 Article
 Open Access
Statistical forecasts and tests for small interplate repeating earthquakes along the Japan Trench
 Masami Okada^{1}Email author,
 Naoki Uchida^{2} and
 Shigeki Aoki^{1}
https://doi.org/10.5047/eps.2011.02.008
© The Society of Geomagnetism and Earth, Planetary and Space Sciences (SGEPSS); The Seismological Society of Japan; The Volcanological Society of Japan; The Geodetic Society of Japan; The Japanese Society for Planetary Sciences; TERRAPUB. 2012
 Received: 2 July 2010
 Accepted: 17 February 2011
 Published: 27 August 2012
Abstract
Earthquake predictability is a fundamental problem of seismology. Using a sophisticated model, a Bayesian approach with lognormal distribution on the renewal process, we theoretically formulated a method to calculate the conditional probability of a forthcoming recurrent event and forecast the probabilities of small interplate repeating earthquakes along the Japan Trench. The numbers of forecast sequences for 12 months were 93 for July 2006 to June 2007, 127 for 2008, 145 for 2009, and 163 for 2010. Forecasts except for 2006–07 were posted on a web site for impartial testing. Consistencies of the probabilities with catalog data of two early experiments were so good that they were statistically accepted. However, the 2009 forecasts were rejected by the statistical tests, mainly due to a large slow slip event on the plate boundary triggered by two events with M 7.0 and M 6.9. All 365 forecasts of the three experiments were statistically accepted by consistency tests. Comparison tests and the relative/receiver operating characteristic confirm that our model has significantly higher performance in probabilistic forecast than the exponential distribution model on the Poisson process. Therefore, we conclude that the occurrence of microrepeaters is statistically dependent on elapsed time since the last event and is not random in time.
Key words
 Earthquake predictability
 small repeating earthquake
 probabilistic forecast
 test of forecast
 interplate earthquake
1. Introduction
Earthquake periodicity and seismic gaps have been used for longterm forecasts of large earthquakes in various regions (e.g., Imamura, 1928; Sykes, 1971; Kelleher, 1972; Kelleher et al., 1973; McCann et al., 1979; Working Group on California Earthquake Probabilities (WGCEP), 1988, 1990, 1995, 2003; Nishenko, 1991; Earthquake Research Committee (ERC), 2001; Matsuzawa et al., 2002; Field, 2007; Field et al., 2009). McCann et al. (1979) gave forecasts for specified ranked categories of earthquake potentials for most of the Pacific Rim. Nishenko (1991) presented the first global probabilities of either large or great interplate earthquakes in 97 segments of simple plate boundaries around the circumPacific region during the next 5, 10, and 20 years, in terms of conditional probability based on elapsed time since the last event and mean recurrence time with a lognormal distribution model. Rigorous tests of Nishenko’s forecasts were conducted by Kagan and Jackson (1995) for 5 years and by Rong et al. (2003) for 10 years by using the seismic catalogs of the Preliminary Determination of Epicenters (PDE) of the U.S. Geological Survey and the Harvard Centroid Moment Tensor (CMT). They statistically rejected Nishenko’s forecasts with the number test (Ntest), the likelihood test (Ltest), and the likelihood ratio test (Rtest). The predicted events in both periods were too numerous to result from random variation. As reasons for failure, they suggested biasing of the estimated earthquake rate and excluding effects of open intervals before the first event and after the last event.
Davis et al. (1989) indicated that parameter uncertainties affect seismic potential estimates strongly for some distributions (e.g., the lognormal) and weakly for the Poisson distribution. The method used by Nishenko is too crude to reflect the parameter estimation errors derived from the small number of samples on the probabilities. Official forecasts by WGCEP and ERC have not yet been tested statistically, as the forecast periods are not yet over.
