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Probability Gain due to Foreshocks following Quiescence Tested by Synthetic Catalogs
Language
English
Obiettivo Specifico
Status
Published
JCR Journal
JCR Journal
Peer review journal
Yes
Title of the book
Issue/vol(year)
3/86(1996)
Pages (printed)
911-913
Issued date
June 1996
Subjects
foreshocks as short-term predictors
Keywords
Abstract
The database obtained from the observations of the Italian Seismological
Network over more than 15 yr was analyzed by a simple algorithm to determine
the rate of occurrence of major events after the precursors called foreshocks: moderate
earthquakes following a period of quiescence. The number of observed foreshocks
depends, among other factors, on the spatial (R1) and temporal (/'1) ranges of
the quiescence used to define the foreshocks. These parameters can be optimized to
get the highest value of the success rate of the precursor for a given data set. In our
case, the optimization process led to a probability gain (G) higher than 100. In order
to estimate the factors that influence the value of G, we tested our method on synthetic
catalogs obtained from the real one by randomizing the origin times of the events,
or the spatial coordinates, or both of them. The analysis showed that the apparently
high value of the probability gain obtained optimizing the parameters. R1 and T 1 is
the result of (a) shortness of the catalog, (b) uneven spatial distribution of seismicity,
and (c) real variation in time of the occurrence rate.
Network over more than 15 yr was analyzed by a simple algorithm to determine
the rate of occurrence of major events after the precursors called foreshocks: moderate
earthquakes following a period of quiescence. The number of observed foreshocks
depends, among other factors, on the spatial (R1) and temporal (/'1) ranges of
the quiescence used to define the foreshocks. These parameters can be optimized to
get the highest value of the success rate of the precursor for a given data set. In our
case, the optimization process led to a probability gain (G) higher than 100. In order
to estimate the factors that influence the value of G, we tested our method on synthetic
catalogs obtained from the real one by randomizing the origin times of the events,
or the spatial coordinates, or both of them. The analysis showed that the apparently
high value of the probability gain obtained optimizing the parameters. R1 and T 1 is
the result of (a) shortness of the catalog, (b) uneven spatial distribution of seismicity,
and (c) real variation in time of the occurrence rate.
References
Agnew, D. C. and L. M. Jones (1991). Prediction probabilities for foreshocks,
J. Geophys. Res. 96, 11959-11971.
Aki, K. (1981). A probabilistic synthesis of precursory phenomena, in
Earthquake Prediction: An International Review, Maurice Ewing Series
4, D. W. Simpson and P. G. Richards (Editors), American Geophysical
Union, Washington, D.C., 566-574.
Console, R., M. Murru, and B. Alessandrini (1993a). Foreshock statistics
and their possible relationship to earthquake prediction in the Italian
region, Bull. Seism. Sac. Am. 83, 1248-1263.
Console, R., M. Murru, and B. Alessandrini (1993b). Statistical short-term
earthquake prediction in the Italian region, Proc. of the 2rid Workshop
on Statistical Models and Methods in Seismology Applications on
Prevention and Forecasting of Earthquakes, European Seismological
Commission, 38-41.
Jones, L. M. (1985). Foreshocks and time-dependent earthquake hazard
assessment in Southern California, Bull. Seism. Soc. Am. 75, 1669-1680.
Reasenberg, P. A. and M. V. Matthews (1988). Precursory seismic quiescence:
a preliminary assessment of the hypothesis, Pageoph 126, 373-406.
J. Geophys. Res. 96, 11959-11971.
Aki, K. (1981). A probabilistic synthesis of precursory phenomena, in
Earthquake Prediction: An International Review, Maurice Ewing Series
4, D. W. Simpson and P. G. Richards (Editors), American Geophysical
Union, Washington, D.C., 566-574.
Console, R., M. Murru, and B. Alessandrini (1993a). Foreshock statistics
and their possible relationship to earthquake prediction in the Italian
region, Bull. Seism. Sac. Am. 83, 1248-1263.
Console, R., M. Murru, and B. Alessandrini (1993b). Statistical short-term
earthquake prediction in the Italian region, Proc. of the 2rid Workshop
on Statistical Models and Methods in Seismology Applications on
Prevention and Forecasting of Earthquakes, European Seismological
Commission, 38-41.
Jones, L. M. (1985). Foreshocks and time-dependent earthquake hazard
assessment in Southern California, Bull. Seism. Soc. Am. 75, 1669-1680.
Reasenberg, P. A. and M. V. Matthews (1988). Precursory seismic quiescence:
a preliminary assessment of the hypothesis, Pageoph 126, 373-406.
Description
In order to investigate the factors that could contribute
to the high values of the probability gain for foreshocks, we
make use of synthetic catalogs generated by a computer program
(Console et al., 1993b). The algorithm was prepared
assuming that the magnitude distribution of the synthetic
catalog should be the same as that of the real one.
to the high values of the probability gain for foreshocks, we
make use of synthetic catalogs generated by a computer program
(Console et al., 1993b). The algorithm was prepared
assuming that the magnitude distribution of the synthetic
catalog should be the same as that of the real one.
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