On the Earthquake Modeling by Using Bayesian Mixture Poisson Process - Aisah, Nur Iriawan, Achmad Choiruddin (International Journal of Advanced Science and Technology, 2020)
An earthquake is a natural disaster causing adverse impacts and damages. Indonesia is one of the most earthquake occurrence countries due to its location on the active Pacific Rings of Fire, especially in the Sulawesi-Maluku region. In many types of previous research, earthquake is commonly analyzed based on its marks such as magnitude, depth, or time. In this paper, we investigate the relationship between earthquakes and their triggers as covariates namely fault, subduction, and volcano. The covariates are defined as the distance of the earthquake’s epicenter to the nearest fault, subduction, and volcano. The mixture Poisson point process model (MPPM) is proposed to model earthquake datasets improving the common single unimodal Poisson process model for clustered or nonhomogeneous earthquake patterns involving the covariates. Parameter estimation is done using the Bayesian Markov Chain Monte Carlo (MCMC) approach which employs the Gibbs sampling method. The results showed that the Poisson mixture process which carries significant earthquake intensity characteristics is better than the Poisson unimodal because it has a smaller Watanabe Akaike Information Criterion (WAIC) value.