In this paper, we study the predictability of recurrent earthquakes, applying sophisticated methods based on the Bayesian approach or small sample theory with lognormal distribution. The small repeating earthquakes (SREs) used in this study occur on the plate boundary in the same condition for large interplate earthquakes. The SRE data is much more suitable than large recurrent event data for experiments of prospective probabilistic forecasts for three reasons: (1) events are objectively qualified and accurate in time; (2) the recurrence intervals are short; and (3) the catalog of events is compiled based on a stable observation network and contains many sequences to test forecasts statistically.
More than 1000 characteristic sequences or clusters of SREs with nearly identical waveform have been found near the east coast of NE Japan since 1984 (36.5–41.5 deg. N) or 1993 (41.5–43.5 deg. N and 34.5–36.5 deg. N) (Igarashi et al., 2003; Uchida et al., 2003). These repeaters in a cluster are assumed to occur on the same small asperity surrounded by an aseismic creeping zone on the plate boundary. The forecast bin specifying an event may be smaller in volume of location and in focal mechanism, but the magnitude range for a sequence may be larger than those for Regional Earthquake Likelihood Models (RELM) by Schorlemmer et al. (2007) and for the Collaboratory for the Study of Earthquake Predictability (CSEP, Jordan, 2006). We estimated the probabilities for repeaters in the forecast period of a year using a Bayesian model with lognormal distribution. There were 93 sequences for July 2006 to June 2007, 127 for 2008, 145 for 2009, and 163 for 2010 that were selected for the forecast. The repeaters occurred from 1993 until the forecast time were used to calculate the forecast probability. The forecast sequences consisted of five events or more. With the exception of the results of the first experiment (2006–2007), those probabilities were posted on a web site for impartial forecast and testing.
Comparing forecasts with a seismic catalog on repeater data, we tested probabilities with not only N and Ltests but also with the test of Brier score (Brier, 1950). We pay attention to whether the next qualifying event in the sequence will occur in the forecast period or not, regardless the event timing within the forecast period. Alternative forecasts were computed with the lognormal distribution model based on the small sample theory and the exponential distribution model based on the Poisson process. The three models were compared using the Rtest and the test of difference in Brier scores.
2. Theory
We assume that (n + 1) events of a sequence have occurred, separated by n time intervals T_{ i }, and that the time elapsed since the last event is T_{ p }. The unknown recurrence interval from the last event to the upcoming one is denoted as T_{ n }+1.
2.1 Bayesian approach
Maximum likelihood estimates may be biased when there are few samples and a wide range of values are consistent with the observation (Davis et al., 1989). We will not determine the parameters with the maximum likelihood method or the least square method. Instead, we directly estimate the conditional probabilities, P_{ q }, in Eq. (1) by the Bayesian approach.
Let us consider here the prior distribution. We adopted the uniform prior distribution for parameter µ since it varies in an infinite interval, −∞ <µ< ∞.For σ^{2} varying in a semiinfinite interval, 0 <σ^{2}< ∞, two types were studied. One is a natural conjugate prior distribution, inverse gamma, Γ_{ R } (ϕ,ζ), and the other is the noninformative prior distribution, inverse of σ^{2}, proposed by Jeffreys (1961). Parameter ϕ indicates shape and ζ indicates scale for inverse gamma.
2.2 Small sample theory
 (1)
 (2)
The variable nS^{2}/σ^{2} follows a chisquared distribution with n − 1 degrees of freedom.
 (3)
At forecasting time, we have n data, and the mean and variance of samples, and s^{2}, corresponding to and S^{2}, can be calculated. Thus, we naturally expect that the variable follows a tdistribution with n − 1 degrees of freedom. Therefore, the probability based on the small sample theory (exact sampling theory) is calculated by the Bayesian approach with Jeffreys’ noninformative prior distribution.
2.3 Exponential distribution model
If the event occurs uniformly and randomly, the probability of an event does not depend on the elapsed time since the last event, and the recurrence interval between successive events is distributed exponentially. Conditional probability is given by , where is the average of observed recurrence intervals.
