Statistics – Computation
Scientific paper
Nov 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011geoji.187..889k&link_type=abstract
Geophysical Journal International, Volume 187, Issue 2, pp. 889-917.
Statistics
Computation
Time Series Analysis, Seismicity And Tectonics, Computational Seismology
Scientific paper
Intense seismic sequences involve a large number of earthquakes densely clustered in space and time. Their detailed analysis is important for the geometry of the activated faults, which contributes to studies of the seismotectonic characteristics of an area. The sheer number of small events as well as their low energy content renders their processing problematic. In this work, we have developed and applied a methodology to automatically pick the arrival-times of P- and S-waves using a correlation detector. Event detection is performed using the waveform recordings of a reference station located close to the epicentral area of an intense seismic sequence such as aftershocks or swarms. Cross-correlation matrices are constructed, followed by nearest-neighbour clustering and the formation of multiplets. A Master-Event is chosen from each cluster and its arrival-times are picked manually. The automatic algorithm uses the P- or S-wave of each Master-Event as a correlation detector, searches the waveforms of the other events of the same multiplet and imposes the corresponding arrival-time when the best fit is achieved. The picks are characterized by observation weights, which derive from the quality of the fit, the type of the available waveform components and the consistency between multiple measurements. The proposed methodology was applied to an important seismic sequence that occurred between 2010 January 18 and 26 near the city of Efpalio, Greece. This procedure has the potential to increase 10-fold the amount of information and provide sufficient detail for a subsequent analysis of the spatiotemporal distribution of a seismic series.
Kapetanidis V.
Papadimitriou Panayotis
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