MINING IN FOCUS
MINING IN FOCUS
Another approach is ‘deep fakes’, which utilises a double network
system. The generator tries to fool another network, the discriminator.
The discriminator penalises the generator for obviously fake information
and so the generator learns to produce better and better fake
information in each cycle. This application is currently being developed
extensively by the oil and gas industry to evaluate seismic sections and
velocity models.
Other industry applications currently in development deal with:
• Classifying various characteristics using spatial data obtained via
GIS systems;
• The use of neural networks/SVM in analysing temporal signals
like those of seismometers to predict phenomenon such as
earthquakes and tsunamis;
• Applications for landslide prediction using seismic data;
• Several ML algorithms such as decision trees and neural networks
have worked well in mineral exploration using remote sensing
data; and
• Subsurface characterisation using various acoustic signals also use
some form of ML for specific problems which involve detecting
types of minerals, various types of folds and fracturing.
Case study
In comparison to highly visible mineralisation, such as massive, semi-
massive and disseminated mineralisation (for example, base metal
mineralisation), trace mineralisation is harder to develop an ML process
for. A project was undertaken to identify gold mineralisation in core
utilising geophysical results. Gold distribution is not homogeneous
in drill core, and it is subject to a high local variability (nugget effect),
which makes ore bodies modelling difficult. The presence of gold in
rocks is usually associated with specific rock formations (for example,
banded iron formation or intrusive rocks), alteration, and the presence
of veins, information on rock composition is critical to the prediction of
gold mineralisation.
The input data was derived from neutron activation and natural
gamma measurements. The team used a hand-held XRF to measure
the variability of the major elements. Six machine learning algorithms
were used to predict the presence of mineralisation. Results indicated
that the integration of a set of rock physical properties measured at
closely spaced intervals along the drill core with ensemble machine
learning algorithms allows the detection of gold-bearing intervals with
an adequate rate of success. The use of this type of tool in the future will
help geologists in selecting sound intervals for assay sampling, which
in turn could potentially increase the reserve and in modelling more
continuous ore bodies during the entire life of a mine.
Along with predicting the presence of metals in rocks, physical
properties combined with machine learning have the potential to
classify lithologies, characterise hydrothermal alteration, and estimate
exploration vectors and geotechnical information in the drill core. The
success rate of predictions will increase as increasing amounts of data
are collected. This method should be applied from the very beginning
of the exploration stage (which means starting from the discovery hole)
so that the initial model can be trained and continuously updated with
new drill holes.
About the Authors
Breton Scott has over two decades of post-qualification experience in
the mining and project engineering industry. He has been involved in a
variety of activities ranging from mine operations, project management,
mining and rock engineering, mineral asset valuations, due diligences,
EPCM contracts and related feasibility studies.
Nicolaas C. Steenkamp has a decade and a half of post-qualification
experience in the geological and geotechnical industry. He has been
involved in a variety of activities ranging for exploration, geochemistry,
geological and geotechnical, desktop studies, due diligence, EPCM
contracts and related feasibility studies.
Bowline Professional Services offers a wide range of geological, mining
and industry related services.
www. africanmining.co.za
African Mining September
2019
53
African Mining
Publication
African Mining
African Mining September 2019
53