Rank-Ordering Statistics of Extreme Events: Application to the Distribution of Large Earthquakes
Didier Sornette, Leon Knopoff, Yan Kagan, Christian Vanneste

TL;DR
This paper uses rank-ordering statistics to analyze the distribution of large earthquakes, revealing a potential transition in magnitude distribution and questioning the universality of power-law behavior.
Contribution
It introduces a method to accurately estimate power-law exponents from few large events and examines the earthquake magnitude distribution with a two-branch model.
Findings
Identifies a transition in earthquake magnitude distribution between small and large events.
Provides more precise estimates of b-values for different earthquake size ranges.
Suggests that a gamma distribution may better fit earthquake data than a pure power law.
Abstract
Rank-ordering statistics provides a perspective on the rare, largest elements of a population, whereas the statistics of cumulative distributions are dominated by the more numerous small events. The exponent of a power law distribution can be determined with good accuracy by rank-ordering statistics from the observation of only a few tens of the largest events. Using analytical results and synthetic tests, we quantify the systematic and the random errors. We also study the case of a distribution defined by two branches, each having a power law distribution, one defined for the largest events and the other for smaller events, with application to the World-Wide (Harvard) and Southern California earthquake catalogs. In the case of the Harvard moment catalog, we make more precise earlier claims of the existence of a transition of the earthquake magnitude distribution between small and…
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