![]() VT can be described as “normal” earthquakes which take place in a volcanic environment and can indicate magma movement. For this reason it is important to generate automatically different time series for each type of volcano-seismic event. Counting the overall number of events is not enough: one has to detect them and classify them, because they are linked to different processes, as detailed below. These include volcano-tectonic (VT) earthquakes, rockfall events, long-period (LP) and very-long-period (VLP) events, explosions, etc. ![]() Other information-rich time series can be built looking at the time evolution of the number of the different discrete volcano-seismic events that can be recorded on a volcano. Moreover, its time evolution can be indicative of variations in other parameters, such as gas flux. Although many time series may be available, seismic data remain always at the heart of any monitoring system, and should always include the analysis of continuous volcanic tremor tremor has in fact a great potential due to its persistence and memory and its sensitivity to external triggering such as regional tectonic events or Earth tides. For instance, at Merapi volcano, seismic, satellite radar, ground geodetic and geochemical data were efficiently integrated to study the major 2010 eruption a multiparametric approach is essential to understand shallow processes such as the ones seen at geothermal systems like e.g., Dallol in Ethiopia. Whenever possible, a multiparametric approach is always advisable. Different time series can be monitored and hopefully used for forecasting, including seismic data, geomagnetic and electromagnetic data, geochemical data, deformation data, infrasonic data, gas data, thermal data from satellite and from the ground. ![]() Many volcanoes are monitored by observatories that try to estimate at least the probability of the different hazardous volcanic events. Stochastic forecasts of volcanic eruptions are difficult, but deterministic forecasts (i.e., specifying when, where, how an eruption will occur) are even harder. Several volcanoes lie close to highly populated areas and the impact of their eruptions could be economically very strong. Pyroclastic density currents, debris flow avalanches, lahars, ash falls can affect dramatically the life of people living close to volcanoes, and other volcanic products such as lava flows can severely affect properties and infrastructures. 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. 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. 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. 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. This final step can benefit from the application of machine learning techniques, that are able to process big data in an efficient way. This can lead in turn to highlight possible precursors of unrests and eruptions. 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. 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. 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. ![]() These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. A volcano is a complex system, and the characterization of its state at any given time is not an easy task.
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