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Earthquakes are a major threat to nations worldwide. Earthquake detection isan important scientific challenge, not only for its social impacts, but alsosince it reflects the actual degree of understanding of the physical processescontrolling seismic events. We propose an approach for evaluating andunderstanding the dynamics of seismic events. The approach is based on thephase between the waveform signals of many stations, enabling detecting theevolution of relatively small magnitudes, down to Mw 1.3. We constructed atime-evolving network in which the network nodes are the stations, while thelinks 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 ofthese methods reflects a different aspect of the phase relations between thewaveforms of different stations in a given time window. We then developedglobal measures to study the properties of the time-evolving network of seismicactivity. The global measures include the leading eigenvalues of the networklinks, the number of links above a certain threshold, and k-means clustering.We show that the network and its corresponding global measures varysignificantly during seismic events. The results are based on detailed waveformstation data and detailed catalogs from Southern California; our analysisfocused on 27 mainshocks, during which we examined one-day data prior to theoccurrence of the mainshock, as well as one hour of data following it. Amongall the measures we investigated, we found that the coherence measure using thek-means clustering procedure exhibits the best performance. This techniquecorrectly identifies earthquake events with magnitudes larger than 2.5 andexhibits moderate performance for weaker earthquakes with magnitudes largerthan 1.3.

Earthquake activity as captured using the network approach