Self-Organized Earthquakes

Details Self-Organized Earthquakes Self-Organized Earthquakes

Dec 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011agufmsh43e..01r&link_type=abstract

American Geophysical Union, Fall Meeting 2011, abstract #SH43E-01

Physics

Geophysics

[4425] Nonlinear Geophysics / Critical Phenomena, [4312] Natural Hazards / Catastrophe And Catastrophe Theory

Scientific paper

Self-Organized Criticality was proposed by the Per Bak et al. [1] as a means of explaining scaling laws observed in driven natural systems, usually in (slowly) driven threshold systems. The example used by Bak was a simple cellular automaton model of a sandpile, in which grains of sand were slowly dropped (randomly) onto a flat plate. After a period of time, during which the 'critical state' was approached, a series of self-similar avalanches would begin. Scaling exponents for the frequency-area statistics of the sandpile avalanches were found to be approximately 1, a value that characterizes 'flicker noise' in natural systems. SOC is associated with a critical point in the phase diagram of the system, and it was found that the usual 2-scaling field theory applies. A model related to SOC is the Self-Organized Spinodal (SOS), or intermittent criticality model. Here a slow but persistent driving force leads to quasi-periodic approach to, and retreat from, the classical limit of stability, or spinodal. Scaling exponents for this model can be related to Gutenberg-Richter and Omori exponents observed in earthquake systems. In contrast to SOC models, nucleation, both classical and non-classical types, is possible in SOS systems. Tunneling or nucleation rates can be computed from Langer-Klein-Landau-Ginzburg theories for comparison to observations. Nucleating droplets play a role similar to characteristic earthquake events. Simulations of these systems reveals much of the phenomenology associated with earthquakes and other types of "burst" dynamics. Whereas SOC is characterized by the full scaling spectrum of avalanches, SOS is characterized by both system-size events above the nominal frequency-size scaling curve, and scaling of small events. Applications to other systems including integrate-and-fire neural networks and financial crashes will be discussed. [1] P. Bak, C. Tang and K. Weisenfeld, Self-Organized Criticality, Phys. Rev. Lett., 59, 381 (1987).

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