Earthquake productivity law

P. N. Shebalin , C. Narteau (1) , S. V. Baranov

1 IPGP (UMR_7154) - Institut de Physique du Globe de Paris

Mechanisms of stress transfer and probabilistic models have been widely investigated to explain earthquake clustering features. However, these approaches are still far from being able to link individual events and to determine the number of earthquakes caused by a single event. An alternative approach based on proximity functions allows us to generate hierarchical clustering trees and to identify pairs of nearest-neighbours between consecutive levels of hierarchy. Then, the productivity of an earthquake is the number of events of the next level to which it is linked. Using a relative magnitude threshold ΔM to account for scale invariance in the triggering process, we show that the ΔM-productivity attached to each event is a random variable that follows an exponential distribution. The exponential rate of this distribution does not depend on the magnitude of triggering events and systematically decreases with depth. These results could now be used to characterize active fault systems and improve epidemic models of seismicity.

Mots clés: forecasting; Probabilistic forecasting; Statistical seismology; Seismicity and tectonics; and prediction; Earthquake interaction

Domaines: Planète et Univers [physics]; Sciences de la Terre

P. N. Shebalin, C. Narteau, S. V. Baranov. Earthquake productivity law. Geophysical Journal International, 2020, 222, pp.1264-1269. ⟨10.1093/gji/ggaa252⟩. ⟨insu-03748811⟩