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Paper Number: 71
Output
selection for Gaussian Processes classification in iron ore
deposits
Silversides,
K.L.1 and Melkumyan, A. 2
1Australian
Centre for Field Robotics, University of Sydney, NSW 2006 Australia
katherine.silversides@sydney.edu.au
___________________________________________________________________________
Modelling stratigraphic ore deposits, such as the banded iron
formation hosted iron ore deposits in the Hamersley Ranges of Western
Australia, requires detailed knowledge of the location of boundaries.
Gaussian Processes (GPs) have been used to identify boundaries in the
exploration hole geophysics [1] and chemical assays [2] from these
deposits. The boundaries identified include marker shale based ore
boundaries and the alluvial to bedded boundary. When using machine
learning for feature classification, different techniques may have
biases that can affect the results. Gaussian Process predictions are
inclined towards the mean value, particularly if there is not sufficient
information [3]. To examine the impact that this inclination has on
feature identification, a typical Marra Mamba style iron ore deposit was
chosen. The alluvial to bedded boundary at the top of the deposit has a
signature that can be identified in the TiO2 and
Al2O3 chemical assays.
Three GP models were trained using the same starting library, and
output ranges of 0 to 1, -0.5 to 0.5 and -1 to 0. In each case the
larger number is the output for positive identification of the
signature. The GP mean is 0 for all tests. Therefore the outputs were
pulled towards the negative, neutral and positive identification
respectively. The libraries were progressively updated [1], diverging
from each other and producing distinctly different final libraries. The
results were compared to a manual geological interpretation (Table
1).
The accuracy and certainty varied between the models, with the 0 to 1
GP having the highest and the -1 to 0 GP having the lowest for both. The
0 to 1 GP also identified the greatest number of correct signatures. The
0 to 1 GP pulled the output towards a negative classification. Therefore
the library had to include sufficient positive examples to identify the
desired feature, but did not need to include all possible negative
examples. This is an advantage due to the very high variability in the
negative examples compared to the positive examples, which are
variations of a single feature. In comparison, the -0.5 to 0.5 GP pulled
all the outputs towards having an uncertain classification. This
resulted in a high number of uncertain results as not all possible
negative examples could be included in the training library. The -1 to 0
GP pulled the results towards having a positive classification. There
were a large number of negative features and including so many
dissimilar features in the same category resulted in difficulty training
this GP model. Therefore, when using GPs for feature classification the
outputs should be arranged so that the pull is against the desired
feature. Then the library can be trained to specifically identify that
feature as having an output away from the mean.
Table 1: Results for the GP models with different output
ranges
-1 to 0 |
|
|
|
-0.5 to 0.5 |
|
|
|
0 to 1 |
|
|
|
|
No. of holes |
|
|
|
|
|
|
Accuracy (%) |
|
|
|
|
|
|
No. of holes |
|
|
|
|
|
|
Accuracy (%) |
|
|
|
|
|
|
No. of holes |
|
|
|
|
|
|
Accuracy (%) |
|
|
|
|
|
|
Certain holes |
|
|
|
|
|
All |
|
|
|
|
3649 |
|
|
|
|
|
|
86.8 |
|
|
|
|
|
|
4063 |
|
|
|
|
|
|
89.2 |
|
|
|
|
|
|
4895 |
|
|
|
|
|
|
88.7 |
|
|
|
|
|
|
|
|
|
|
|
|
Signature |
|
|
|
|
2141 |
|
|
|
|
|
|
80.6 |
|
|
|
|
|
|
2623 |
|
|
|
|
|
|
87.2 |
|
|
|
|
|
|
3098 |
|
|
|
|
|
|
87.0 |
|
|
|
|
|
|
|
|
|
|
|
|
No Signature |
|
|
|
|
1508 |
|
|
|
|
|
|
95.6 |
|
|
|
|
|
|
1440 |
|
|
|
|
|
|
92.8 |
|
|
|
|
|
|
1797 |
|
|
|
|
|
|
91.7 |
|
|
|
|
|
|
Uncertain holes |
|
|
|
|
|
All |
|
|
|
|
2285 |
|
|
|
|
|
|
66.2 |
|
|
|
|
|
|
1871 |
|
|
|
|
|
|
66.2 |
|
|
|
|
|
|
1039 |
|
|
|
|
|
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65.8 |
|
|
|
|
|
|
|
|
|
|
|
|
Signature |
|
|
|
|
1565 |
|
|
|
|
|
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69.8 |
|
|
|
|
|
|
1082 |
|
|
|
|
|
|
63.4 |
|
|
|
|
|
|
591 |
|
|
|
|
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71.2 |
|
|
|
|
|
|
|
|
|
|
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No Signature |
|
|
|
|
720 |
|
|
|
|
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58.5 |
|
|
|
|
|
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789 |
|
|
|
|
|
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70.1 |
|
|
|
|
|
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448 |
|
|
|
|
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58.7 |
|
|
|
|
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Overall accuracy |
|
78.9 |
|
|
|
82.0 |
|
|
|
84.7 |
|
|
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Percent of holes certain |
|
61.5 |
|
|
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68.5 |
|
|
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82.5 |
|
|
|
[1] Silversides K et al. (2015) Comput Geosci
77:118-125
[2] Silversides KL and Melkumyan A (2015) Conf Proc 12th SEGJ
[3] Rasmussen CE and Williams CKI (2006) Gaussian Processes for
Machine Learning. MIT Press, 248 pp