Publication Date:
2024-05-10
Description:
Infrasound monitoring plays an important role in the framework of the surveillance of Mt. Etna, Europe’s largest active volcano. Compared to seismic monitoring, which is particularly effective for buried sources, infrasound signals mirror the activity of shallow sources like Strombolian explosions or degassing. The interpretation of infrasound signals is difficult to the untrained eye, as we have to account for volcanic and non-volcanic sources. The problem of handling large and complex data sets can be tackled with machine learning, namely pattern recognition techniques. Here, we focus on so-called ‘Unsupervised Learning’, where we identify groups of patterns being similar to each other. The degree of similarity is based on a metric measuring the distance among the features of the patterns. This work aims at the identification of typical regimes of infrasound radiation and their relation to the state of volcanic activity at Mt. Etna. For this goal, we defined features describing any infrasound pattern. These features were obtained using wavelet transform. We applied ‘Self-Organizing Maps’ (SOM) to the features projecting them to a 2-D representation space—the ‘map’. An intriguing aspect of SOM resides in the fact that the position of the patterns on the map can be expressed by a colour code, in a manner that similar patterns are assigned a similar colour code. This simplified representation of multivariate patterns allows to follow the development of their characteristics with time efficiently. During a training phase we considered a reference data set, which encompassed a large variety of scenarios. We identified typical groups of patterns which correspond to a specific regime of activity, being representative of the state of the volcano or noise conditions. These groups form areas on the 2-D maps. In a second step, we considered a test data set, which was not used during the training phase. Applying the same pre-processing as for the training data, we blindly assigned the test patterns to the regimes found before, identifying the one whose colour code is most similar to the one calculated to the test pattern. We are thus able to assess the validity of the prediction. The classification scheme presented provides a reliable assessment of the state of activity and adds useful and supplementary details to the results of the real-time automatic system in operation at Istituto Nazionale di Geofisica e Vulcanologia—Osservarorio Etneo. This is of particular importance when no visible information of the volcanic activity is available either for unfavourable meteorological conditions or during night time.
Description:
IMPACT PROJECT (INGV Department strategic Projects - 2019)
Description:
Published
Description:
1-16
Description:
JCR Journal
Keywords:
infrasound
;
volcano monitoring
;
self-organization
;
time-series analysis
;
machine learning
;
volcanic hazards and risks
;
04.06. Seismology
;
04.08. Volcanology
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
article
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