1518 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
GEOMETALLURGICAL OBJECTIVE,
MODEL
When considering using hyperspectral imaging as a poten-
tial solution for answering your geometallurgical questions,
it is important to consider your geometallurgical objective
and/or your geological, mineralogical, mineral process-
ing and metallurgical materials and models (Figure 2).
Eismann (2012) mentions that material spectroscopy is the
initial consideration in hyperspectral imaging (Figure 4).
As a non-destructive technology and one that can provide
in-situ measurements, hyperspectral imaging allows for
measurements that can be directly or indirectly linked to
geometallurgical objectives such as deposit/rock character-
ization, ore characterization, mineral concentrate, predic-
tion of metallurgical properties, and metal products.
Within deposit/rock characterization, imaging spec-
troscopy can help characterize the mineralogy and lithology
of the deposit including alteration patterns. Historically,
imaging spectroscopy has been used in regional and district
level remote sensing studies. More recently, imaging spec-
troscopy sensor solutions have been developed for desktop,
laboratory workbench, and full core box imaging solu-
tions. Mineralogical, alteration, and lithological models are
important to establish prior to implementing HSI as part
of a geometallurgical program. This model can be refined
through-out the mining life-cycle as more rocks are sampled
and measured as part of the program. Mineralogical quan-
tification, geochemistry can be linked to imaging spectros-
copy by a couple of methods. Traditionally, this has been
done by using Partial Least Squares Regression or similar
methods (Hecker, Dilles et al. 2012). Mineral textures and
their 2-dimensional nature can be determined using hyper-
spectral imaging however, these textures are understudied
in imaging spectroscopy research. Hardness, rock strength,
ultrasonic velocity, bulk modulus, shear modulus, dielectric
properties and other physical, mechanical, and chemical
properties of rocks can be predicted using PLS-R or similar
techniques or calculated from mineral abundances (Riley
in prep).
OBSERVABLE /DETECTABLE
PHENOMENA
Coulter, Zhou et al. (2017) produced a tables for silicates
and non-silicates highlighting mineral absorption fea-
tures and wavelength ranges for mineral mapping Table 1
and Table 2 .Unfortunately, these tables were limited to
visible-near infrared (VNIR), shortwave infrared (SWIR)
and longwave infrared (LWIR) atmospheric windows.
Moreover, this table focused on minerals primarily asso-
ciated with mineral exploration and not the wide variety
of ore and gangue minerals that are likely to be present a
host of mineral deposits and mining environments. These
tables were modified to include the MWIR, Far LWIR,
and Ultraviolet along with including other minerals that
were not shown. Another weakness of Coulter et al. 2017 is
the lack of a clear definition on poor, good, clear regarding
absorption features. The definition of poor, good, clear, and
excellent should be defined by not just depth of feature, but
include unique location(s), shape (symmetrical, asymmetri-
cal, doublet, single, or more) and be focused on quantita-
tive measures (Table 3).
The aforementioned tables can be leveraged to look at
a material’s spectroscopic signatures related to their location
in the mining life-cycle beyond minerals that are focused on
mineral exploration. For examples, minerals such as chalco-
pyrite (ore) and pyrite (environmental) have spectroscopic
Figure 4. Modified from Eismann (2012)
GEOMETALLURGICAL OBJECTIVE,
MODEL
When considering using hyperspectral imaging as a poten-
tial solution for answering your geometallurgical questions,
it is important to consider your geometallurgical objective
and/or your geological, mineralogical, mineral process-
ing and metallurgical materials and models (Figure 2).
Eismann (2012) mentions that material spectroscopy is the
initial consideration in hyperspectral imaging (Figure 4).
As a non-destructive technology and one that can provide
in-situ measurements, hyperspectral imaging allows for
measurements that can be directly or indirectly linked to
geometallurgical objectives such as deposit/rock character-
ization, ore characterization, mineral concentrate, predic-
tion of metallurgical properties, and metal products.
Within deposit/rock characterization, imaging spec-
troscopy can help characterize the mineralogy and lithology
of the deposit including alteration patterns. Historically,
imaging spectroscopy has been used in regional and district
level remote sensing studies. More recently, imaging spec-
troscopy sensor solutions have been developed for desktop,
laboratory workbench, and full core box imaging solu-
tions. Mineralogical, alteration, and lithological models are
important to establish prior to implementing HSI as part
of a geometallurgical program. This model can be refined
through-out the mining life-cycle as more rocks are sampled
and measured as part of the program. Mineralogical quan-
tification, geochemistry can be linked to imaging spectros-
copy by a couple of methods. Traditionally, this has been
done by using Partial Least Squares Regression or similar
methods (Hecker, Dilles et al. 2012). Mineral textures and
their 2-dimensional nature can be determined using hyper-
spectral imaging however, these textures are understudied
in imaging spectroscopy research. Hardness, rock strength,
ultrasonic velocity, bulk modulus, shear modulus, dielectric
properties and other physical, mechanical, and chemical
properties of rocks can be predicted using PLS-R or similar
techniques or calculated from mineral abundances (Riley
in prep).
OBSERVABLE /DETECTABLE
PHENOMENA
Coulter, Zhou et al. (2017) produced a tables for silicates
and non-silicates highlighting mineral absorption fea-
tures and wavelength ranges for mineral mapping Table 1
and Table 2 .Unfortunately, these tables were limited to
visible-near infrared (VNIR), shortwave infrared (SWIR)
and longwave infrared (LWIR) atmospheric windows.
Moreover, this table focused on minerals primarily asso-
ciated with mineral exploration and not the wide variety
of ore and gangue minerals that are likely to be present a
host of mineral deposits and mining environments. These
tables were modified to include the MWIR, Far LWIR,
and Ultraviolet along with including other minerals that
were not shown. Another weakness of Coulter et al. 2017 is
the lack of a clear definition on poor, good, clear regarding
absorption features. The definition of poor, good, clear, and
excellent should be defined by not just depth of feature, but
include unique location(s), shape (symmetrical, asymmetri-
cal, doublet, single, or more) and be focused on quantita-
tive measures (Table 3).
The aforementioned tables can be leveraged to look at
a material’s spectroscopic signatures related to their location
in the mining life-cycle beyond minerals that are focused on
mineral exploration. For examples, minerals such as chalco-
pyrite (ore) and pyrite (environmental) have spectroscopic
Figure 4. Modified from Eismann (2012)