3. Small Repeating Earthquakes
It has been pointed out that SREs are caused by the repeated rupture of small asperities within the creeping zone of a fault plane (e.g., Nadeau et al., 1995; Igarashi et al., 2003; Uchida et al., 2003). We use the waveform similarity of earthquakes with a magnitude of 2.5 or larger to identify repeaters in the subduction zone between the Japan Trench and the east coast of NE Japan. Maximum magnitude is practically about 5 mainly owing to the employed waveform similarity threshold that is applicable for small earthquakes. The SRE was objectively selected by the threshold of waveform coherence in a 40second waveform that contains both P and S phases. Details of the methods for identifying SRE and for compiling the SRE catalog are described by Uchida et al. (2009).
4. Forecasts and Observations
Fundamental values of forecasts and consistency scores of the probabilities for 12 months.
Forecast period  

2006.7–07.6  2008.1–08.12  2009.1–09.12  2010.1–10.12  Total  
Forecast sequences  93  127  145  163  528  
Qualifying events  51  56  70  177  
Analysis for forecast  
Data period  1993/1–06/6  1993/1–07/12  1993/1–08/12  1993/1–09/12  
SREs  671  912  1075  1239  
Model  Score/Verification  
LNBayes  E(N)  41.4 UD  56.1 AC  61.2 AC  158.7 UD  
MLL  −0.519 AC  −0.531 AC  −0.647 RJ  −0.574 AC  
BS  0.177 AC  0.178 AC  0.228 RJ  0.197 AC  
LNSST  E(N)  39.4 RJ  53.7 AC  58.0 UD  151.1 RJ  
MLL  −0.526 AC  −0.497 AC  −0.678 RJ  −0.576 UD  
BS  0.182 AC  0.167 AC  0.235 RJ  0.198 RJ  
Exp  E(N)  39.7 UD  49.9 AC  55.4 RJ  145.0 RJ  
MLL  −0.669 AC  −0.638 AC  −0.696 UD  −0.669 AC  
BS  0.238 AC  0.223 AC  0.251 UD  0.238 AC 

LNBayes: A Bayesian approach for lognormal distribution of the recurrence interval with an inverse gamma prior distribution. The probability forecast by this model is given by Eq. (6). The parameters of inverse gamma were ϕ = 2.5 and ζ = 0.44 (Okada et al., 2007) for the two earlier trials. They were changed to ϕ = 1.5 and ζ = 0.15 for the two later trials, based on the Rtest result for the 2008 probabilities calculated by LNBayes and by LNSST. Determination of these parameters is briefly explained in Appendix.

LNSST: Lognormal distribution model based on the small sample theory. The probability forecast by this model is given by Eq. (7). This is an alternative model to determine consistency with LNBayes.

EXP: Exponential distribution model based on the Poisson process. The probability of an event is independent of the elapsed time since the last event. This is the alternative model to compare with the two timedependent models, LNBayes and LNSST.
5. Forecast Verification
Three tests (N, L, and Rtests) were applied by Kagan and Jackson (1995) and Rong et al. (2003) to the probabilistic forecasts by Nishenko (1991). Those tests are a fundamental procedure to evaluate the rates of earthquakes forecast by various models submitted to the Regional Earthquake Likelihood Models Center (Schorlemmer et al., 2007). Moreover, we use different verification methods presented by Jolliffe and Stephenson (2003) for the probabilistic forecasts of binary events. In the following sections, we assume that events are independent from seismic activities in other clusters.
5.1 Reliability and resolution
The values of reliability are 0.00091 for LNBayes, 0.00105 for LNSST, and 0.0019 for EXP. And the resolutions are 0.0590 for LNBayes, 0.0616 for LNSST, and 0.0254 for EXP. These results indicate that forecast probabilities by those models are not so biased and that their reliability may be fairly good. The forecast by the LNBayes model is slightly better in reliability but worse in resolution than those by the LNSST model. The gray bars in the top panel in Fig. 5 are slightly more consistent with the black ones and are more concentrated than those in the middle panel. The EXP model is much lower in resolution than others, as shown in bottom panel in Fig. 5 the probabilities are apt to gather in some ranges.
