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

[STT43-P05] Gaussian Process Model for Spatio-temporal Background Seismicity Rates

*Yuanyuan Niu¹, Jiancang Zhuang^1,2 (1.The Institute of Statistical Mathematics, 2.The Graduate University for Advanced Studies, SOKENDAI)

The Epidemic Type Aftershock Sequence (ETAS) model, an example of a self-exciting, spatiotemporal, marked Hawkes process, is widely used in statistical seismology to describe the self-exciting mechanism of earthquake occurrences. This model expresses the seismicity rate as the sum of the background seismicity rate and aftershock rates derived from Omori-Utsu's aftershock law. The GP-ETAS model, proposed by Christian Molkenthin (2022), defines the spatial background seismicity rates in a Bayesian non-parametric way via a Gaussian Process prior. Leveraging the flexibility of Gaussian process modeling in the spatiotemporal domain, we have further developed the GP-ETAS model to incorporate spatiotemporal background seismicity. Our goal is to use this model to study the spatiotemporal distribution of seismicity in regions with slow-slip earthquakes.