Clustering analysis of seismicity in the space-time-depth-magnitude domain preceding the 2016 Kumamoto earthquake, Southwestern Japan

This analysis is an extension of the studies based on the space-time-magnitude domain nearest-neighborhood distances (n) obtained from two successive earthquakes. We included epicentral depth as a parameter, and established a new analytical solution for seismicity clustering in the space-time-depth-magnitude domain. The proposed technique was applied to SW Japan seismic observation data for 2011-2018, and revealed that the temporal, spatial, and epicentral (T, R, E) components of the joint distribution for n were prominently bimodal, indicating the presence of two, statistically distinct, earthquake populations. We used the Lomb-Scargle periodogram (LSP) to generate a power spectrum preceding the Kumamoto earthquake, and detected the periodic component of unequally spaced points for log(10)n (logarithmic scale of n). The LSP showed a dominant peak corresponding to the time period between foreshocks and aftershocks, due to the highest number of recorded n observations. The technique presented in this paper can be applied globally for any time period and any seismotectonic zone preceding large earthquakes of interest.

Automated summary

This analysis extends previous studies by incorporating epicentral depth as a parameter for seismicity clustering. The technique was applied to SW Japan seismic data, revealing two statistically distinct earthquake populations. The Lomb-Scargle periodogram detected periodic components in log(10)n, with a dominant peak between foreshocks and aftershocks.