5.2 Relative/Receiver Operating Characteristic
5.3 Consistency test
We use three scores and related tests, based on the total number of qualifying events (Ntest), the likelihood score (Ltest), and the Brier score (BStest) (Brier, 1950). For the consistency test, the score of observation data is compared with the theoretical score distribution that is computed from the forecast probabilities. Those scores and the test results are summarized in Table 2.
5.4 Comparison test
We statistically compare the forecasting model (H1) and the alternative model (H0) by the Rtest, which is based on the differences in LL, and the dBStest, which is based on the differences in BS. Theoretical score distributions used for these tests are numerically computed by a procedure similar to that applied for LL.
Scores of R (= LL1−LL0) and dBS (= BS1−BS0) and results of tests for forecast probabilities for 12 months.
Forecast model, H1  Alternative model, H0  Forecast period  

2006.7–07.6  2008.1–08.12  2009.1–09.12  Total  
Rtest  
LNBayes  EXP  13.9 ACRJ  13.5 ACRJ  7.09 ACRJ  34.5 ACRJ 
LNSST  EXP  13.2 ACRJ  17.9 ACRJ  2.72 RJRJ  33.9 ACRJ 
LNBayes  LNSST  0.640 ACAC  −4.39 RJAC  4.39 ACRJ  0.618 UDUD 
dBStest  
LNBayes  EXP  −0.0609 ACRJ  −0.0458 ACRJ  −0.0230 ACRJ  −0.0406 ACRJ 
LNSST  EXP  −0.0558 ACRJ  −0.567 ACRJ  −0.158 UDRJ  −0.0402 ACRJ 
LNBayes  LNSST  −0.0051 ACUD  0.0109 RJAC  −0.0072 ACRJ  −0.00035 ACUD 
6. Discussion
To evaluate the prospective forecasts, we consider the following four sets of forecasts: (1) 93 probabilities for the first trial (July 2006 to June 2007), (2) 127 probabilities for the second trial (2008), (3) 145 probabilities for the third trial (2009), and (4) 365 combined probabilities for all three trials. The results of the fourth trial (2010) are not available for evaluation because the forecast period was not over at the time of the submission of this paper. Among these sets, (1), (2), and (4) were accepted by consistency tests, the Ltest, and the BStest (Table 2). However, the forecasts of (3) were rejected statistically, as we will discuss later.
The recurrence interval itself is important for forecast. If the interval is less than the forecast period, the probability for the repeater is inevitably high. The ROC in Fig. 6 shows that the EXP model based on Poisson process taking the averaged sequence recurrence interval into account is better than the random forecast. Results of R and dBStests (Fig. 11 and Table 3) indicate that the LNBayes and LNSST models dependent on elapsed time since the last event are significantly better than the EXP model. The ROC curves depicted in Fig. 6 also suggest that these models have much higher performance than the EXP model for estimating probabilities (Figs. 5 and 6 and Tables 2 and 3). Therefore, the repeaters on the plate boundary along the Japan Trench are significantly dependent on elapsed time since the last event and are not random in time. However, it is presumed that the inverse gamma prior distribution used in the LNBayes model is slightly more effective for forecast repeaters, since the differences between the consistency scores of the LNBayes and the LNSST models are very small.
Missing event, especially last one, has significant effect on the forecast probability for the relevant sequence. We collected the SRE by comparing waveform of events recorded at the same station which were listed in the catalog maintained by the Japan Meteorological Agency for 2.5 or larger in magnitude. Nanjo et al. (2010) estimated that the completeness magnitude, M_{c}, for recent event was 1.5 or smaller in the coastal zone and between 2.0 and 2.5 in the offing area near Japan Trench. M_{c} before the deployment of modern dense observation network in 2002 for the northeastern Japan was about 0.5 larger than the recent one. The ratio of signal to noise is smaller for the events in the distant offing than the coastal zone and the fluctuation of noise level also affect the detectability of the small repeating earthquake. Therefore we assume that some older events near the Japan Trench might be missed from our SRE catalog. However the most sequences in coastal zone seem to be nearly complete and our results are considered to be in high quality as a whole as shown in the data quality estimation at off Sanriku region (Uchida et al., 2005).
