Motif Discovery on Seismic Amplitude Time Series: The Case Study of Mt Etna 2011 Eruptive Activity

Algorithms searching for similar patterns are widely
used in seismology both when the waveforms of the events of
interest are known and when there is no a priori-knowledge. Such
methods usually make use of the cross-correlation coefficient as a
measure of similarity; if there is no a-priori knowledge, they
behave as brute-force searching algorithms. The disadvantage of
these methods, preventing or limiting their application to very large
datasets, is computational complexity. The Mueen–Keogh (MK)
algorithm overcomes this limitation by means of two optimization
techniques—the early abandoning concept and space indexing.
Here, we apply the MK algorithm to amplitude time series
retrieved from seismic signals recorded during episodic eruptive
activity of Mt Etna in 2011. By adequately tuning the input to the
MK algorithm we found eight motif groups characterized by distinct
seismic amplitude trends, each related to a different
phenomenon. In particular, we observed that earthquakes are
accompanied by sharp increases and decreases in seismic amplitude
whereas lava fountains are accompanied by slower changes.
These results demonstrate that the MK algorithm, because of its
particular features, may have wide applicability in seismology.