Three-dimensional geologic models require complete, homogeneous and valid databases. The resulting 3D models are directly based on and rely on high-quality data. The data comprises both surface and underground observations. “Raw” or “hard” data should always be assigned more weight and act as rigid control points in 3D models. Hard data should always be distinguishable from interpreted data in 3D models. Investing the necessary time to learn how to homogenize and structure raw data in a rigorous way will be paid back during the 3D interpretation process. Once 3D models are completed, they should be used as an exploration tool, populating their cells with user-chosen properties. Both quantitative and qualitative properties can be interpolated throughout the cells of the 3D model for further querying and questioning. Thus, the extra benefit of 3D map models is their use as dynamic interactive tools to help define new mineral exploration targets at depth. A 3D map model is not a goal but a tool that should be dynamic, modified, questioned, shared and updated. Its future usefulness is determined by how well it can be utilized by a multi-disciplinary team of geologists, geophysicists, geochemists, engineers, metallurgists and environmental experts.

1. Robust 3D Geological Models:
Hard Data is Key
Francine Fallara, P.Geo., M.Sc.A.
3D Mapping Geology
Session VGP11A
Abstract 34841
Joint Assembly
May 4th, 2015
Montreal, Québec
AGU-GAC-MAC-CGU

2. 2
Objectives: Incorporation and use of hard data
Robust 3D Geological Models: Hard Data is Key
Coherent 3D geological models:
1. Understand the importance of:
a. Extracting relevant information from complete, homogeneous and valid
databases:
• Resulting 3D models accuracy = f (High-quality data)
b. Using regional hard data = f (Applications) = f (Scale):
• Project objectives, geological environments, exploration approach, etc.
• Regional, Camp, Mine, Greenfield, Underexplored, etc.
c. Assigning more weights to “Raw” or “Hard” data to use as rigid constraints
while building the resulting 3D geological model

3. 3
Introduction: Why 3D geological modelling?
Robust 3D Geological Models: Hard Data is Key
3D models are used to better understand the geology of a studied area:
a. One region can have a completely different geometry at depth vs its surface
outline
Surface
projection
Fallara, Rabeau, Cheng and De Kemp (2008)
DEM
Faults
2D to 3D transition

4. 4
Introduction: 2D to 3D transition using hard data
Robust 3D Geological Models: Hard Data is Key
Key hard data integration vs resulting coherent 3D geological models:
a. 3D models are initiated by the importation of elevation points (DEM) to create
the topographic surface:
• Topo surface accuracy = f (punctual and outcrop contours + DDH collars)
b. Geological field mapping, historical compilations and underground mapping
combines the following hard data (quantitative and qualitative):
• Outcrops with geological labels, structural measures, main lithological and
stratigraphic (marker horizons) contacts, faults and folds traces, mineralized zones,
drill holes markers, assays, lithogeochemistry, alteration indexes, facies textures,
physical rock properties, geophysics, etc.

5. 5
Introduction: 2D to 3D transition using hard data
Robust 3D Geological Models: Hard Data is Key
Fallara et al. (2006)
2Dto3Dtransition
Fallara, Rabeau, Cheng and De Kemp (2008)

6. 6Robust 3D Geological Models: Hard Data is Key
Berra et al. (2014)
Accurate 3D modeling must reproduce
key geological characteristics:
• Respect stratigraphy chronology and
spatial lithological unit relationships:
• Simplify lithological contacts to best link
2D and 3D data
• Reconciliation geological and
structural data
• Define structural domains and trends:
• Strike, Dip, Bedding
• Enhance regional structures with
geophysical maps
2D to 3D transition: Hard data uniformity analysis
Excellent correspondence
with measured field data and
underground mapping

7. 7
Case Study 1: 3D Regional Model
Very limited hard data constraints
Robust 3D Geological Models: Hard Data is Key
 2012: Acquired Rouyn Noranda Mining Camp: Canada's most established VMS districts
 Include: Horne Mine Complex area and 13 other former producers
 Bounded: Destor-Porcupine and Larder Lake-Cadillac fault zones (2 most productive gold
bearing structures in North America)
Color Coded Surface Geology imageDigital Elevation Model
Noranda Camp 3D Model

8. 8Robust 3D Geological Models: Hard Data is Key
 3D Regional Noranda Camp: 70.2 km X 45 km X 1.3 km
 2D geological contacts
 2D interpreted down dip geological contacts
gOcad© Model boundary (4 100 km3)
Surface Geology curves Surface and interpreted down dip
geology curves
Edited surface and down dip geology
curves
Noranda Camp 3D Model
Case Study 1: 3D Regional Model
Very limited hard data constraints