The forecasts by Nishenko (1991) for large characteristic interplate earthquakes around the circumPacificregion were rigorously tested by Kagan and Jackson (1995) and Rong et al. (2003), and were statistically rejected. They suggested two reasons, the biasing of rate and the effects of excluding open intervals. We also assume that the model in the previous study is too crude for the small number of data. The SRE and the models employed in this study can adequately deal with these problems. The SRE selected on the basis of waveform similarity excludes bias by the selection of sequence; the models using the Bayesian approach and small sample theory adapt fairly well to small samples and the openended interval. We also suggest that the common variance parameter, σ = 0.215, used by Nishenko is probably too small. In our Bayesian model, the mean of prior distribution for σ^{2} is close to 0.3, and the expectation of σ exceeds 0.215 for most sequences.
As the SREs occur in the same geophysical condition for the large/great interplate earthquakes, it is likely that our method is applicable for those recurrent events. A preliminary prior distribution for large events has been proposed by Okada et al. (2007). However it is fairly difficult to test the prospective forecasts for the large/great earthquake due to much longer time interval and small number of recurrent events for each sequence. Moreover we have to pay attention to the uncertainty and errors of old data derived from historical documents or geological surveys. Therefore it might be fruitful to perform the prospective forecast experiments for the moderate recurrent earthquakes with 4 to 7 in magnitude recorded with seismological instruments and to test them with observation data in advance of the experiment for large events.
Next we discuss why the consistency tests rejected our forecasts of 2009. The distribution patterns of clusters in Figs. 1, 3, and 4 in the northern part are somewhat different from those in the southern part. The SRE sequences are crowded near the coast in the northern part and widely distributed in the southern part. In the northern part to the north of 38°N, the score of MLL is −0.614 and that of BS is 0.218 for 97 sequences of the 2009 forecasts, and the expected number of qualifying events of 44.1 is close to the observation of 47. In the southern part, for 48 sequences the score of MLL is as bad as −0.715 and that of BS is as bad as 0.247. Furthermore, the expected number of 17.1 is considerably smaller than the observed 23. Consistency scores for the southern part are considerably worse than those for the northern part.
We also must pay more attention to regional differences and clustering of SRE activities. The frequency of qualifying events within every six months from 1993 to 2009 is 36.0 on average for the 163 sequences used for the 2010 forecasts, and their variance is 68.9, which is much larger than that of Poisson distribution with a mean of 36.0. It is plausible that some variation in SRE activity is caused by the coherent occurrence of repeaters among characteristic sequences, the effects of which are neglected in our models not only for forecast but also testing.
The Brownian Passage Time (BPT) distribution model is frequently used for recurrence interval as a physically based model (e.g., Ellsworth et al., 1999; ERC, 2001; Matthews et al., 2002; WGCEP, 2003). Matthews et al. (2002) discuss the characteristic of Brownian relaxation oscillator and BPT distribution in detail and show the physical interpretation of parameters and the effect of stepwise stress change on recurrence time. But it was rather difficult for us to apply Bayes’ theorem to this distribution. We tried to estimate probabilities, using parameters determined by the maximum likelihood method from the observed data. We failed to obtain the conditional probabilities for some sequences. In several sequences, the open interval from the last event was so long that the CDF was abnormally high. When the sequence contained a doublet (earthquakes with very short interval), the parameters could not be determined by using likelihood with an openended interval denoted by Eq. (2). In one case, we could not calculate CDF for a BPT distribution due to an overflow in computation. The BPT distribution may be suitable for forecast based on the declustered data. We also tried to forecast probabilities with other distributions for the recurrence interval (e.g., Weibull and gamma on the large sample theory); however, the probability for some sequences could not be computed normally, due to the difficulties mentioned above. The probabilities by these models exclusive of abnormal cases were worse than those by LNBayes and LNSST. Therefore, we did not use those distributions in present study, just lognormal and exponential distributions, for forecasting probabilities.
