Statistics – Computation
Scientific paper
May 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011geoji.185.1037r&link_type=abstract
Geophysical Journal International, Volume 185, Issue 2, pp. 1037-1048.
Statistics
Computation
Probabilistic Forecasting, Earthquake Interaction, Forecasting, And Prediction, Computational Seismology, Statistical Seismology, Fractures And Faults
Scientific paper
The Every Earthquake a Precursor According to Scale (EEPAS) model has performed well in retrospective studies of long-range forecasting of major earthquakes in a number of real earthquake catalogues. It is based on the precursory scale increase phenomenon and associated predictive scaling relations, the detailed physical basis of which is not well understood. Synthetic earthquake catalogues generated deterministically from known fault physics and long- and short-range stress interactions on fault networks have been analysed using the EEPAS model, to better understand the physical process responsible for the precursory scale increase phenomenon. In a generic fault network with one major fault and a number of parallel minor faults, the performance of the EEPAS model is poor. But in a more elaborate network involving major faults at a variety of orientations and a large number of small, randomly oriented faults, individual examples of the precursory scale increase phenomenon can be readily identified and the performance of the EEPAS model is similar to that in real catalogues, such as those of California and central Japan, albeit with some differences in the scaling parameters for precursor time and area. The fault geometry therefore affects conformity to the EEPAS model. Tracking the stress evolution on a set of individual cells in the synthetic seismicity model may give new insights into the origin of the precursory scale increase phenomenon.
Gerstenberger M. C.
Rhoades D. A.
Robinson Rebecca
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