A Generic Model of Global Earthquake Rupture Characteristics Revealed by Machine Learning
Zefeng Li
Abstract
Abstract Rupture processes of global large earthquakes have been observed to exhibit great variability, whereas recent studies suggest that the average rupture behavior could be unexpectedly simple. To what extent do large earthquakes share common rupture characteristics? Here, we use a machine learning algorithm to derive a generic model of global earthquake source time functions. The model indicates that simple and homogeneous ruptures are pervasive whereas complex and irregular ruptures are relatively rare. Despite the standard long‐tail and near‐symmetric moment release processes, the model reveals two special rupture types: runaway earthquakes with weak growing phases and relatively abrupt termination, and complex earthquakes with all faulting mechanisms but mostly shallow origins (<40 km). The diversity of temporal moment release patterns imposes a limit on magnitude predictability in earthquake early warning. Our results present a panoptic view on the collective similarity and diversity in the rupture processes of global large earthquakes.