1998/03/16 by D. D. Bowman, Guy Ouillon, G. Ouillon +4 · 4 citations
Earth and Planetary Sciences · Mathematics · Physics and Astronomy · #Critical point (mathematics) #Criticality #Earthquake Detection and Analysis #Event (particle physics) #Geology #Geometry #High-pressure geophysics and materials #Induced seismicity #Logarithm #Magnitude (astronomy) #Mathematics #Nuclear physics #Physics #Power law #Seismology #Spurious relationship #Statistical hypothesis testing #Statistical physics #Statistics #cond-mat.stat-mech #earthquake and tectonic studies
paper · pdf · doi:10.1029/98jb00792
published as J.Geophys. Res. 103, (NB10), 24359-24372 (1998). · 17 pages, 9 figures, 1 table, accepted in Journal of Geophysical Research (special issue on 'Stress Triggers, Stress Shadows, and Implications for Seismic Hazard')
arxiv created 1998/03/16 · openalex publication_date 1998/10/10 · arxiv updated 2015/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We test the concept that seismicity prior to a large earthquake can be understood in terms of the statistical physics of a critical phase transition. In this model, the cumulative seismic strain release increases as a power law time to failure before the final event. Furthermore, the region of correlated seismicity predicted by this model is much greater than would be predicted from simple elastodynamic interactions. We present a systematic procedure to test for the accelerating seismicity predicted by the critical point model and to identify the region approaching criticality, based on a comparison between the observed cumulative energy (Benioff strain) release and the power law behavior predicted by theory. This method is used to find the critical region before all earthquakes along the San Andreas system since 1950 with M ≥6.5. The statistical significance of our results is assessed by performing the same procedure on a large number of randomly generated synthetic catalogs. The null hypothesis, that the observed acceleration in all these earthquakes could result from spurious patterns generated by our procedure in purely random catalogs, is rejected with 99.5% confidence. An empirical relation between the logarithm of the critical region radius ( R ) and the magnitude of the final event ( M ) is found, such that log R ∝0.5 M , suggesting that the largest probable event in a given region scales with the size of the regional fault network.