7. Conclusion
We theoretically formulated a method to calculate the conditional probability of a forthcoming recurrent event, using the Bayesian approach of a lognormal distribution model with the uniform prior distribution for the mean of the logarithm of recurrence interval and inverse gamma for its variance (LNBayes), and the model on the small sample theory (LNSST). The probabilities forecast by both models are given by simple equations including tdistribution function.
The probabilities forecast by the LNBayes model for SREs in the subduction zone along the Japan Trench are estimated for 12 months in 2006–07, 2008, and 2009. The results indicate that all forecasts except that for 2009 were so good that they passed the N L and BStests statistically. The 2009 forecasts were rejected by the L and BStests, probably due to a large and longterm afterslip event on the plate boundary triggered by M ∼ 7 earthquakes in 2008. The MLL and BS scores of the SRE forecasts of two former experiments were comparable to those for precipitation several day forecasts at Tokyo, but the 2009 SRE forecasts were worse than the weather forecasts.
Comparison tests, the Rtest and dBStest, for all 365 forecasts in the three experiments indicate that the LNBayes model based on the renewal process had significantly higher performance than the EXP model based on the Poisson process. The ROC curve also indicates that the LNBayes model is remarkably better than the EXP model. Therefore, we conclude that the SREs on the plate boundary are statistically dependent on elapsed time since the last event and are not random in time. However, we assume that the inverse gamma prior distribution for variance used in the LNBayes model is slightly more effective than the LNSST model, although the consistency scores of our experiments are fairly close for the two models.
Declarations
Acknowledgements
We are indebted to two anonymous referees for helpful comments to suggest many improvements in the content and presentation of this report. We thank the members of the Research Center for Prediction of Earthquakes and Volcanic Eruptions, Tohoku University, for valuable discussions and suggestions. We are especially grateful to Prof. T. Matsuzawa, Drs. K. Nanjo, and H. Tsuruoka for their kind encouragement. The digital waveform data from the seismic stations of Hokkaido University, Tohoku University, and University of Tokyo were used in compiling the SRE catalog. We used the software coded by Mr. H. Takayama, which uses the GMT program package developed by Wessel and Smith (1995). The first author has been staying at the Meteorological Research Institute as a guest researcher and was helped by many members, especially Drs. Y. Hayashi, K. Maeda, A. Katsumata, M. Hoshiba, and S. Yoshikawa.
Authors’ Affiliations
References
 Brier, G. W., Verification of forecasts expressed in terms of probability, Month. Weather Rev., 78, 1–3, 1950.View ArticleGoogle Scholar
 Chen, K. H., R. Buergmann, R. M. Nadeau, T. Chen, and N. Lapusta, Postseismic variations in seismic moment and recurrence interval of repeating earthquakes, Earth Planet. Sci. Lett., 299, 118–125, doi:10.1016/j.epsl.2010.08.027, 2010.View ArticleGoogle Scholar
 Davis, P. M., D. D. Jackson, and Y. Y. Kagan, The longer it has been since the last earthquake, the longer the expected time till the next?, Bull. Seismol. Soc. Am., 79, 1439–1456, 1989.Google Scholar
 Earthquake Research Committee, Regarding methods for evaluating longterm probability of earthquake occurrence, 48 pp, 2001 (in Japanese).Google Scholar
 Ellsworth, W. L., M. V. Matthews, R. M. Nadeau, S. P. Nishenko, P. A. Reasenberg, and R. W. Simpson, A physicallybased earthquake recurrence model for estimation of longterm earthquake probabilities, U.S. Geol. Surv., OpenFile Rept., 99–522, http://geopubs.wr.usgs.gov/openfile/of99522/of99522.pdf, 1999.
