IEEE Signal Processing - March 2018 - 24
volcano-seismic events. In particular, we focus on the two folthem in three groups: statistical features, entropy features, and
lowing tasks: 1) detection of relevant volcano-seismic events
shape descriptors.
and 2) their classification into semantic classes. We also
■ Statistical features: Statistical features are interesting bereview hybrid (HYB) methods that perform both tasks with the
cause of their immediate interpretation regarding the signals' shapes. For instance, standard deviation describes the
same architecture.
spread of data around its mean, skewness describes the
Regarding detection, it is still done manually in numerous
asymmetry of the signal as a distribution (compared to
studies [25], [28], [30], [35], [36], [39]. Among the automatic
the Gaussian distribution), and kurtosis is related to the
detection processes, the short-term average (STA)/long-term
flatness of the distribution. Considering the feature "i of
average (LTA) method is, by far, the most popular method and
central energy" in the temporal domain, it displays the time
was originally presented in [48] and [49]. STA/LTA is widely
around which the signal energy is centered and can be reused in operational contexts and in published works, including
ferred to as the time centroid. Computed in the frequency
[37] and [43]. Detection systems based on the signal kurtosis have
domain, it is related to the fundamental frequency in the
also been considered [47]. Optimal filtering has also been used
case of a periodic signal, and, similarly, if the original ob[26]. In all cases, results are satisfactory or promising but need be
servation is harmonic, the feature will describe the harimproved. In particular, they have proven to be efficient for wellmonic frequency if computed in the cepstral domain. Some
separated events or for some specific volcano-seismic classes.
of those features are used in [22] and [46].
However, to our knowledge, there is no established procedure to
detect volcano-seismic events in continuous recordings 1) when
■ Entropy: Shannon entropy describes the distribution of the
numerous signals occur in a short period of time (hundreds per
amplitude levels of a given signal. For a periodic signal,
hour), which is the case during an eruption: in this case, signals
amplitude levels would be equally likely and the entropy
associated to different events (not necessarily of the same type)
high. On the contrary, a signal containing a single impulse
can occur overlapped in time and show very
on a continuous (constant amplitude)
different amplitudes; and 2) for emergent sigsignal would have a lower entropy given
Once extracted-manually, nals, i.e., signals whose amplitude increases
the fact that the distribution of amplior by using an automatic
and decreases very slowly (TRs in particular).
tude levels would be very biased toward
detection algorithm-
the mean amplitude level. Entropy feaThose signals are difficult to detect because
tures are used in [21] and [24].
their start and end points are not always clearvolcano-seismic events
ly detectable, especially when the analysis is
■ Shape descriptors: Some ratios have a
need to be organized into
carried out on relatively short temporal winvery helpful physical interpretation, e.g.,
one of several classes
dows Another issue of volcano-seismic events
the ratio maximum value over mean
related to a physical
detection is the high variability in an event's
value can describe the contrast of a sigbehavior of the volcano.
duration: an event can last fewer than 10 s for
nal: if the ratio is large in time, it means
some (VT) to several days (TR). Furthermore,
that the waveform is not constant. This
can be related to the cause of the event; an EXP, e.g., will
methods such as STA/LTA need to be manually tuned at each
application (setting thresholds, window lengths, etc). Many of
lead to a strong peak in the seismic signature, followed by
those approaches are tested on relatively small data sets (a few
a fast decay in terms of amplitude. In the frequency
hundred or fewer than 100 samples in some cases) or on data sets
domain, the maximum over the mean ratio describes the
including only a given class of signals.
spectral richness of the signature: a white noise would have
Once extracted-manually or by using an automatic detecan unitary ratio, while the ration of a pure sinusoid would
tion algorithm-volcano-seismic events need to be organized
be infinite. Finally, the ratio also describes the harmonic
into one of several classes related to a physical behavior of the
content of an observation if computed in the cepstral
volcano. This information is then used to analyze the volcano and
domain: a harmonic signal has a periodic spectra and, conpredict eruptions. In many observatories, this classification task
sequently, is represented by a peak in the cepstral domain.
is still done manually, but the literature offers some studies on the
The maximum over mean ratio is then infinite. On the consubject. HMMs have been used [30]. Neural networks are also
trary, a nonharmonic observation would have a low ratio.
popular, but with very various results [25], [28], [29], [34]-[38],
This ratio is thus particularly interesting to describe a sig[50]. Bayesian classifiers were also tested in [42] but with limnal shape: depending on the computation from the different
ited results. The SVM algorithm has also been used, for instance,
representation domains, it is related to various physical
in [35], [43], and [50] with excellent results. Their approach is
interpretations of the observation. Sensitivity to outliers is a
very similar to studies using random forest (RF) as a learning
drawback of the features of this family. Some of those feaalgorithm; see, e.g., [41]. Some studies also tried using unsupertures are used in [22] and [46].
vised models, such as [34] with principal component analysis;
[27], [39], and [50] with self-organizing maps; or [50] with cluster
State of the art in machine-learning
analysis. Results, however, are highly variable.
techniques for volcano-seismic signals
Finally, a few studies have proposed to develop (in one step)
In this section we give an account on the techniques that have
an architecture to process continuous volcano-seismic recording
been proposed in the literature for the automatic analysis of
24
IEEE SIgnal ProcESSIng MagazInE
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March 2018
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