2002/12/31 by Mirko S. Mega, Paolo Allegrini, Paolo Grigolini +4 · 2 citations
Earth and Planetary Sciences · Economics, Econometrics and Finance · Mathematics · Physics and Astronomy · #Aftershock #Complex Systems and Time Series Analysis #Earthquake Detection and Analysis #Entropy (arrow of time) #Exponent #Geology #Induced seismicity #Materials science #Mathematics #Physics #Poisson distribution #Power law #Range (aeronautics) #Seismology #Statistical Mechanics and Entropy #Statistical physics #Statistics #Thermodynamics #cond-mat.stat-mech
paper · pdf · doi:10.1103/physrevlett.90.188501
published as Phys. Rev. Lett. 90 (2003) 188501 · 4 pages, 3 figures. Revised version accepted for publication. Typos corrected, more detailed discussion on the method used, refs added. Phys. Rev. Lett. (2003) in press
arxiv created 2003/04/07 · openalex publication_date 2003/05/09 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We study the statistical properties of time distribution of seismicity 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+/-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.