All-domain Anomaly Resolution Office U.S. Department of Defense (U) Case: “Eg...
PDAC 2016 presentation by Martini, Carey, & Witter
1. HYPERSPECTRAL CORE
IMAGING FOR
CHARACTERIZATION OF CU-
AU PORPHYRY
7 MARCH 2016
Brigette A. Martini, PhD & Ronell Carey, PhD
Corescan
Jeff Witter, PhD
Mira Geosciences
Presented at PDAC 2016
2. The mechanisms of Cu-porphyry formation (Harris and Golding, 2002; Richards, 2003; Sillitoe, 2010),
theories of location (Tosdal and Richards, 2001), prediction and identification of type mineral assemblages
(Lowell, 1970; Titley, 1982, 1993; Hedenquist at al., 1998; Seedorf et al., 2005; Halley et al., 2015), relative
size and footprint (both vertically and horizontally) of alteration (Sillitoe, 2000,2010; Kerrich, 2000), grade
in relation to size, age, lithology, location and fluid geochemistry (Singer, 1995; Cooke et al., 2005) have all
been profoundly studied in the last 40+ years
“But more fundamentally, however, we require better and more detailed documentation of geologic
relationships in porphyry Cu systems worldwide, at all scales from the thin section to the entire system,
and with greater emphasis on the regional to district scale…[we] must further emphasize the relative
timing of intrusion, brecciation, alteration and mineralization events…this geologic detail [will] hopefully
further clarify the localization and evolutionary histories of porphyry Cu systems as well as the
fundamental controls on large size and high hypogene grade.” (Sillitoe, 2010)
There are three goals:
1. To expand current resource (less risk and highest reward/margins)
2. To optimize current mine process (increasing margins by mining better ore)
3. Greenfield discovery including potential new districts (high risk – low success) ;
Porphyry Alteration
3. Porphyry alteration variables from hyperspectral imaging
-Assemblage identification
-subtypes Cu-Mo, Cu-Au
-Textures (veined, pervasive,
porphyritic)
-Paragenesis, vein selvages, cross-
cutting, overprints
-Sharpness of alteration boundaries
-Scaling from fine resolution (cm’s)
through to borehole scale (m’s)
through to entire deposit scales (km’s)
18. Porphyry: Sulfides
Bornite Py/Cpy Moly
Sericite
Py/Cpy
Calcite
Silica
Bornite
Moly
• It is possible to map sulfides in the
VNIR-SWIR spectral range
• However, unlike typical alteration
mineralogy spectra, sulfide signatures
are not unique and ambiguity
between sulfides can be a problem
• Massive sulfide has higher accuracy
than finely disseminated sulfides
Pyrite Spectral Signature
19. Assemblage – Alteration similarity across deposits (Cu-Mo)
Sericite
Sericite (Hi Xtal)
Kaolinite
Sulfide
Sericite + Chlorite
Chlorite
Montmorillinite
Phlogopite
Carbonate
Photo Class Sericite Ser. Wave Kaolinite Class Sericite Ser. Wave Kaolinite
Porphyry A Porphyry B
20. Porphyry alteration variables from hyperspectral imaging
-Assemblage identification
-subtypes Cu-Mo, Cu-Au
-Textures (veined, pervasive,
porphyritic)
-Paragenesis, vein selvages, cross-
cutting, overprints
-Sharpness of alteration boundaries
-Scaling from fine resolution (cm’s)
through to borehole scale (m’s)
through to entire deposit scales (km’s)
22. Textural Mapping: Pervasive v. Veined
Photo Class Ser. Wave Kaolinite Alunite Gypsum Sericite
Sericite (Hi Xtal)
Kaolinite
Alunite
Gypsum
Tourmaline
Low match
Mineral match
High match
~17m
26. Paragenesis: Cross-Cutting Relationships
Low match
Mineral match
High match
2212 nmMuscovite2196 nm
White mica composition index (~2200 nm position)
Increase in Na
(Paragonite)
Increase in K/Al
(Muscovite)
2196 nm 2212 nm
Fe substitution
(Phengite)
2185 nm 2225 nm
Porphyry A
Photo Class Phlog. Kaolinite Chlorite Sericite Ser. Wav.
Cu-Mo
27. Vein Halos
Low match
Mineral match
High match
Photo Class Sericite Ser. Wav. Kaolinite
2212 nmMuscovite2196 nm
White mica composition index (~2200 nm position)
Increase in Na
(Paragonite)
Increase in K/Al
(Muscovite)
2196 nm 2212 nm
Fe substitution
(Phengite)
2185 nm 2225 nm
Porphyry A
Cu-Mo
28. Vein/Fracture Halos
Photo
Class
Sericite
Ser. Wav.
Gypsum
Low match
Mineral match
High match
2212 nmMuscovite2196 nm
White mica composition index (~2200 nm position)
Increase in Na
(Paragonite)
Increase in K/Al
(Muscovite)
2196 nm 2212 nm
Fe substitution
(Phengite)
2185 nm 2225 nm
Porphyry A
Cu-Mo
30. Porphyry alteration variables from hyperspectral imaging
-Assemblage identification
-subtypes Cu-Mo, Cu-Au
-Textures (veined, pervasive,
porphyritic)
-Paragenesis, vein selvages, cross-
cutting, overprints
-Sharpness of alteration boundaries
-Scaling from fine resolution (cm’s)
through to borehole scale (m’s)
through to entire deposit scales (km’s)
Copper canyon
Photo Class Asp.Ser. Ser. Wav. Chl.
