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. Indonesiais one of the most earthquake occurrence countries due to its location on the activePacific Rings of Fire, especially in the Sulawesi-Maluku region. In many types of previousresearch, earthquake is commonly analyzed based on its marks such as magnitude, depth,or time. In this paper, we investigate the relationship between earthquakes and theirtriggers as covariates namely fault, subduction, and volcano. The covariates are definedas the distance of the earthquake’s epicenter to the nearest fault, subduction, andvolcano. The mixture Poisson point process model (MPPM) is proposed to modelearthquake datasets improving the common single unimodal Poisson process model forclustered or nonhomogeneous earthquake patterns involving the covariates. Parameterestimation is done using the Bayesian Markov Chain Monte Carlo (MCMC) approachwhich employs the Gibbs sampling method. The results showed that the Poisson mixtureprocess which carries significant earthquake intensity characteristics is better than thePoisson unimodal because it has a smaller Watanabe Akaike Information Criterion(WAIC) value.