Asperity distribution and earthquake recurrence time based on patterns of forerunning earthquakes.

Abstract. Due to the long recurrence time of the largest earthquakes and the short time covered by seismic catalogues, the potential for the strongest earthquakes in a given region should be estimated based both on combined seismological and geodetic observations, as well as on the developed seismicity models. At the same time, the asperity model, which is a general view of earthquake occurrence in seismic zones, still requires refinement and more solid empirical support.

In this study, we use new data science methods to analyze and interpret various data from selected subduction and collision zones, including Japan, Chile, and Himalaya-Nepal regions. First, we estimate the expected recurrence times of large earthquakes within a given magnitude range as functions of the Gutenberg-Richter’s b values, for  the assumed maximum magnitude and seismic moment deficit accumulation rate due to the tectonic plate movement. Second, we show seismicity patterns and underlying asperity structures using graphs representing the forerunning and afterrunning earthquakes, which are strictly defined based on the location of earthquakes in time and space, as well as their sizes.

In particular, we propose a method to estimate the rupture areas and magnitudes of possible megathrust earthquakes based on seismicity from the last few decades. We use the graph characteristics to distinguish among different seismicity patterns and scenarios. We also argue that changes in these features over time and space can be used to forecast seismicity forecasting.

 

Keywords: Earthquake forecasting, Gutenberg-Richter law, Recurrence time, Asperities, Forerunning earthquakes.