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Abstract:
Volcanic tremors are one of many seismic signals recorded on volcanoes and are associated with
different pre- and co-eruptive processes. Therefore, they are widely used in volcano monitoring.
The properties of the tremor signals such as duration, spectral content, or intermittency are very
variable, reflecting the possible different tremor source mechanisms. In many cases, several
tremor-generating processes can act simultaneously resulting in overlapping signals in the
seismogram. Despite their complex signal characteristics and different source
mechanisms, volcanic tremors are either treated as one seismic signal class or as a set of seismic
signal classes. With a scattering network, we can access the information conveyed by volcanic
tremors, even in the presence of short-term impulsive signals. We apply blind source separation
methods and manifold learning techniques to continuous seismograms recorded at the
Klyuchevskoy Volcanic Group (Kamchatka, Russia) and reveal the underlying patterns in the time
series data dominated by volcanic tremors. The data-driven descriptors of the year-long
seismogram reveal an ever-changing tremor signal, challenging the division of the observed
volcanic tremors into a few distinct classes. The results highlight the complexity and nonstationarity
of the volcanic tremors, suggesting a non-stationary volcanic system. Relating the datadriven
patterns to the different underlying processes is the next step to understanding better the
inner workings of a volcano.
