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We consider P-wave perturbations from a standard layered model for Japan, as a predictive parameter that may be useful for assessing regional seismogenesis. To assess the performance of a seismicity model with predictive parameters, we used the Kullback-Leibler statistic in terms of information gain per event (IGpe), which is the distance between two distributions of parameters, the background distribution (parameters over the entire space domain), and the conditional distribution (parameters at earthquake epicenters). We selected 198 epicenters of earthquakes with magnitudes ≥5.0 that occurred between 1961 and 2008 to estimate the conditional distribution. More than 3,000 points were selected at every point on a 0.1 × 0.1° grid for the background distribution. P-wave variations were considered at four different depths (10, 15, 20, and 25 km at each point) for both distributions. We compared the two distributions at each depth but found no significant difference in the average value of perturbations between them. As these distributions are well-approximated by normal distributions, IGpe can be estimated directly from the means and standard deviations of both distributions at each depth. We obtained an IGpe of ≤0.03 using a single parameter. However, when multiple parameters with correlations were considered, an IGpe of 0.3 was estimated, which means that the average probability across the 198 earthquakes is 1.35-fold higher than that of a Poisson process model.

Performance of a seismicity model for earthquakes in Japan (M ≥ 5.0) based on P-wave velocity anomalies