Groups of similar earthquakes and repeating earthquakes have been detected in various tectonic environments worldwide (Uchida and Bürgmann, 2019). Similar earthquakes are defined as events possessing similar waveforms, and repeating earthquakes consist of similar earthquakes that occur in approximately the same location and possess nearly identical focal mechanisms. Many repeating earthquakes occur regularly at plate boundaries, whereas some occur as short-lived burst-type activity in shallow inland crustal environments (e.g., Igarashi, 2010).
Repeating earthquakes are useful for understanding earthquake generation mechanisms, as well as monitoring the local seismic velocity structure. Both the spatial distribution and temporal changes in their source processes have been revealed via analyses of repeating earthquakes (e.g., Okada et al., 2003; Ariyoshi et al., 2014). The earthquake repetition over a long period is used to estimate the spatiotemporal evolution of interplate aseismic slip (e.g., Igarashi, 2010; Kato et al., 2016). Slight changes among the waveforms of repeating earthquakes often indicate temporal changes in the local seismic velocity structure (e.g., Rubinstein et al., 2007; Taira et al., 2009; Tkalčić et al., 2013). The matched filter technique can detect frequent repetitive sources embedded in the seismic record, even if the waveforms have undergone moderate changes, and thereby improves the completeness of the hypocenter catalog (e.g., Kato and Nakagawa, 2014). Nearby earthquakes can also be efficiently extracted from high-seismicity areas where many of the earthquakes possess similar waveforms.
Early approaches to similar earthquake extraction were based on the visual inspection of seismogram records (e.g., Omori, 1905; McEvilly and Casaday, 1967; Stauder and Ryall, 1967; Tsujiura, 1973; Hamaguchi and Hasegawa, 1975; Geller and Mueller, 1980). Their recurrence at approximately the same location was investigated using other information, such as the S-P time, which is the time difference between the P- and S-wave arrivals (e.g., Hamaguchi and Hasegawa, 1975), and the source size, which is estimated from the corner frequency (e.g., Geller and Mueller, 1980).
Large-scale analyses of similar earthquakes based on waveform similarity have been made possible by the accumulation of vast digital seismogram data volumes and advanced computing capabilities. Aster and Scott (1993) investigated the features related to waveform similarity using 4569 seismic records from ten seismic stations in the ANZA network (Southern California, USA) over a 9.5-year period (October 1, 1982 to April 14, 1992). Those authors analyzed 1,121,332 earthquake pairs with an inter-event distance of ≤ 10 km, and identified similar earthquakes when the earthquake pairs had a median cross-correlation coefficient of ≥ 0.725. They detected 290 similar earthquake sequences (1255 events) based on the threshold.
Schaff and Richards (2004) extracted similar earthquakes from ~ 14,000 earthquakes that occurred in China between 1985 and 2000 using ~ 130,000 waveforms that were recorded at 115 neighboring seismic stations. The maximum interevent distance was 150 km, resulting in ~ 1,200,000 analyzed earthquake pairs. Similar earthquakes were identified when the cross-correlation coefficient between an earthquake pair was ≥ 0.8 at a given station. They detected 494 similar earthquake sequences (1303 events).
Igarashi (2010) identified similar earthquakes throughout the Japanese Islands using waveforms obtained from a nationwide seismic network over an eight-year period (January 2002 to December 2009). He analyzed 428,156,998 earthquake pairs using the waveforms from 168,425 earthquakes that were recorded by up to 1105 stations. Similar earthquakes were identified when the cross-correlation coefficient was ≥ 0.95 at two or more stations. This paper detected 2356 similar earthquake sequences (6638 events).
Dodge and Walter (2015) extracted similar earthquakes using global seismic data from the LLNL waveform database over a 43-year period (1970–2013). They analyzed ~ 310,000,000 waveforms from 3,745,879 events and 6266 seismic stations, with ~ 1,485,000,000 event pairs possessing an inter-event distance of ≤ 50 km. Similar earthquakes were determined when the correlation coefficient was ≥ 0.6 at any station. The number of events exceeding the threshold was 14.5% of the total (542,405 events).
Several studies have investigated the degree of colocation or overlap between the source areas of each earthquake pair to accurately identify repeating earthquakes. For example, Lees (1998) used the travel time difference obtained via cross-spectral and coherence calculations. Chen et al. (2008) also validated the overlap between two source areas by imposing the following conditions: the cross-correlation coefficient is ≥ 0.85 and the S-P time is < 0.012 s. Li et al. (2011) extracted candidates for repeating earthquakes when the relative distance calculated from the S-P differential times and assumed velocity structure was less than the rupture dimensions of each earthquake.
Here we update the similar earthquake catalog that was constructed by Igarashi (2010) to analyze the long-term earthquake activity throughout the Japanese Islands that is associated with the subducting Pacific and Philippine Sea plates, and overriding Amur and Okhostk plates. Furthermore, we improve this small repeating earthquake catalog by imposing the colocation or overlapping constraints onto the rupture areas of each similar earthquake pair. We then compare these two catalogs in terms of the average slip-rate distribution along the entire Japanese Islands.