  Earthquakes are a major threat to nations worldwide. Earthquake detection is
an important scientific challenge, not only for its social impacts, but also
since it reflects the actual degree of understanding of the physical processes
controlling seismic events. We propose an approach for evaluating and
understanding the dynamics of seismic events. The approach is based on the
phase between the waveform signals of many stations, enabling detecting the
evolution of relatively small magnitudes, down to Mw 1.3. We constructed a
time-evolving network in which the network nodes are the stations, while the
links are the level of correspondence between the stations' signals. The links'
weights are quantified using the following statistical methods:
cross-correlation, synchronization, mutual information, and coherence. Each of
these methods reflects a different aspect of the phase relations between the
waveforms of different stations in a given time window. We then developed
global measures to study the properties of the time-evolving network of seismic
activity. The global measures include the leading eigenvalues of the network
links, the number of links above a certain threshold, and k-means clustering.
We show that the network and its corresponding global measures vary
significantly during seismic events. The results are based on detailed waveform
station data and detailed catalogs from Southern California; our analysis
focused on 27 mainshocks, during which we examined one-day data prior to the
occurrence of the mainshock, as well as one hour of data following it. Among
all the measures we investigated, we found that the coherence measure using the
k-means clustering procedure exhibits the best performance. This technique
correctly identifies earthquake events with magnitudes larger than 2.5 and
exhibits moderate performance for weaker earthquakes with magnitudes larger
than 1.3.
