MINING IN FOCUS
such as that variables are independent and identically distributed. A
prime example is in the field of structural geophysics, where variables are
structurally related to each other in the context of space and time, unless
there is a discontinuity, such as a fault, across which autocorrelation
ceases to persist. Cognisance of the spatio-temporal autocorrelation in
geoscience data collected in continuous media is crucial for the effective
modelling of geophysical phenomena.
A holistic interpretation
ML has the ability to link, combine and process different types of data
together, making it easier to synthesise a holistic interpretation. A simple
application of ML can be done by importing photographs of a core tray or
a face in mining. The ML will then attempt to crop out core from a tray or
read core blocks.
In the case of the face photograph, ML will attempt domaining ore
versus waste zones. In a supervised learning approach, the operator will
instruct the ML model what the most likely solution is using training and
validation datasets. The operator can assess the accuracy of the solution
to improve the model in a variety of ways to increase the prediction
accuracy. A simple improvement may involve examining the labelled
data for any mistakes and changing the label. The main challenge with
supervised learning is small sample size initially, and a lack of established
standards being applied.
Technology will have a significant impact on the way exploration will be
done in future.
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African Mining September 2019
There are two main approaches: the first is simple neural networks,
and the second is deep learning networks. A simple way to explain the
difference is that the simple neural network approach will entail trying
to do many different computations simultaneously. The deep learning
network, on the other hand, breaks up the computations into separate
steps with each layer learning something different based on the output of
the previous layer. This hierarchy of representations seems to enable deep
learning to predict better on new data than the simple neural network.
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