This talk describes the process of generating a 3D model of the Kevitsa (Finland) ore body through wavelet transform of geochemistry obtained from drill core. Tesselation is then used to determine an appropriate scale of study for the data and 3D modelling. Subtle signals are identified, while the effects of analytical noise are dampened through this process. A genetic model for ore body formation was also formulated due to the success of the data filtering process.

1. 3D Model of a Ni-Cu-PGE Ore Body
MINERAL RESOURCES
Margaux Le Vaillant, June Hill and Stephen J. Barnes
May 2017
The Kevitsa Cu-Ni-Au-PGE mine, northern Finland

2. Layered Intrusions
A simplified ore body model | Margaux Le Vaillant and June Hill2 |
• Repositories of some of the largest ore
bodies on earth of Cu, Ni PGE, Cr, V…
• Large intrusions presenting
compositional layering
 This layering gives us
information on the genetic
magmatic processes at
play…
 Need for a meaningful way
to visualise these variations
in 3D

3. Kevitsa Intrusion
A simplified ore body model | Margaux Le Vaillant and June Hill3 |

4. Kevitsa Ore
A simplified ore body model | Margaux Le Vaillant and June Hill4 |
plag
ol
px
sulf

5. Kevitsa 3D Model
A simplified ore body model | Margaux Le Vaillant and June Hill5 |
Variations within the intrusion poorly
modelled

6. Kevitsa 3D Model
A simplified ore body model | Margaux Le Vaillant and June Hill6 |
 But access to a gigantic assay database! (>92,000 analyses)
Variations within the intrusion poorly
modelled

7. Ore Classification
A simplified ore body model | Margaux Le Vaillant and June Hill7 |
Ni < 2% - ‘False Ore’
S < 0.5% - ‘Not Ore’
Ni > 2% and Pd > 1,500 ppm
‘Normal Ore – High Pd’
Ni > 2% and Pd < 1,500 ppm
‘Normal Ore – Low Pd’
Ni > 10%
‘High Ni-PGE Ore’

8. 3D visualisation
A simplified ore body model | Margaux Le Vaillant and June Hill8 |
Assay points visualised and classified individually / no ‘lumping’ or domaining

9. A simplified ore body model | Margaux Le Vaillant and June Hill9 |
3D visualisation
Need for an objective & fast simplification of the model in order to distinguish
the large scale variations from the small scale one
 Upscaling method using continuous wavelet transform (CWT) and
tessellation methods
WORKFLOW

10. Multiscale Boundary Detection
A simplified ore body model | June Hill10 |
Signal
Signalsmoothing
Increasingscale

11. Multiscale Boundary Detection
A simplified ore body model | June Hill11 |
Signal
Signalsmoothing
inflection point in signal:
best estimate of
boundary location
Increasingscale
many boundaries
few boundaries

12. A simplified ore body model | June Hill12 |
continuous wavelet transform
using
2nd derivative of Gaussian wavelet
(convolve wavelet with signal over range of scales)
(smooth & find inflection points)
Method
zero contours of
2nd derivative

13. A simplified ore body model | June Hill13 |
Multiscale Spatial Domaining
“Tessellation”
of
continuous wavelet transform
Method
involves
depth-correction of
zero contours

14. A simplified ore body model | June Hill14 |
signal Filter One scaleTessellation

15. A simplified ore body model | June Hill15 |
Combine
Domain
Boundaries

16. A simplified ore body model | June Hill16 |
Classify
Combined
Domains

17. Classification
by Domain
vs Interval
A simplified ore body model | June Hill17 |
Low S
High S, Low Ni
Mod Ni, Low PGE
Mod Ni, High PGE
High Ni
ORETYPES
IntervalDomain

18. Effect of
Filtering Level
A simplified ore body model | June Hill18 |
Low S
High S, Low Ni
Mod Ni, Low PGE
Mod Ni, High PGE
High Ni
ORETYPES
Weak filter Strong filter
50% 70%

19. Effect of
Filtering Level
A simplified ore body model | June Hill19 |
Low S
High S, Low Ni
Mod Ni, Low PGE
Mod Ni, High PGE
High Ni
ORETYPES
weak changes disappear
strong changes are preserved
Filter

20. 3D visualisation
A simplified ore body model | Margaux Le Vaillant and June Hill20 |
unfiltered

21. 3D visualisation
A simplified ore body model | Margaux Le Vaillant and June Hill21 |
70% Filtered

22. 3D visualisation
A simplified ore body model | Margaux Le Vaillant and June Hill22 |
Unfiltered
100

23. 3D visualisation
A simplified ore body model | Margaux Le Vaillant and June Hill23 |
70% Filtered
100

24. 3D visualisation
A simplified ore body model | Margaux Le Vaillant and June Hill24 |
Unfiltered

25. 3D visualisation
A simplified ore body model | Margaux Le Vaillant and June Hill25 |
70% Filtered

26. Interpretation
A simplified ore body model | Margaux Le Vaillant and June Hill26 |

27. A simplified ore body model | Margaux Le Vaillant and June Hill27 |
Interpretation

28. Genetic Model
A simplified ore body model | Margaux Le Vaillant and June Hill28 |
Interconnected sill sediment-complex choked with
country rock inclusions

29. Genetic Model
A simplified ore body model | Margaux Le Vaillant and June Hill29 |
Larger magmatic chamber

30. Genetic Model
A simplified ore body model | Margaux Le Vaillant and June Hill30 |
Freely convecting magma chamber

31. Genetic Model
A simplified ore body model | Margaux Le Vaillant and June Hill31 |

32. Conclusions
A simplified ore body model | Margaux Le Vaillant and June Hill32 |
• Consistent and objective reduction of the number of
units in each drill hole, and creation of a simplified
3D model of the orebody
 Insights on the genetic processes at play
• Advantages of automated domaining process:
 Consistent results over whole data base
 Time saving
• Domaining using CWT and tessellation results in
domains whose size is a reflection of the location of
major changes in the variable values – not fixed
length like in conventional compositing of drill holes

33. A simplified ore body model | Margaux Le Vaillant and June Hill33 |
Thank you!

34. A simplified ore body model | June Hill34 |

35. 35 |
S NiS PdIntervals Domains
Low S
High S, Low Ni
Mod Ni, Low PGE
Mod Ni, High PGE
High Ni
A simplified ore body model | Margaux Le Vaillant and June Hill
Making Sense of the Results