 Field, E. H., A summary of previous working groups on California earthquake probabilities, Bull. Seismol. Soc. Am., 97, 1033–1053, doi:10.1785/0120060048, 2007.View ArticleGoogle Scholar
 Field, E. H., T. E. Dawson, K. R. Felzer, A. D. Frankel, V. Gupta, T. H. Jordan, T. Parsons, M. D. Petersen, R. S. Stein, R. J. Weldon II, and C. J. Wills, Uniform California earthquake rupture forecast, version 2 (UCERF 2), Bull. Seismol. Soc. Am., 99, 2053–2107, doi:10.1785/0120080049, 2009.View ArticleGoogle Scholar
 Geographical Survey Institute, Crustal movements in the Tohoku district, Rep. Coord. Comm. Earthq. Predict., 83, 59–81, 2010 (in Japanese).Google Scholar
 Igarashi, T., T. Matsuzawa, and A. Hasegawa, Repeating earthquakes and interplate aseismic slip in the northeastern Japan subduction zone, J. Geophys. Res., 108 (B5), 2249, doi:10.1029/2002JB001920, 2003.View ArticleGoogle Scholar
 Imamura, A., On the seismic activity of central Japan, Jpn. J. Astron. Geophys., 6, 119–137, 1928.Google Scholar
 Jeffreys, H., Theory of Probability, third ed., 459 pp, Oxford Univ. Press, 1961.Google Scholar
 Jolliffe, I. T. and D. B. Stephenson, eds., Forecast Verification, 240 pp, Chichester, England, John Wiley & Sons, 2003.Google Scholar
 Jordan, T. H., Earthquake predictability, brick by brick, Seismol. Res. Lett., 77, 3–6, 2006.View ArticleGoogle Scholar
 Kagan, Y. Y. and D. D. Jackson, New seismic gap hypothesis: five years after, J. Geophys. Res., 100, 3943–3959, 1995.View ArticleGoogle Scholar
 Kelleher, J. A., Rupture zones of large South American earthquakes and some predictions, J. Geophys. Res., 77, 2087–2103, 1972.View ArticleGoogle Scholar
 Kelleher, J., L. Sykes, and J. Oliver, Possible criteria for predicting earthquake locations and their application to major plate boundaries of the Pacific and the Caribbean, J. Geophys. Res., 78, 2547–2585, 1973.View ArticleGoogle Scholar
 Matsuzawa, T., T. Igarashi, and A. Hasegawa, Characteristic smallearthquake sequence off Sanriku, northeastern Honshu, Japan, Geophys. Res. Lett., 29, 11, doi:10.1029/2001GL014632, 2002.View ArticleGoogle Scholar
 Matthews, M. V., W. L. Ellsworth, and P. A. Reasenberg, A Brownian model for recurrent earthquakes, Bull. Seismol. Soc. Am., 92, 2233–2250, 2002.View ArticleGoogle Scholar
 McCann, W. R., S. P. Nishenko, L. R. Sykes, and J. Krause, Seismic gaps and plate tectonics: Seismic potential for major boundaries, Pure Appl. Geophys., 117, 1082–1147, 1979.View ArticleGoogle Scholar
 Mishina, M., K. Tachibana, and S. Miura, Postseismic deformation associated with off Fukushima earthquakes as inferred data of borehole strainmeters, Geophys. Bull. Hokkaido Univ., 72, 287–298, 2009 (in Japanese with English abstract).Google Scholar
 Nadeau, R. M., W. Foxall, and T. V. McEvilly, Clustering and periodic recurrence of microearthquakes on the San Andreas fault at Parkfield, California, Science, 267, 503–507, 1995.View ArticleGoogle Scholar
 Nagoya University, Source process of OffIbaraki earthquake on May 8, 2008 (Mj6.4, 7.0), Rep. Coord. Comm. Earthq. Predict, 80, 108–110, 2008 (in Japanese).Google Scholar
 Nanjo, K. Z., T. Ishibe, H. Tsuruoka, D. Schorlemmer, Y. Ishigaki, and N. Hirata, Analysis of the completeness magnitude and seismic network coverage of Japan, Bull. Seismol. Soc. Am, 100 (6), 3261–3268, doi:10.1785/0120100077, 2010.View ArticleGoogle Scholar
