[1] This paper presents a Bayesian method of probability forecasting for a renewal of earthquakes. When only limited records of characteristic earthquakes on a fault are available, relevant prior distributions for renewal model parameters are essential to computing unbiased, stable time-dependent earthquake probabilities. We also use event slip and geological slip rate data combined with historical earthquake records to improve our forecast model. We apply the Brownian Passage Time (BPT) model and make use of the best fit prior distribution for its coefficient of variation (the shape parameter, alpha) relative to the mean recurrence time because the Earthquake Research Committee (ERC) of Japan uses the BPT model for long-term forecasting. Currently, more than 110 active faults have been evaluated by the ERC, but most include very few paleoseismic events. We objectively select the prior distribution with the Akaike Bayesian Information Criterion using all available recurrence data including the ERC datasets. These data also include mean recurrence times estimated from slip per event divided by long-term slip rate. By comparing the goodness of fit to the historical record and simulated data, we show that the proposed predictor provides more stable performance than plug-in predictors, such as maximum likelihood estimates and the predictor currently adopted by the ERC.
In many tectonically active regions of the world, a variety of slow deformation phenomena have been discovered and collectively termed slow earthquakes. Tectonic tremor is the high-frequency component of slow earthquakes and can be analyzed to monitor the overall slow deformation process, both spatially and temporally. Although tectonic tremor activity is complex, it does possess some characteristic patterns, such as spatial segmentation, a quasi-periodic recurrence, migration, and tidal modulation. These features are helpful for forecasting future activity if they are properly modeled in a quantitative manner. Here, we propose a stochastic renewal process to standardize and forecast tectonic tremor activity in the Nankai subduction zone, southwest Japan, using a 12.5-year tremor catalog that is divided into a 10-year estimation period and 2.5-year forecasting period. We group the tremor events into small rectangular 10-km regions and observe that the distribution of inter-event times is nearly bimodal, with the short and long inter-event times representing the characteristic times of nearby tremor interactions and long-term stress accumulation processes, respectively. Therefore, as the probabilistic distribution for the renewal process, we adopt a mixture distribution of log-normal and Brownian passage time distributions for the short and long inter-event times, respectively. The model parameters are successfully estimated for 72% of the entire tremor zone using a maximum likelihood method. This standard model can be used to extract anomalous tremor activity, such as that associated with long-term slow-slip events. We derive a scaling relationship between two characteristic times, the relative plate motion, episodicity of tremor activity, and tremor duration by characterizing the spatial differences in tremor activity. We confirm that the model can forecast the occurrence of the next tremor event at a given reference time for a certain prediction interval. This study can serve as a first step for implementing more complex models to improve the space–time forecasting of slow earthquakes.
We propose a stochastic model for characteristically repeating earthquake sequences to estimate the spatiotemporal change in static stress loading rate. These earthquakes recur by a cyclic mechanism where stress at a hypocenter is accumulated by tectonic forces until an earthquake occurs that releases the accumulated stress to a basal level. Renewal processes are frequently used to describe this repeating earthquake mechanism. Variations in the rate of tectonic loading due to large earthquakes and aseismic slip transients, however, introduce nonstationary effects into the repeating mechanism that result in nonstationary trends in interevent times, particularly for smaller-magnitude repeating events which have shorter interevent times. These trends are also similar among repeating earthquake sites having similar hypocenters. Therefore, we incorporate space-time structure represented by cubic B-spline functions into the renewal model and estimate their coefficient parameters by maximizing the integrated likelihood in a Bayesian framework. We apply our model to 31 repeating earthquake sequences including 824 events on the Parkfield segment of the San Andreas Fault and estimate the spatiotemporal transition of the loading rate on this segment. The result gives us details of the change in tectonic loading caused by an aseismic slip transient in 1993, the 2004 Parkfield M6 earthquake, and other nearby or remote seismic activities. The degree of periodicity of repeating event recurrence intervals also shows spatial trends that are preserved in time even after the 2004 Parkfield earthquake when time scales are normalized with respect to the estimated loading rate.
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