Cu-Au
31. Sharpness of Alteration Boundaries
Photo Class Gyp.Kaol. Tourm.Ser. Ser.
Wav.
Mont. Chl.
Sericite
Sericite (Hi Xtal)
Kaolinite
Alunite
Gypsum
Tourmaline
Low match
Mineral match
High match
Cu-Mo
32. Sharpness of Alteration Boundaries
Class
Sericite
Sericite (Hi Xtal)
Kaolinite
Alunite
Gypsum
Tourmaline
Low match
Mineral match
High match
Biotite/Phlogopite
Cu-Au
~992m
33. Sharpness of Alteration Boundaries
Photo Class
Sericite
Sericite (Hi Xtal)
Kaolinite
Alunite
Gypsum
Tourmaline
Cu-Au
~1148m
34. Porphyry alteration variables from hyperspectral imaging
-Assemblage identification
-subtypes Cu-Mo, Cu-Au
-Textures (veined, pervasive,
porphyritic)
-Paragenesis, vein selvages, cross-
cutting, overprints
-Sharpness of alteration boundaries
-Scaling from fine resolution (cm’s)
through to borehole scale (m’s)
through to entire deposit scales
(km’s)
35. Borehole-scale Alteration Domains: Cu-Au
Class Chlorite Sericite
Kaolinite
Alunite
Gypsum
Tourmaline
Low
match
Mineral match
High
match
Phlogopite
Sericite
~992m
37. <<WHITE MICA (PHENGITE),
HIGH XTAL WHITE MICA
PHLOGOPITE
+ CHLORITE (FE-RICH)
+ =
HIGHER CU-GRADE
Borehole-Scale Alteration Domains
~169m
38. Borehole-scale Alteration Domains: Cu-Mo
Photo Class Kaol. ChloriteAlunite Ser. Wav. Phlog. Mont.
Argillic Lithocap Potassic CoreOverprint
Low
match
Mineral match
High
match
~561m
39. Borehole-scale Alteration Domains -> Deposit Scale
Photo Class AluniteMont.
Low
match
Mineral match
High
match
Export to
downhole mineral
% logs for database
and 3D modeling
~561m
40. Assemblage ID: Mineral Point Logs
Consistent, high resolution mineral point logs reveal basic (and sometimes subtle) mineral assemblages
Alunite Atacamite GypsumAsp. (Sericite)
Argillic
41. Assemblage ID: Mineral Point Logs
Consistent, high resolution mineral point logs reveal basic (and sometimes subtle) mineral assemblages
Chlorite Mont.Phlog (Sericite)Asp.
Potassic
42. Deposit-Scale Alteration Domains: Alunite
Alteration % point data
brought into simple 3D
models (e.g. Gocad)
• Point data represents % of
minerals counted
downhole, in specific depth
intervals
• This model was created
with 1m interval data
which represents ~200,000
pixels/signatures per meter
of core
• Color of model spheres
relates to purity or
‘goodness’ of fit to verified
mineral spectral signatures
• Size of model spheres also
relates directly to purity of
the identified mineral
Cu-Mo
43. Deposit-Scale Alteration Domains: Aspectral
• Aspectral refers to
measured signatures that
lack spectral absorption
features
• They are related to either
non-included, crystalline
quartz OR un-altered
feldspars
• Spatial mapping of this
class is accurate – though
identification can be
ambiguous
• In this porphyry, most of
the aspectral class relates
to quartz (confirmed from
previous traditional
logging)
45. Deposit-Scale Alteration Domains: Carbonate
• While the chemistry of
carbonates is possible to
measure (e.g. dolomite v.
calcite, ankerite, siderite,
etc.), it is often useful to
lump the carbonate classes
in order to study gross
patterns in alteration
• Further delineations such
as crystallinity are also
possible
51. Deposit-Scale Alteration Domains: Phlogopite
• Discrimination between
phlogopite and biotite is
generally possible – though
in some cases difficult
• In general, the higher the
iron content (as measured
directly from the spectral
signatures) and the less
water detected – the more
biotitic the rock is
53. Deposit-Scale Alteration Domains: Sericite Chemistry
2212 nmMuscovite2196 nm
White mica composition index (~2200 nm position)
Increase in Na
(Paragonite)
Increase in K/Al
(Muscovite)
2196 nm 2212 nm
Fe substitution
(Phengite)
2185 nm 2225 nm
Porphyry A
54. Deposit-Scale Alteration Domains: Tourmaline
• Distinction between
tourmaline varietals is
possible – though
frequently of lesser
importance
• Typically, tourmaline is
lumped into a single class
55. Deposit-Scale Alteration Domains: RQD
• RQD data is derived using a
laser profiling system with
15 micron vertical
resolution
• Though very consistent and
accurate, automated RQD
data should be considered
carefully based on age and
condition of core
• Core that is old and/or
been moved frequently
may report different RQD
values than those derived
directly after drilling
• On-site deployment of
automated core-logging
during drilling solves this
issue
56. Deposit-Scale Alteration: Alunite ≈ QS
Potassic - Bi Quartz-Sericite (QS)
• Alteration ‘cylinders’
derived from traditional
core-logging data identified
by on-site geologists
• Hyperspectral alteration
(alunite) correlates to QS
code
59. Deposit-Scale Alteration Domains: Alun+Kaol (+Gyp)
Alunite+Kaolinite
(Gypsum)
• We can start to create initial
assemblage classifications and
model these relationships in 3D