Applications Of Machine Learning In Volcanology
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Applications of Machine Learning in Volcanology
Author: Bellina Di Lieto
language: en
Publisher: Frontiers Media SA
Release Date: 2025-04-29
The characterization of volcano state is not a simple task due the complexity of physics processes underway. Understanding their evolution prior to and during eruptions is a critical point for identifying transitions in volcanic state. Permanent monitoring networks are developed for such a purpose. With the increase of the number of monitoring sites, the amount of available continuous data coming from different sources (infrasonic, seismic, GPS, geochemical, etc.) has increased exponentially and extracting the huge amount of information this data brings, represents a non-trivial task for researchers, who are always more often looking at the potentiality of computer algorithms to find correlations among them. Recent developments in the field of Machine Learning (ML) have proven to be very useful and efficient for automatic discrimination, decision, prediction, clustering and information extraction in many fields, including volcanology. In recent times, Deep Learning has seen rapid growth in its popularity along with other supervised strategies, such as Support Vectors Machines and Recurrent neural networks (RNN), which have consistently been applied with success to broader and broader sets of applications and fields. However, supervised machine learning requires labels for training, and obtaining these labels for large volumes of seismic and volcanic data is a very demanding and challenging task. Therefore, semi-supervised and unsupervised methods, such as Self-organized Maps, have been applied with success, to extract relevant information from huge amounts of unlabelled data. In seismic and deformative data processing, these techniques are used for waveform inversion, automatic picking of first arrivals, and interpretation of peculiar characteristics of transients. ML is helpful in the discrimination of magmatic complexes, in distinguishing tectonic settings of volcanic rocks, in the evaluation of correlations between volcanic signals and the chemico-physical composition of erupted materials. Other applications of ML 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. The results obtained with the help of these algorithms would otherwise represent for researchers’ tasks hard to be solved with the usual standard methodologies.
Intelligent Methods with Applications in Volcanology and Seismology
This book presents intelligent methods like neural, neuro-fuzzy, machine learning, deep learning and metaheuristic methods and their applications in both volcanology and seismology. The complex system of volcanoes and also earthquakes is a big challenge to identify their behavior using available models, which motivates scientists to apply non-model based methods. As there are lots of seismology and volcanology data sets, i.e., the local and global networks, one solution is using intelligent methods in which data-based algorithms are used.
Chapter Machine Learning in Volcanology: A Review
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.