Continuous seismograms contain a wealth of information with a large
variety of signals with different origins. Identifying these signals is
a crucial step in understanding physical geological objects. We propose
a strategy to identify classes of seismic signals in continuous
single-station seismograms in an unsupervised fashion. Our strategy
relies on extracting meaningful waveform features based on a deep
scattering network combined with an in- dependent component analysis.
Based on the extracted features, agglomerative clustering then groups
these waveforms in a hierarchical fashion and reveals the process of
clustering in a dendrogram. We use the dendrogram to explore the seismic
data and identify different classes of signals. To test our strategy, we
investigate a two-day-long seismogram collected in the vicinity of the
North Anatolian Fault, Turkey. We analyze the automatically inferred
clusters' occurrence rate, spectral characteristics, cluster size, and
waveform and envelope characteristics. At a low level in the cluster
hierarchy, we obtain three clusters related to anthropogenic and ambient
seismic noise and one cluster related to earthquake activity. At a high
level in the cluster hierarchy, we identify a seismic crisis that
includes more than 200 repeating events and high-frequent signals with
correlated envelopes and an anthropogenic origin. The application shows
that the cluster hierarchy helps to identify particular families of
signals and to extract subclusters for further analysis. This is
valuable when certain types of signals, such as earthquakes, are
under-represented in the data. The proposed method may also successfully
discover new types of signals since it is entirely data-driven.