 Nishenko, S. P., CircumPacific seismic potential: 1989–1999, Pure Appl. Geophys., 135, 169–259, 1991.View ArticleGoogle Scholar
 Okada, M., H. Takayama, F. Hirose, and N. Uchida, A prior distribution of the parameters in the renewal model with lognormal distribution used for estimating the probability of recurrent earthquakes, Zisin 2, 60, 85–100, 2007 (in Japanese with English abstract).View ArticleGoogle Scholar
 Rong, Y., D. D. Jackson, and Y. Y. Kagan, Seismic gaps and earthquakes, J. Geophys. Res., 108, 2471, doi:10.1029/2002JB002334, 2003.View ArticleGoogle Scholar
 Schorlemmer, D., M. C. Gerstenberger, S. Wiemer, D. D. Jackson, and D. A. Rhodes, Earthquake likelihood model testing, Seismol. Res. Lett., 78, 17–29, 2007.View ArticleGoogle Scholar
 Sykes, L. R., Aftershock zone of great earthquakes, seismicity gaps, and earthquake prediction for Alaska and the Aleutians, J. Geophys. Res., 76, 8021–8041, 1971.View ArticleGoogle Scholar
 Toth, Z., O. Talagrand, G. Candille, and Y. Zhu, Probability and ensemble forecasts, in Forecast Verification, a Practitiner’s Guide in Atmospheric Science, edited by Jolliffe and Stephenson, 137–163, John Wiley & Sons, 2003.Google Scholar
 Uchida, N., T. Matsuzawa, A. Hasegawa, and T. Igarashi, Interplate quasistatic slip off Sanriku, NE Japan, estimated from repeating earthquakes, Geophys. Res. Lett., 30, 1801, doi:10.1029/2003GL017452, 2003.View ArticleGoogle Scholar
 Uchida, N., T. Matsuzawa, A. Hasegawa, and T. Igarashi, Recurrence intervals of characteristic M4.8+/−0.1 earthquakes off Kamaishi, NE Japan—Comparison with creep rate estimated from small repeating earthquake data, Earth Planet. Sci. Lett., 233, 155–165, 2005.View ArticleGoogle Scholar
 Uchida, N., M. Mishina, and T. Matsuzawa, Afterslip of the 2008 off Ibaraki (M7.0) and off Fukushima (M6.9) earthquakes estimated from small repeating earthquakes, ASC meeting, Tsukuba, November, 2008, Program & Abstracts, X038, 210, 2008.Google Scholar
 Uchida, N., J. Nakajima, A. Hasegawa, and T. Matsuzawa, What controls interplate coupling?: Evidence for abrupt change in coupling across a border between two overlying plates in the NE Japan subduction zone, Earth Planet. Sci. Lett., 283, 111–121, doi:10.1016/j.epsl.2009.04.003, 2009.View ArticleGoogle Scholar
 Wessel, P. and W. H. F. Smith, New version of the generic mapping tools released, Eos Trans. AGU, 76 (33), 329, 1995.View ArticleGoogle Scholar
 Wilks, S. S., Mathematical Statistics, 644 pp, John Wiley & Sons, 1962.Google Scholar
 Working Group on California Earthquake Probabilities, Probabilities of large earthquakes occurring in California on the San Andreas fault, U.S. Geol. Surv., OpenFile Rept., 88–398, 1988.Google Scholar
 Working Group on California Earthquake Probabilities, Probabilities of large earthquakes in the San Francisco bay region, California, U.S. Geol. Surv, Circular 1053, 51 pp, 1990.Google Scholar
 Working Group on California Earthquake Probabilities, Seismic hazards in southern California: probable earthquakes, 1994 to 2024, Bull. Seismol. Soc. Am., 85, 379–439, 1995.Google Scholar
 Working Group on California Earthquake Probabilities, Earthquake probabilities in the San Francisco bay region: 2002–2031, U.S. Geol. Surv., OpenFile Rept., 03214, http://pubs.usgs.gov/of/2003/of03214/, 2003.