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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 5/6
Nonlin. Processes Geophys., 9, 453–461, 2002
https://doi.org/10.5194/npg-9-453-2002
© Author(s) 2002. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Nonlin. Processes Geophys., 9, 453–461, 2002
https://doi.org/10.5194/npg-9-453-2002
© Author(s) 2002. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  31 Dec 2002

31 Dec 2002

A simple model for the earthquake cycle combining self-organized complexity with critical point behavior

W. I. Newman1 and D. L. Turcotte2 W. I. Newman and D. L. Turcotte
  • 1Departments of Earth and Space Sciences, Physics and Astronomy, and Mathematics, University of California, Los Angeles, CA 90095, USA
  • 2Department of Earth and Atmospheric Sciences, Snee Hall, Cornell University, Ithaca, NY 14853, USA

Abstract. We have studied a hybrid model combining the forest-fire model with the site-percolation model in order to better understand the earthquake cycle. We consider a square array of sites. At each time step, a "tree" is dropped on a randomly chosen site and is planted if the site is unoccupied. When a cluster of "trees" spans the site (a percolating cluster), all the trees in the cluster are removed ("burned") in a "fire." The removal of the cluster is analogous to a characteristic earthquake and planting "trees" is analogous to increasing the regional stress. The clusters are analogous to the metastable regions of a fault over which an earthquake rupture can propagate once triggered. We find that the frequency-area statistics of the metastable regions are power-law with a negative exponent of two (as in the forest-fire model). This is analogous to the Gutenberg-Richter distribution of seismicity. This "self-organized critical behavior" can be explained in terms of an inverse cascade of clusters. Small clusters of "trees" coalesce to form larger clusters. Individual trees move from small to larger clusters until they are destroyed. This inverse cascade of clusters is self-similar and the power-law distribution of cluster sizes has been shown to have an exponent of two. We have quantified the forecasting of the spanning fires using error diagrams. The assumption that "fires" (earthquakes) are quasi-periodic has moderate predictability. The density of trees gives an improved degree of predictability, while the size of the largest cluster of trees provides a substantial improvement in forecasting a "fire."

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