Power-Law Time Distribution of Large Earthquakes

TL;DR

This paper analyzes the time intervals between large earthquakes in California, revealing a power-law distribution that challenges Poisson-based models and proposing a new long-range triggering model.

Contribution

It introduces a novel analysis method, the Diffusion Entropy, and demonstrates that earthquake intervals follow a power-law distribution, leading to a new seismic triggering model.

Findings

Time intervals follow an inverse power law with exponent ~2.06.

Poisson statistics do not describe large earthquake timing.

The Long-Range model captures key properties of seismic triggering.

Abstract

We study the statistical properties of time distribution of seimicity in California by means of a new method of analysis, the Diffusion Entropy. We find that the distribution of time intervals between a large earthquake (the main shock of a given seismic sequence) and the next one does not obey Poisson statistics, as assumed by the current models. We prove that this distribution is an inverse power law with an exponent $\mu=2.06 \pm 0.01$. We propose the Long-Range model, reproducing the main properties of the diffusion entropy and describing the seismic triggering mechanisms induced by large earthquakes.