9. 9Robust 3D Geological Models: Hard Data is Key
 3D Regional Noranda Camp: 70.2 km X 45 km X 1.3 km
 2D fault contacts
 2D interpreted down dip fault contacts
Edited surface and down dip geology
curves with fault traces
Surface plan map with previously built
‘Central Camp’ model and fault traces
Noranda Camp 3D ModelgOcad© Model boundary (4 100 km3)
Surface Geology curves
Case Study 1: 3D Regional Model
Very limited hard data constraints

10. 10Robust 3D Geological Models: Hard Data is Key
 3D Geological contact surfaces
 Lithological Domains (regions) in Voxet
3D surfaces
Case Study 1: 3D Regional Model
Very limited hard data constraints

11. 11Robust 3D Geological Models: Hard Data is Key
1 • Rivière Mouilleuse (RIMO) property is located roughly
25 km NW of Rouyn-Noranda, in the Duprat Township
• RIMO: Greenfield exploration project
• Compilation and interpretation project:
(S. Poitras 2012-2015)
• As compilation advanced and the hard data was
re-interpreted the area’s 3D comprehension
changed
• The next slides summarizes the precious input of
hard data within a regional 3D geological model
Case Study 2: 3D RIMO area model
High hard data constraints

12. 12Robust 3D Geological Models: Hard Data is Key
 Years of detailed compilation integrated into a common platform
 Powerful querying and target generating capabilities in a mature mining camp
Falco Resources
Database
Historical
DDH*
Historical
Rock samples*
Total 16 585 45 929
Total length drilled (m) 4 133 045 NA
Total length sampled (m) 545 982 NA
Assays 256 300 17 066
Lithogeochemistry 47 914 32 682
*As of late April 2014
Unified platform with quantitative and qualitative
geological, geophysical and geochemical data
Integrated approach: 3D gOcad© models
Fallara et al. (2006)
Case Study 2: 3D RIMO area model
High hard data constraints

13. 13Robust 3D Geological Models: Hard Data is Key
Surface level -500 m level from the surface
-1000 m level from the surface -2000 m level from the surface
DDH density distribution
(200 m radius) and depth1 1
1 1
Case Study 2: 3D RIMO area model
High hard data constraints
RIMO area

14. 14Robust 3D Geological Models: Hard Data is Key
 Type: VMS±Au (Concepts and interpretations; S. Poitras 2012-2015)
Hard data compilation: Added roughly 10,000 samples (March 17th, 2014):
• Compiled thousands of metals and whole-rock analysis from historical statutory
reports and from private companies including geophysics, mapping and
prospecting works done between 1951 to today
New geological highlights for the rhyolite contact:
• Often marked by thin cherty exhalative horizon mineralized: PY-PO-CP-SP
• Capped by an undrilled exhalite strongly anomalous in zinc
• Possible distal VMS deposit at depth?
Spatial distribution of these new samples on the geological map:
• Helped to trace the felsic/mafic units contacts
• Traced a folded contour clearly showing both flanks to be underexplored: 2.4
km along strike not drilled on the northern flank
Case Study 2: 3D RIMO area model
High hard data constraints

15. 15Robust 3D Geological Models: Hard Data is Key
 Type: VMS±Au (Concepts and interpretations; S. Poitras 2012-2015)
Samples comparison of P. Riopel and L. Martin (2006):
Horne type felsic geochemistry study in the Noranda Camp noted that:
• RIMO’s felsic rocks have identical Horne Mine geochemical signatures
• prospecting works done between 1951 to todayCarbonate alteration (molar CO2/CaO) from CONSOREM 2005:
• RIMO’s felsic rocks have identical Horne Mine geochemical signatures
Latest geochronology study: Exchanged with J. Goutier
(McNicoll et al., 2014)
• Noted the felsic rocks of the RIMO area are older (≈2700 𝑀𝑎) than those from
the central camp and corresponds to the Horne Mine’s felsic units
Case Study 2: 3D RIMO area model
High hard data constraints

16. 16Robust 3D Geological Models: Hard Data is Key
 Type: VMS±Au (Concepts and interpretations; S. Poitras 2012-2015)
Questioned and re-interpreted the structural domains associated to Au:
• Western boundary = NW-SE Rivière Mouilleuse Fault (Smokey Creek Fault)
• Major Proterozoic units displacements: Proving hydrothermal activity over a long period
(McNicoll, Goutier et al., 2014)
• Southern boundary = Major NE-SW Hunter Creek Fault
Used historical reports to help understand the stratigraphy chronology:
Kanasuta River overfold anticline:
• Only literature reference (P. Verpaelst, 1986): Axial plane ENE plunging NE (about 30°)
• Stratigraphy: Oriented SW-NE to WSW to ENE (N050-070°) with a low sub-horizontal dip
to the SE
Tested the hypothesis of a sub-horizontal stratigraphy:
• Chose recent/well documented DDH (MB-94-19, MB-94-21 and MB-98-26; Cambior) on the southern flank
• Verified their existence on the field (2 out of 3 casing and the 3rd setting were found)
• Linked in 3D their rhyolite-andesite contact intersections: Attitude: N076.5° dipping SE 11.2°
• Searched and found multiple mapping reports observing varying dips from 10 to 80° SE (average 30°)
Case Study 2: 3D RIMO area model
High hard data constraints

