AN OBSERVATIONAL TEST OF THE CRITICAL EARTHQUAKE CONCEPT

Citation
Dd. Bowman et al., AN OBSERVATIONAL TEST OF THE CRITICAL EARTHQUAKE CONCEPT, J GEO R-SOL, 103(B10), 1998, pp. 24359-24372
Citations number
59
Language
INGLESE
art.tipo
Article
Categorie Soggetti
Geochemitry & Geophysics","Geosciences, Interdisciplinary","Astronomy & Astrophysics",Oceanografhy,"Metereology & Atmospheric Sciences
Journal title
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
ISSN journal
2169-9313 → ACNP
Volume
103
Issue
B10
Year of publication
1998
Pages
24359 - 24372
Database
ISI
SICI code
2169-9313(1998)103:B10<24359:AOTOTC>2.0.ZU;2-X
Abstract
We test the concept that seismicity prior to a large earthquake can be understood in terms of the statistical physics of a critical phase tr ansition. In this model, the cumulative seismic strain release increas es as a power law time to failure before the final event. Furthermore, the region of correlated seismicity predicted by this model is much g reater than would be predicted from simple elastodynamic interactions. We present a systematic procedure to test for the accelerating seismi city predicted by the critical point model and to identify the region approaching criticality, based on a comparison between the observed cu mulative energy (Benioff strain) release and the power law behavior pr edicted by theory. This method is used to find the critical region bef ore all earthquakes along the San Andreas system since 1950 with M gre ater than or equal to 6.5. The statistical significance of our results is assessed by performing the same procedure on a large number of ran domly generated synthetic catalogs. The null hypothesis, that the obse rved acceleration in all these earthquakes could result from spurious patterns generated by our procedure in purely random catalogs, is reje cted with 99.5% confidence. An empirical relation between the logarith m of the critical region radius (R) and the magnitude of the final eve nt (M) is found, such that log R proportional to 0.5M, suggesting that the largest probable event in a given region scales with the size of the regional fault network.