Abstract
We constructed a model of earthquakes (M ? 5.0) in Kanto, central Japan, based on three parameters: the a and b values of the Gutenberg-Richter relation, and the ?- parameter of changes in mean event size. In our method, two empirical probability densities for each parameter, those associated with target events (conditional density distributions) and those not associated with them (background density distributions), are defined and assumed to have a normal distribution. Therefore, three parameters are transformed by appropriate relations so that new parameters are normally distributed. The retrospective analysis in the learning period and the prospective test of testing period demonstrated that the proposed model performs better by about 0.1 units in terms of the information gain per event than the value summed up with those of the three parameters. The results are confirmed by a simulation with randomly selected model parameters.
Highlights
A number of researchers (Utsu, 1977, 1982; Rhoades and Evison, 1979; Aki, 1981; Hamada, 1983; Grandori, et al, 1988) have formulated expressions of earthquake probabilities based on precursory anomalies detected by multidisciplinary observation
He further considered the effects originating from mutual correlations between two precursory anomalies, where he assumed two distributions for each precursory parameter: those associated with only space-time volumes in the vicinity of target events and those associated with space-time volumes excluding target events within a short distance
This paper introduces a way to combine multi-disciplinary observations into one hazard function and demonstrates its superior performance to that expected from the well known formula of Aki and others
Summary
A number of researchers (Utsu, 1977, 1982; Rhoades and Evison, 1979; Aki, 1981; Hamada, 1983; Grandori, et al, 1988) have formulated expressions of earthquake probabilities based on precursory anomalies detected by multidisciplinary observation. Their formulas assume that different precursory phenomena occur independently of each other. We estimate IGpe for the parameters and combinations of the parameters, which are compared with values estimated from data in both the learning period and a testing period
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