17. 17Robust 3D Geological Models: Hard Data is Key
 Type: VMS±Au (Concepts and interpretations; S. Poitras 2012-2015)
Explained the poorly and/or un-interpreted MegaTEM anomalies:
Prior modelling had been defined based on a sub-vertical stratigraphy:
• Explaining why the historical DDH (DUP-04-08) had missed the anomaly "EM-
01A" which had been drilled "down-dip”; i.e. remained unverified
• New ground geophysical survey: Planned in SE portion of the overfold
anticline (optimized EM in-loop configuration for maximum coupling for sub-horizontal conductors)
Results of the EM in-loop survey outlined:
The chosen firm was not aware of these new interpretations on purpose making sure no biased
interpretations would be concluded.
• 17 EM conductors: Oriented NE (moderate to good)
• Most conductors were defined to be sub-horizontal with a SE dip
• Defined a new brittle faults network: NNW with apparent dextral movements
• Top conductors bodies are mainly at 50 to 200 m depths
Case Study 2: 3D RIMO area model
High hard data constraints

18. 18Robust 3D Geological Models: Hard Data is Key
 Type: VMS±Au (Concepts and interpretations; S. Poitras 2012-2015)
Metallogeny chronology synthesis based on new geological interpretations
and the latest geochronology study (McNicoll et al., 2014):
• RIMO gold = Horne Mine gold = Syn- to post-VMS
• Sulfurs in both systems are syn-volcanic
• Fe in the system favors Au precipitation: Au = Syn- to post-VMS
• Pierce (1933) thesis: also interpreted Au = Post-VMS mineralization
New interpretations of the hard data: Rivière Mouilleuse (RIMO) area:
• Will possibly lead to the discovery of a: New mine and/or a new mining camp
• The existing regional 3D geological model should be rebuilt taking into
considerations all these new interpretations:
• Local low dipping sub-horizontal stratigraphy vs regional theoretical sub-
vertical stratigraphy
• Several EM sub-horizontal conductors still remain undrilled
Case Study 2: 3D RIMO area model
High hard data constraints

19. 19Robust 3D Geological Models: Hard Data is Key
ConclusionsHarddata
Plays key role for accurate
3D geological models
Starting point of any integrated and
coherent 3D geologic model; especially for
areas with big geometric contrasts
Ensure 3D models are well
constrained to the hard data
3D geological interpretations should be
consistent with the geological hard data
Database structures should include
observed relationships between
different types of hard data
Keep clearly separated the hard data and
interpretations in 3D integrated geological
models
Case Study 2
RIMO area
Falco
Resources
Regional studies
Local differences
Invest and take the time to try
to find those local
« anomalies » in the hard data
Do not force preconceived
ideas and/or theoretical
interpretations
Built 3D models from surveyed
and measured hard data

20. 20Robust 3D Geological Models: Hard Data is Key
Conclusions
3Dmaps
If hard data is absent
3D modelling is essentially performed
with blinders on
Results theory-driven 3D models
lacking geologists input and “real”
field data observations
3D map models
Not a goal but a tool that should be
dynamic, modified, questioned, shared
and updated
Future value of this tool is defined as
how well a multi-disciplinary team of
geologists, geophysicists,
geochemists, engineers, metallurgists
and environmental experts will use itWard et al. (2012) Field surface data
Underground mapping
Drill holes

21. 21Robust 3D Geological Models: Hard Data is Key
Special thanks to the Falco Resources Exploration Team:
Michael Byron Vice President Exploration, Director
Stéphane Poitras Exploration Manager
Nancy Lafrance Exploration Geologist
Hughes de Corta Exploration Geologist
Stéphane Guenette Data Entry
Denis Lebreux Database Manager
Carmen Gervais Accountant
Acknowledgements
Perfect example of an exploration team investing
time to complete the database with historic data
compilations, field surveys and DDH…
Outside the box interpretations not confined to
well-known theoretical models
by applying unbiased data-driven approaches….
Best of luck for your future careers!