[S-TT43] Seismic Big Data Analysis Based on the State-of-the-Art of Bayesian Statistics

[STT43-01] Slip-size-dependent Brownian passage time model with slip-size uncertainties

★Invited Papers

*Yoshikazu Terada¹ (1.The University of Osaka)

This study focuses on the evaluation of long-term earthquake probabilities. For recurrence interval data, we estimate the probability of the next earthquake using non-negative distributions, such as the Brownian passage time (BPT) distribution. When slip size data are available, the time-predictable model is often used to estimate the timing of the next earthquake. However, this model has faced significant criticism. For example, Rubinstein et al. (2012) demonstrated its limitations for short-term predictions. Statistically, the time-predictable model has a fundamental drawback: it fails to account for random noise in recurrence interval data.
To address this issue, Ogata (2002) introduced a stochastic extension of the time-predictable model, called the slip-size-dependent Brownian passage time (SSD-BPT) model. Bayesian inference for the SSD-BPT model has been developed to incorporate uncertainties in hazard estimates.
However, obtaining accurate slip size data from historical earthquakes remains a significant challenge. To overcome this limitation, we propose a Bayesian framework for the SSD-BPT model incorporating slip-size uncertainties. Additionally, we extend the SSD-BPT model to incorporate older recurrence interval data that lack slip size records, enhancing its applicability for seismic hazard assessment.