Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/3368
AuthorsFerretti, G.* 
Massa, M.* 
Solarino, S.* 
TitleAn Improved Method for the Recognition of Seismic Families: Application to the Garfagnana-Lunigiana Area, Italy
Issue DateOct-2005
Series/Report no.5/ 95 (2005)
DOI10.1785/0120040078
URIhttp://hdl.handle.net/2122/3368
KeywordsSeismicity
multiplets
seismic families
seismic sequences
Subject Classification04. Solid Earth::04.06. Seismology::04.06.03. Earthquake source and dynamics 
04. Solid Earth::04.06. Seismology::04.06.06. Surveys, measurements, and monitoring 
04. Solid Earth::04.06. Seismology::04.06.09. Waves and wave analysis 
AbstractWith the aim to find a more objective way to detect seismic families, we applied a series of successive steps to constrain the results of a waveform similarity analysis. The evaluation of similarity was carried out on the waveforms recorded in the period 1999–2003 by the stations operating in the Garfagnana area, located in northern Tuscany (Italy). The algorithm is based on the cross-correlation technique applied in a process that overcomes the limit of one order of magnitude between events to be compared through a bridging technique. In practice, if two couples of events (A, B) and (B, C), each exceeding the correlation threshold, share a common quake (B), then all three events are attributed to the same family even if the match between A and C is below a value chosen as a reference for similarity. To avoid any subjective choice of threshold for cross-correlation values, the results from the computation algorithm are submitted to a routine that gives increasing reliability to them if they are confirmed by the three components of the seismogram and if the number of families detected by each station is confirmed by more recordings. This latter constraint is made possible by the geometry of the recording network, with interdistances between stations of the order of 40–50 km. The process finally leads to the recognition of 27 families detected and confirmed by, on average, 3 stations that represent 40% of the recording capabilities. Since the performances of the recording network have been very odd in the past, especially in the early years of operation, the reliability of the detection is much higher, as in most cases the stations that detected the families were the only ones to be effectively recording. The methodology proved to be more efficient than other methods applied in the past; moreover, the results could be probably improved even more if, instead of doing a one-run process, it would be borne as a trial-and-error approach.
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