Forecasting tectonic tremor activity using a renewal process model

Abstract

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.