Peakmatch: A Java Program for Multiplet Analysis of Large Seismic Datasets

The identification of repeating earthquakes by waveform cross correlation (multiplet analysis) has many applications in the analysis of seismic data and is increasingly being applied to volcanic‐seismic datasets. Multiplet analysis can be used to evaluate stress changes at volcanoes, to constrain the depth of explosions, or to improve locations of small‐amplitude events (Buurman and West, 2010; Thelen et al. , 2011; Battaglia et al. , 2012; West, 2013). Repeating earthquakes often occur during tectonic earthquake sequences (Nadeau et al. , 1995; Peng and Zhao, 2009), can occur within large regional seismic data catalogs (Schaff and Richards, 2011), and have been observed as microearthquakes on creeping faults (Waldhauser and Ellsworth, 2002; Malservisi et al. , 2005) and as icequakes in glaciers (Carmichael et al. , 2012; Thelen et al. , 2013). Multiplet analysis thus has the potential to be an intrinsic part of the seismologist’s toolbox; however, many studies cite computational limitations when analyzing multiplets (Petersen, 2007; Thelen et al. , 2010, 2011; Ketner and Power, 2013). Cross correlation is computationally expensive and large datasets can rapidly become difficult to analyze. Cross‐correlation algorithms are considered Big‐ O ( N 2) problems (in which N is the total number of events) in terms of the performance time of the algorithm (Black, 2004). Seismic data catalogs of hundreds of thousands of events are not uncommon at well‐monitored volcanoes (Ketner and Power, 2013), and global seismic catalogs can number many millions of events (Addair et al. , 2014). Waveform cross correlation for the identification of repeating earthquakes has been carried out on seismic events for many years (Nadeau et al. , 1995) and initially was confined to small datasets. Cross correlation can be used to …