A volcano is a complex system, and the characterization of its state at any given
time is not an easy task. Monitoring data can be used to estimate the probability
of an unrest and/or an eruption episode. These can include seismic, magnetic,
electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal
situation, a combination of them. Merging data of different origins is a non-trivial
task, and often even extracting few relevant and information-rich parameters from
a homogeneous time series is already challenging. The key to the characterization
of volcanic regimes is in fact a process of data reduction that should produce
a relatively small vector of features. The next step is the interpretation of the
resulting features, through the recognition of similar vectors and for example,
their association to a given state of the volcano. This can lead in turn to highlight
possible precursors of unrests and eruptions. This final step can benefit from the
application of machine learning techniques, that are able to process big data in an
efficient way. Other applications of machine learning in volcanology include the
analysis and classification of geological, geochemical and petrological “static” data
to infer for example, the possible source and mechanism of observed deposits, the
analysis of satellite imagery to quickly classify vast regions difficult to investigate
on the ground or, again, to detect changes that could indicate an unrest. Moreover,
the use of machine learning is gaining importance in other areas of volcanology,
not only for monitoring purposes but for differentiating particular geochemical
patterns, stratigraphic issues, differentiating morphological patterns of volcanic
edifices, or to assess spatial distribution of volcanoes. Machine learning is helpful
in the discrimination of magmatic complexes, in distinguishing tectonic settings of
volcanic rocks, in the evaluation of correlations of volcanic units, being particularly
helpful in tephrochronology, etc. In this chapter we will review the relevant methods
and results published in the last decades using machine learning in volcanology,
both with respect to the choice of the optimal feature vectors and to their subsequent
classification, taking into account both the unsupervised and the supervised
approaches.
