Recurrent frequency-size distribution of characteristic events

TL;DR

This paper examines the recurrent frequency-size distribution of characteristic events in complex systems like faults, finding Weibull distribution best models these events, supported by empirical data and models.

Contribution

It introduces the Weibull distribution as the best-fit model for recurrent characteristic events in fault systems, validated through empirical and simulation data.

Findings

Weibull distribution fits recurrent slip event data well.

Exponent values of Weibull distribution range from 1.6 to 2.2.

Results are consistent across empirical data and models.

Abstract

Many complex systems, including sand-pile models, slider-block models, and earthquakes, have been discussed whether they obey the principles of self-organized criticality. Behavior of these systems can be investigated from two different points of view: interoccurrent behavior in a region and recurrent behavior at a given point on a fault or at a given fault. The interoccurrent frequency-size statistics are known to be scale-invariant and obey the power-law Gutenberg-Richter distribution. This paper investigates the recurrent frequency-size behavior of characteristic events at a given point on a fault or at a given fault. For this purpose sequences of creep events at a creeping section of the San Andreas fault are investigated. The applicability of the Brownian passage-time, lognormal, and Weibull distributions to the recurrent frequency-size statistics of slip events is tested and the Weibull distribution is found to be a best-fit distribution. To verify this result the behaviors of the numerical slider-block and sand-pile models are investigated and the applicability of the Weibull distribution is confirmed. Exponents of the best-fit Weibull distributions for the observed creep event sequences and for the slider-block model are found to have close values from 1.6 to 2.2 with the corresponding aperiodicities of the applied distribution from 0.47 to 0.64.