MODELLING THE ENERGY RELEASE PROCESS OF AFTERSHOCKS


D. Gospodinov
Résumé

A stochastic model for the study of Benioff strain release during aftershock sequences is suggested. The stochastic model is elaborated after a compound Poisson process and is applied on data of the M7.1 Ocober 18, 1989 Loma Prieta aftershock sequence in northern California, USA. The temporal evolution of the number of events is first modelled by the Restricted Epidemic Type Aftershock Sequence (RETAS) model and then the identified best fit model is incorporated in the energy release analysis. The suggested model is based on the assumptions that there is no relation between the magnitude and the occurrence time of an event first and second, that there is no relation between the magnitude of a certain event and magnitudes of previous events. The obtained results from the examination of the energy release reveal that the suggested model makes a good fit of the aftershock Benioff strain release and enables a more detailed study by identifying possible deviations between data and model. The real cumulative energy release values surpass the expected model ones, which proves that aftershocks, stronger than forecasted by the model, are clustered at the beginning of the Loma Prieta sequence.

Article Details
  • Rubrique
  • Seismology
Téléchargements
Les données relatives au téléchargement ne sont pas encore disponibles.
Références
Akaike, H., 1974. A new look at the statistical model identification, IEEE Trans Autom Control AC,
, 716-723.
Bhattacharya, P., Kamal and Chakrabarti, B., 2009. The time distribution of aftershock magnitudes,
fault geometry and aftershock prediction, arXiv: 0910.3738 [physics.geo-ph].
Dietz, L. and Ellsworth, W., 1991. LOMA PRIETA DATA ARCHIVE, National Information Service
for Earthquake Engineering (NISEE), http://nisee.berkeley.edu/lp_archive/aftershk.html.
Drakatos, G., 2000. Relative seismic quiescence before large aftershocks, Pure Appl. Geophys. 157,
-1421.
Gospodinov, D. and Rotondi, R., 2006. Statistical analysis of triggered seismicity in the Kresna
Region of SW Bulgaria (1904) and the Umbria-Marche Region of Central Italy (1997), Pure
Appl. Geophys. 163, 1597-1615.
Gospodinov D., Papadimitriou E., Karakostas, V. and Ranguelov, B., 2007. Analysis of relaxation
temporal patterns in Greece through the RETAS model approach, Phys. Earth Planet. Inter.,
/3-4, 158-175, doi: 10.1016/j.pepi.2007.09.001.
Ogata, Y., 1988. Statistical models for earthquake occurrences and residual analysis for point
processes, J. Am. Stat. Assoc., 83, 9-27.
Ogata, Y., 1998. Space-time point-process models for earthquake occurrences, Ann. Inst. Stat.
Math., 50, 379-402.
Ogata, Y., Jones, L.M. and Toda, S., 2003. When and where the aftershock activity was depressed:
contrasting decay patterns of the proximate large earthquakes in southern California, J.
Geophys. Res., 108, 2318, doi: 10.1029/2002JB002009.
Snyder, D. and Miller, M., 1991. Random point processes in time and space, Springer-Verlag, 481 pp.
Taylor, H. and Karlin, S., 1984. An introduction to stochastic modeling: Academic Press, Inc., 399 pp.
Tzanis, A. and Vallianatos, F., 2003. Distributed power-law seismicity changes and crustal
deformation in the SW Hellenic arc, Natural Hazards and Earth System Sciences, 3, 179-198.
Utsu, T., 1970. Aftershocks and earthquake statistics (II): further investigation of aftershocks and
other earthquake sequences based on a new classification of earthquake sequences, J. Fac.
Sci., Hokkaido Univ., Ser. VII (Geophys.) 3, 198-266.
Articles les plus lus par le même auteur ou la même autrice