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Machine learning for geological mapping: Algorithms and applications


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Cracknell, M 2014 , 'Machine learning for geological mapping: Algorithms and applications', PhD thesis, University of Tasmania.

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Machine learning algorithms are designed to identify efficiently and to predict accurately
patterns within multivariate data. They provide analysts computational tools to aid
predictive modelling and the interpretation of interactions between data and the
phenomena under investigation. The analysis of large volumes of disparate multivariate
geospatial data using machine learning algorithms therefore offers great promise to
industry and research in the geosciences. Geoscience data are frequently characterised by a
restriction in the number and distribution of direct observations, irreducible noise in these
data and a high degree of intraclass variability and interclass similarity. The choice of
machine learning algorithm, or algorithms and the details of how algorithms are applied
must therefore be appropriate to the context of geoscience data. With this knowledge, I aim
to employ machine learning as a means of understanding the spatial distribution of
complex geological phenomena.
I conduct a rigorous and comprehensive comparison of machine learning algorithms,
representing the five general machine learning strategies, for supervised lithology
classification applications. I also develop and test a novel method for obtaining robust
estimates of the uncertainty associated with machine learning algorithm categorical
predictions. The insights gained from these experiments leads to the further development
and comparison of new methods for the incorporation of spatial-contextual information
into machine learning supervised classifiers.
In using machine learning algorithms for geoscience applications, I have developed bestpractice
methodologies that address the challenges facing geoscientists for geospatial
supervised classification. Guidelines are established that detail the preparation and
integration of disparate spatial data, the optimisation of trained classifiers for a given
application and the robust statistical and spatial evaluation of outputs. I demonstrate,
through a case study in a region that is prospective for economic mineralisation, the
combination of supervised and unsupervised machine learning algorithms for the critical
appraisal of pre-existing geological maps and formulation of meaningful interpretations of
geological phenomena. The experiments conducted as part of my research confirm the efficacy of machine
learning algorithms to generate accurate geological maps representing a variety of terranes.
I identify and explore key aspects of the spatial and statistical distributions of geoscience
data that affect machine learning algorithm performance. My research clearly identifies
Random Forests™ as a good first-choice algorithm for the prediction of classes
representing lithologies using commonly available multivariate geological and geophysical
data. Furthermore, Random Forests prediction uncertainty is shown to be closely related to
ambiguous and/or erroneous classifications and, thus provides a practical means of
indicating variable levels of confidence. Spatial-contextual information is best incorporated
into machine learning supervised classifiers via the pre-processing of input variables
and/or the post-regularisation of classifications. My findings indicate that a trade-off
between optimal predictive models and interpretable explanatory models exists, whereby,
intuitively interpretable models are not necessarily the most accurate.
The practical application of machine learning algorithms requires the implementation of
three key stages: (1) data pre-processing; (2) algorithm training; and (3) prediction
evaluation. This methodology provides the foundation for generating accurate and
geologically meaningful predictions with minimal user intervention and assists in the
formulation of robust interpretations of complex geological phenomena. For example,
classifications obtained by Random Forests are useful for critically appraising interpreted
geological maps. Clusters produced by Self-Organising Maps indicate the presence of
discrete, spatially contiguous and geologically significant sub-classes within individual
lithological units, which represent regions of contrasting primary composition and
alteration styles. My results may be widely applied to a broad range of practical geoscience
challenges such as ore deposit targeting, geo-hazard risk assessment, engineering and
construction projects, hydrological and environmental modelling and ecological studies.
The applications of machine learning algorithms detailed in this thesis align well with
state-of-the-art Big Data online infrastructure and virtual laboratories currently emerging in

Item Type: Thesis - PhD
Authors/Creators:Cracknell, M
Keywords: Machine learning; geological mapping; computational geophysics; uncertainty; spatial analysis; geochemistry; remote sensing
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