**Abstract**

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.

**Keywords:** machine learning, volcano seismology, volcano geophysics, volcano geochemistry, volcano geology, data reduction, feature vectors

## **1. Introduction**

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. Several volcanoes lie close to highly populated areas and the impact of their eruptions could be economically very strong. Stochastic forecasts of volcanic eruptions are difficult [1, 2], but deterministic forecasts (i.e., specifying when, where, how an eruption will occur) are even harder. Many volcanoes are monitored by observatories that try to estimate at least the probability of the different hazardous volcanic events [3]. Different time series can be monitored and hopefully used for forecasting, including seismic data [4], geomagnetic and electromagnetic data [5], geochemical data [6], deformation data [7], infrasonic data [8], gas data [9], thermal data from satellite [10] and from the ground [11]. Whenever possible, a multiparametric approach is always advisable. For instance, at Merapi volcano, seismic, satellite radar, ground geodetic and geochemical data were efficiently integrated to study the major 2010 eruption [12]; a multiparametric approach is essential to understand shallow processes such as the ones seen at geothermal systems like e.g., Dallol in Ethiopia [13]. 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 [14]; tremor has in fact a great potential [15] due to its persistence and memory [1, 2] and its sensitivity to external triggering such as regional tectonic events [16] or Earth tides [17]. Moreover, its time evolution can be indicative of variations in other parameters, such as gas flux [18]. 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. These include volcano-tectonic (VT) earthquakes, rockfall events, long-period (LP) and very-long-period (VLP) events, explosions, etc. 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. For this reason it is important to generate automatically different time series for each type of volcano-seismic event.

VT can be described as "normal" earthquakes which take place in a volcanic environment and can indicate magma movement [19, 20]. LP events have a great potential for forecasting [21]. Their debated interpretation involves the repeated expansion and compression of sub-horizontal cracks filled with steam or other ash-laden gas [22], stick–slip magma motion [23], fluid-driven flow [24], eddy shedding, turbulent slug flow, soda bottle analogues [25], deformation acceleration of solidified domes [26] and slow ruptures [27]. Explosion quakes are generated by sudden magma, ash, and gas extrusion in an explosive event, often associated to VLP events [28]. In many papers also "Tremor episodes" (TRE events) are described and counted, usually associated to magma degassing [20]. However, a volcano with any activity produces a continuous "tremor" which detectability only depends on the seismic instrumentation sensitivity [29, 30]. So, the class "TRE" should be better defined as "tremor episode that exceeds the detection limits". Of course, at volcanoes we can also record natural but non-volcanic seismic signals such as far tectonic earthquakes, far explosions, etc., and also anthropogenic signals e.g., due to industries, ground vehicles, helicopters used for monitoring, etc.

Most volcano observatories rely on manual classification and counting of such seismic events, which suffers from human subjectivity and can become unfeasible during an unrest or a seismic crisis [31, 32]. For this reason, manual classification should be substituted by an automated processing, and here is where machine learning (ML) comes into place. The same reasoning applies of course also to the automated processing of other monitoring time series, such as deformation, gas and water geochemistry, etc. Moreover, ML in volcanology is not restricted to monitoring active volcanoes but has demonstrated to be useful also when dealing with other large datasets. Examples include correlating volcanic units in general e.g., [33], of tephra e.g., [34, 35] and ignimbrites e.g., [36], a task which may become very difficult especially when many deposits of similar ages and geochemical and

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**Figure 1.**

*Machine Learning in Volcanology: A Review DOI: http://dx.doi.org/10.5772/intechopen.94217*

unsupervised or based on reinforcement.

**2. Machine learning**

petrographic characteristics crop out in a given area. ML is also effective for discriminating tectonic settings of volcanic rocks [34, 37]. Recently it has been used

ML is a field of computer science dedicated to the development of algorithms which are based on a collection of examples of some phenomenon. These examples can be natural, human-generated or computer-generated. From another point of view, ML can be seen as the process of solving a problem by building and using a statistical model based on an existing dataset [39]. ML can also be defined as the study of algorithms that allow computer programs to automatically improve through experience [40]. ML is only one of the ways we expect to achieve Artificial Intelligence (AI). AI has in fact a wider, dynamic and fuzzier definition, e.g., Andrew Moore, former Dean of the School of Computer Science at Carnegie Mellon University, defined it as "the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence". ML is usually characterized by a series of steps: data reduction, model training, model evaluation, model final deployment for classification of new, unknown data (see **Figure 1**). The training (which is the proper learning phase) can be supervised, semi-supervised,

More data does not necessarily imply better results. Low quality and irrelevant data can instead lead to worse classification performances. If for each datum we have a very high number of columns, we may wonder how many of those are really informative. A number of techniques can help us with this process of **data reduction**. The simplest include column variance estimations and evaluating correlations between columns. Each of the components of the vector that "survive" this phase is called a feature and is supposed to describe somehow the data item, hopefully in a way that makes it easier to associate the item to a given class. There are dimensionality reduction algorithms [41] where the output is a simplified feature vector that is (almost) equally good at describing the data. There are many techniques to find a smaller number of independent features, such as Independent Component Analysis (ICA) [42], Non-negative Matrix

*ML can be divided in several steps, from top to bottom. Raw data have first to be reduced by extracting short and information-rich feature vectors. These can then be used to build models that are trained, analyzed and finally used for classification of new data. The [labels] are present only in a (semi-)supervised approach.*

also for the prediction of trace elements in volcanic rocks [38].

petrographic characteristics crop out in a given area. ML is also effective for discriminating tectonic settings of volcanic rocks [34, 37]. Recently it has been used also for the prediction of trace elements in volcanic rocks [38].
