Deviation from power law of the global earthquake seismic moment distribution

TL;DR

This study evaluates the distribution of earthquake seismic moments, finding that a truncated gamma model better explains the data than traditional power-law models, especially around major events like the 2004 Sumatra earthquake.

Contribution

It introduces a statistical comparison showing the truncated gamma model more accurately fits global earthquake data than the Gutenberg-Richter power law.

Findings

Truncated gamma model outperforms power-law models in fitting seismic data.

The model explains data before and after major earthquakes like 2004 Sumatra.

Likelihood-ratio tests support the truncated gamma as the best fit.

Abstract

The distribution of seismic moment is of capital interest to evaluate earthquake hazard, in particular regarding the most extreme events. We make use of likelihood-ratio tests to compare the simple Gutenberg-Richter power-law distribution with two statistical models that incorporate an exponential tail: the so-called tapered Gutenberg-Richter and the truncated gamma, when fitted to the global CMT earthquake catalog. The outcome is that the truncated gamma model outperforms the other two models. If simulated samples of the truncated gamma are reshuffled in order to mimic the time occurrence of the order statistics of the empirical data, this model turns out to be able to explain the empirical data both before and after the great Sumatra-Andaman earthquake of 2004.