Publication Date:
2021-11-26
Description:
The analogue experiments that produce seismo-acoustic events are relevant for understanding
the degassing processes of a volcanic system. The aimof thiswork is to design an unsupervised
neural network for clustering experimental seismo-acoustic events in order to investigate the
possible cause-effect relationships between the obtained signals and the processes. We
focused on two tasks: 1) identify an appropriate strategy for parameterizing experimental
seismo-acoustic events recorded during analogue experiments devoted to the study of
degassing behavior at basaltic volcanoes; 2) define the set up of the selected neural
network, the Self-Organizing Map (SOM), suitable for clustering the features extracted from
the experimental events. The seismo-acoustic events were generated using an ad hoc
experimental setup under different physical conditions of the analogue magma (variable
viscosity), injected gas flux (variable flux velocity) and conduit surface (variable surface
roughness). We tested the SOMs ability to group the experimental seismo-acoustic events
generated under controlled conditions and conduit geometry of the analogue volcanic system.
We used 616 seismo-acoustic events characterized by different analogue magma viscosity (10,
100, 1000 Pa s), gas flux (5, 10, 30, 60, 90, 120, 150, 180 × 10−3 l/s) and conduit roughness (i.e.
different fractal dimension corresponding to 2, 2.18, 2.99). We parameterized the seismoacoustic
events in the frequency domain by applying the Linear Predictive Coding to both
accelerometric and acoustic signals generated by the dynamics of various degassing regimes,
and in the time domain, applying a waveform function. Then we applied the SOM algorithm to
cluster the feature vectors extracted fromthe seismo-acoustic data through the parameterization
phase, and identified four main clusters. The results were consistent with the experimental
findings on the role of viscosity, flux velocity and conduit roughness on the degassing regime.
The neural network is capable to separate events generated under different experimental
conditions. This suggests that the SOM is appropriate for clustering natural events such as the
seismo-acoustic transients accompanying Strombolian explosions and that the adopted
parameterization strategy may be suitable to extract the significant features of the seismoacoustic
(and/or infrasound) signals linked to the physical conditions of the volcanic system.
Description:
Published
Description:
581742
Description:
5V. Processi eruttivi e post-eruttivi
Description:
JCR Journal
Keywords:
self-organizing map
;
neural network
;
seismo-acoustic signals
;
experimental volcanology
;
clustering method
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
article
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